Ovum Evaluates

Ovum Evaluates:

OLAP Contents

Contents and services

OLAP fundamentals

Using OLAP to make better decisions How to choose the right OLAP tool/s The anatomy of an OLAP tool Guide to the evaluations

Guide to the evaluations Briefing papers

Growth, transition and change: trends in business intelligence and implications for the OLAP market Summary of the evaluations

Summary of the evaluations 1999 Evaluations

Applix TM1 Brio Technology Brio Enterprise Business Objects BusinessObjects Cognos PowerPlay Gentia Software Gentia Millenium Applications Platform Hummingbird BI/Suite Hyperion Solutions Hyperion Essbase Information Advantage DecisionSuite Microsoft SQL Server 7.0 OLAP Services Microstrategy DSS Product Suite Oracle Oracle Express Development Suite Pilot Software Pilot Decision Support Suite SAP AG SAP Business Information Warehouse Seagate Seagate Holos Sterling Software Eureka:Suite WhiteLight Systems Whitelight Analytic Application Server Using OLAP to make better decisions

Overview ...... 2 What does an OLAP tool do? ...... 3 Ovum definition of OLAP ...... 5 The uses of OLAP ...... 6 Overview Decision making is at the heart of running a business. Whatever the departmental function or level of management, there are decisions to be made. Decisions range from operational issues requiring immediate resolution to longer term strategic issues. At the heart of decision making is access to quality information, meaning that it is correct, complete, timely and consistent. It is generally implied (rather than explicitly stated) that this information must also be in an accessible form. Online application processing (OLAP) is an important technology for organisations looking for better ways to access and analyse information. OLAP can enable organisations to improve their analysis of performance indicators, manage their customer relationships more efficiently and support critical parts of the manufacturing process. Given the current corporate climate, all decision makers need to know about, and exploit, this technology. Improving decision making with tool support is not an option, it is an imperative. What does an OLAP tool do? Online application processing (OLAP) is the interactive analysis of business information. End users can explore important business measures (such as profits, sales and costs) along many different ‘dimensions’. With an OLAP tool, the user moves seamlessly from one perspective on the business (‘annual sales for all stores’) to another (‘the most profitable stores over the last three months’) and drills between different levels of detail (sales by day, week or quarter). This interactive exploration of information is commonly referred to as multidimensional analysis. The common factor defining all OLAP tools – and there are many different implementations of the core functionality – is an analytical engine that turns corporate data into multidimensional information for online analysis. Complex decision support and tailored, easy-to-use applications with limited functionality can be built with OLAP tools. However, OLAP tools also support applications that match the needs of a much wider range of users. These applications are characterised by the flexibility offered to the user not merely in terms of navigation through a multidimensional model, but also in terms of the definition of reports and applications. OLAP applications are characterised by a lack of fixed structure. An OLAP tool provides an analytical environment for the power user or specialist knowledge worker, which enables them to use a range of functions to explore the information available. As well as core multidimensional operations such as drilling and rotation, users can quickly define new reports and may even have access to advanced features such as forecasting algorithms, data mining tools or software agents. The overlapping relationship between reporting, OLAP and data mining tools is shown in Figure 1. Reporting tools are aimed at a general audience, and the results are disseminated throughout the organisation. OLAP tools are specialised for the interactive exploration of multidimensional information and are used at all levels of the organisation. The division between the two types of tool is not totally clear-cut however, because some reporting tools have limited facilities to allow users to explore data, and OLAP tools benefit from some of the features of reporting tools.

Figure 1 Tool relationships

Reporting tools OLAP tools Data mining tools

Increasingly specialised At the other end of the spectrum, data mining tools allow users to find patterns and explore data using less structured hypotheses. Some OLAP tools offer limited data mining features, although the current trend is more towards integration with data mining tools than an expansion of the functionality of the OLAP tool. Ovum definition of OLAP

The Ovum definition Ovum defines online analytical processing as: the interactive, multidimensional analysis of business information on an enterprise scale. Interactive, multidimensional analysis Multidimensionality frees the users to examine their key business measures (sales revenues, costs, profits and so on) from different perspectives (for example, by time, by products or by region). It is the interactive exploration of information along these different ‘dimensions’ that distinguishes OLAP from simple query and reporting tools. Business information OLAP allows users to focus on business concepts (such as sales, profits, customers and products). The users work intuitively with the data they wish to analyse, framing questions that answer their business needs. The user is unaware and unconcerned about technical issues such as the physical data formats and client-server architectures. OLAP does not force users to con- form to the requirements of the technology. Enterprise scale Users are able to work with corporate data sources and share information across the enterprise. OLAP is a strategic technology: it must be robust and scalable enough to meet the information analysis needs of an organisation as a whole. The uses of OLAP

Information is a resource Thirty years of automating manual and administrative processes has generated unimaginable amounts of data. The amount of data collected has further escalated with the widespread use of bar codes and EPOS systems and dramatic reduction in the price/performance ratio for collecting, storing and analysing that data. Now more than ever, exploitation of this resource is seen as a crucial element in the armoury of any competitive strategy. There are three major driving forces behind the desire to make better use of information within all organisations. The complexity of the market The increasing volume of data is only one aspect of an increasingly complex commercial environment. Deregulation of markets, new competitors, new forms of relationship (with both customers and suppliers), and external technological, social and economic changes all help to further complicate the forecasting and planning process. Customer focus Competitive advantage is no longer seen in terms of price and quality alone. Companies must be able to innovate to survive. They must understand their customers needs and be able to meet them in an increasingly personalised fashion. The move from mass marketing to individual marketing requires great resources to be devoted to information collection and analysis. Organisational change The 1990s have seen major organisational changes on a worldwide scale. Business process re-engineering has led to a drastic thinning of middle management ranks and a new emphasis on flatter, more flexible organisation structures. The re-engineered organisation requires information to be available to those who need it to make the most effective decisions at the most effective time. How OLAP can help OLAP has an important role to play in helping organisations deal with several challenges: • it helps establish, monitor and communicate the performance measures that allow managers to understand how the business is performing in a complex competitive environment • it enables a better understanding of markets and customers • it can make manufacturing more efficient • it allows quality information to be shared and disseminated within the organisation. Measuring performance A core function of OLAP is the monitoring of important performance measures. OLAP provides a flexible environment for the definition, analysis and sharing of information on performance, including: • the interactive analysis and exploration of performance measurement data • exception reporting, which provides an automatic check of important performance indicators • flexibility in the definition and redefinition of applications to monitor performance measures – making it easier to redefine those measures in line with changing business needs • an environment for the definition and communication of the values to be measured, helping to build agreement and support for the underlying goals. OLAP has a well-established role in budgeting and planning, cost monitoring and other standard management functions, but it also has much to offer in support of other performance measures. New measures for performance Traditionally, finance has provided the only measures of corporate performance, and fiscal indicators remain critical to any business. Nonetheless, there is an increasing movement to bring in other factors, such as quality measures (fed by production systems), capital-investment projects, business and operational efficiency, and human-resource measures. Summary financial measures provide a single, ‘rear-view mirror’ perspective, and many organisations are beginning to realise that they need a wider range of measures in order to understand their performance in a highly competitive and fast moving market. One means of developing a common set of values for measuring company performance is the balanced scorecard. The balanced scorecard method adds three additional perspectives – customers, internal business processes and learning & growth – to traditional financial measures. The devisors of this approach have suggested that it can be further extended to monitor strategic management systems: linking strategic goals with the results of short-term actions such as quality improvement or new training initiatives. They highlight the role of information systems in helping managers disaggregate summary measures in order to investigate the underlying causes for any unexpected signals. OLAP has a role to play that extends beyond the monitoring of important performance measures. The definition of such measures should be a process of articulating shared values and goals. In many cases, this process is as important as the results from the measurement exercise itself. OLAP tools can provide a collaborative environment in which goals can be defined and disseminated. OLAP and other financial tools As well as the analysis of financial performance, OLAP is commonly used in finance departments for budgeting, cost allocation and financial modelling. There is a clear overlap in the area of financial reporting, planning and analysis between OLAP and the use of spreadsheets and corporate account- ing packages. Most OLAP tools provide links to Excel and other spreadsheets, which allows the robust data access, multi-user support and server-based architecture of OLAP to be used in conjunction with familiar front-end tools. Many users also want direct links between their transaction-based financial applications and the powerful query-and-analysis facilities of OLAP. This market opportunity is being filled by OLAP vendors producing specialised financial applications and by vendors specialising in financial packages incorporating OLAP functionality into their products.

Customer relationship management The need for a customer-centric approach to business is now a truism of commercial life. Mass marketing is being replaced by targeted marketing that focuses on the needs of specific customer segments. In its most radical guise, ‘segment-of-one’ marketing focuses on the personalisation of the products and services offered to customers. In order to win customers, and even more importantly, to retain them, an organisation must understand who its customers are and what their needs are, and be able to monitor their satisfaction with the services and products with which they are provided. It is equally important to know who your most important customers are. Typically, 20–25% of customers will generate 80% of profit. These are the customers that offer the best prospect for cross- selling new products and services. Organisations involved in large-scale retailing – large telecommunication companies, airlines, utilities, financial services organisations, supermarket chains and fast-moving consumer goods suppliers – are leading the way in the implementation of OLAP and data warehousing solutions. It is not simply a question of collecting data about customers, but of maximising the use that can be made of that information for commercial advantage. Typical OLAP applications in the customer relationship management area include: • sales forecasting • promotions analysis • customer analysis • market and customer segmentation • market share analysis. Informing the manufacturing process Many parts of the manufacturing process can benefit from the better understanding of information that OLAP provides. The industry sectors that most rapidly adopted OLAP were finance, retailing and pharmaceuticals. Manu- facturing is increasingly starting to use the technology; for instance: • purchasing staff can more easily negotiate discounts and pricing structures if they have better access to information about supplier delivery performance, material costs and expenditure • inventory levels can be more accurately controlled if staff are better informed about factors influencing stock volumes and movement • quality control staff can test hypotheses and explore correlations to understand causal factors • supply chain management involves the interaction of complex data about inventory levels, finished stock, logistics, distribution and payment. All of these factors interact, and managers need to be able to explore how they relate to each other.

Support for collaboration Along with analytical power, OLAP provides an environment in which users can collaborate in the analysis process and in which results can be disseminated throughout the organisation. It does this by providing: • a consistent view on corporate data and a shared framework for information analysis • a means of sharing information directly between users • techniques that allow the automatic dissemination of information among a large number of users • support for analysis over intranets and the Web, thus increasing the number of users with access to this information. There has been a radical downsizing of the management layers in many organisations. This trend has increased the need for better information systems to support more flexible working practices and more fluid job specifications. If the aim is to make everyone a decision maker, then everyone must have access to the information needed to make the relevant decisions. Many organisations now find themselves bereft of the embedded knowledge of experienced practitioners. The greater scarcity of managerial experience and the fact that businesses are invented and re-invented at a much greater rate – the software industry being a perfect example – means that the lack of personally acquired knowledge has to be compensated for by better information systems. However, the drive for better information should not be a purely negative requirement in response to cost-cutting exercises. The better use of information requires a common framework for understanding what information is important and the opportunities and risks that it discloses. Information rarely comes in self-contained ‘nuggets’, and even when it does its value is enhanced within a context of shared goals and values. The sharing of information obviously has many ramifications in terms of the culture and politics of an organisation. The move to a more horizontal structure does not come without challenges to all involved. However, although organisational issues must be given their due weight, technology has an important role to play in providing more flexible mechanisms for making information available to all who need it. How to choose the right OLAP tool/s

Overview ...... 2 The Ovum evaluation framework for OLAP ...... 3 Important considerations when constructing a profile of OLAP requirements ...... 4 Questions that need to be answered ...... 6 Overview Unfortunately there is no universally ‘best’ tool that magically adjusts to the needs of your users, connects to the available data sources, requires zero maintenance, scales without limit and came free with Cornflakes. While we wait for this to happen, we need to acknowledge that tools and organisational needs are very diverse. Effective use of OLAP results from getting the best fit between the two. Choosing an OLAP solution is a multi-faceted decision and the starting point is to consider your requirements. In this section, we outline the important issues that influence this. One consequence of this diversity is that you may require several tools to meet the interactive decision support requirements in your organisation. You may also require several tools if you adopt a ‘best of breed’ approach to creating an OLAP system. We describe seven questions that need to be addressed to establish your requirements profile. To enable you to use your profile of needs with our evaluations, we summarise the evaluation framework and show in a summary chart how your needs can be matched to the right tool using our evaluation framework. The Ovum evaluation framework for OLAP The aim of the Ovum evaluation framework is to provide a comprehensive means of describing OLAP tools. The framework covers the totality of OLAP functionality, so none of the tools we have evaluated offer all the features in all the categories. Indeed, even if a tool did, it would not necessarily make the ideal tool for every user. Many users would be paying for unused functionality. In our evaluations, we describe the components of each toolset, the architectural configurations supported and describe the support provided for all aspects of OLAP use. A full description is given in the Guide to the Evalua- tions; here, the eight perspectives of OLAP functionality are briefly summarised. End-user functionality How easy is it for casual users to find and use a previously created model? We also consider support for report distribution and subscription. Building the business model Does the tool enable the model builder to build a complex multidimensional business model? Advanced analytical power What in-built support does the tool provide for complex analytics? Web support Can the tool be used to access and create models via the Web? Management How easy is it to manage the models, persistent data and users? Adaptability How does the tool ensure that the data sources, models, reports derived from these and metadata are all synchronised? Performance tunability What are the tuning options? Customisation What support is available to customise and develop applications? In the following pages, we describe the major issues that need to be considered when choosing an OLAP tool, and how these relate to these perspectives. Important considerations when constructing a profile of OLAP requirements

Overview Building a profile of OLAP requirements is neither simple nor a ‘one-off’ exercise. Requirements change and your tool needs to be flexible enough to accommodate these. In this section, we describe the major considerations that need to be addressed when considering which tool/s to use, and we show how to use our evaluations to pick the best fit between your requirements and the tools available. Bear in mind that different tools can be used to support different clusters of needs, and that requirements change over time. Figure 1 shows the main questions that need to be addressed to decide your organisational requirements for OLAP. Each of these points is explored in more detail in the following pages. In the summary below, we show how the answers to these questions relate to the information given in the evaluations.

How to use the evaluations When you have decided which of the considerations described are important in your organisational setting, you can use this information when reading our evaluations. Figure 2 gives an indication of how this can be done. The figure shows which aspects of our evaluation framework are important in gauging tool support for these seven business requirements. These are the aspects you need to pay particular attention to when reading the evaluations. So, for instance, if the considerations that are most important to you require a high score in End-user functionality, Building the business model, Adaptability and Customisation, then this profile can be used with our scores for the tools. Matching your profile of needs with our scores is the first step to choosing a tool that will meet your needs. The details in the evaluations will enable you to refine your decisions.

Figure 1 Choosing an OLAP tool

What is the size Integration What type and nature of the with other tools? of analysis? data to be analysed?

How will OLAP be The OLAP Which roles delivered to the user? tool decision need most support?

Need for How many customisation? business models? Figure 2 Using the evaluations Adaptability Performance tunability End-user functionality model business the Building power analytical Advanced support Web Management Customisation

Complex and specialised analysis

Support for power users

Support for casual users

Support for the designer

Support for the administrator

Support for the application developer

Managing a large number of volatile models

Support for customisation

Web access to explore models/reports

Web access to create models

Use of the Internet/Web to disseminate OLAP reports

An architecture appropriate to the nature of See descriptions of architectural configurations in the the data sources and models evaluations

Integration with other tools Information is given in Adaptability and Deployment

It is desirable to have a good score if this is required It is essential to have a good score if this is required Questions that need to be answered Here, we examine in detail the issues, briefly introduced above, that need to be considered when working out your organisational requirements for OLAP.

What type of analysis is required? There are three main types of analysis: • general • ad hoc • specialised. General analysis This is the least complex type of analysis. This type of analysis was once delivered via hard copy scheduled business reports. Each report was a single perspective. With OLAP, the data can now be interactively explored. In general, this type of analysis uses what is provided and does not create extra information. The models are pre-defined and the analysis is conducted using the basic OLAP functionality of drill-down, pivoting and slice-and-dice. Ad hoc analysis This type of analysis demands more functionality than the general analysis described above. It extends the previous requirements by requiring that the user can enhance the model provided with the addition of extra dimensions or new information derived from what is currently available. Specialised business analysis The major feature of specialised business analysis is that it models a complex business problem, often using specialist financial, statistical, mathematical or forecasting functions. Such specialised complexity cannot be bought off-the-shelf, but is built to individual requirements. The OLAP tools we have evaluated offer broadly similar functionality to support general and ad hoc analysis; the principal differentiator in this field is the support offered for specialised business analysis. Figure 2 indicates which parts of the evaluation framework show the level of support that tools provide for this requirement.

What is the size and nature of the data to be analysed? A relational OLAP (ROLAP) architecture is necessary when multidimensional OLAP (MOLAP) cannot scale up to the data requirements. In ROLAP, the data is stored in a relational database and retrieved as required; in MOLAP the data for the model is stored in a multidimensional database (MDDB). These concepts and terms are further described in The anatomy of an OLAP tool. However, there are many criteria that can be used to determine when a ROLAP architecture is the best fit. Apart from the fact that the MOLAP vendors will put forward one set of criteria and the ROLAP vendors another, the issue is made more complex because of the interactive nature of the determinants. For instance, there is general agreement that some of the factors necessitating a ROLAP architecture are: • the size of the source data for the analysis (hundreds of gigabytes of raw data suggests a ROLAP system) • when there is a large number of unique members (generally more than one million in a single dimension) • when there is a large number of dimensions (simply adding an extra dimension with no increase in the amount of input data can double the size of a consolidated database). However, in some circumstances it is more difficult to choose (for example, if there is a small database with a large number of dimensions and 500,000 unique members, or a terabyte-sized database with only a few thousand members). Unfortunately there is no absolute formula. This means that – apart from circumstances where all these factors indicate that a ROLAP solution is most adequate – other issues need to be taken into account when choosing an OLAP tool. Within the evaluation framework, we describe the architectural configurations that the tools support.

Which OLAP roles need most support? When accessing the suitability of tools, prospective purchasers need to consider the intended use and users of the system. Tools offer different support for different roles. In this section, we first describe the four key roles involved in using OLAP and then indicate which aspects of the evaluation framework are most relevant to each type of user. The four main OLAP roles are: • end user • designer • administrator • application developer. Of course, one person may have more than one role. The end user End users are not a homogeneous group. The unifying feature of this role is that all the users have a business rather than an IT perspective. OLAP is a means to an end, and the end is making better business decisions as a result of interactively exploring quality information. From an end-user perspective, an OLAP tool is as good as the support it provides for business functions. It delivers important functionality, but how it does it is of less concern. End users are divided into two main groups, which have different levels of familiarity with the tool and the extent to which they expect to push the tool. Casual user The casual user requires ease-of-use as a primary feature. The use of the tool should be largely intuitive, so it can be used intermittently without reference to manuals or mentors. The casual user is greatly supported if the tool can provide a simplified EIS-style interface or a selection of reports provide a multidimensional business model with some text to give it a context. It is tempting, but wrong, to assume that the casual user only wants to carry out what we defined earlier as a ‘general’ type of analysis on the data. What is important to the casual user is the presentation; it is perfectly possible that ‘under the bonnet’ there are highly specialised algorithms manipulating the data. If this is an important requirement, it is essential that the tool supports customisation so that the complexity can be hidden. Power user The power user asks questions that make extensive demands on the analytical capabilities of the tool. They want to add complex new measures on an ad hoc basis and to extrapolate from given information using a variety of assumptions. Power users want to apply financial, statistical or specialised knowledge to the historical data. They require good support for sophisticated analytical capabilities. The designer The designer is responsible for creating the mapping layer (if one exists) between the data sources and business view of the data, the multidimensional business models and the reports derived from these. In some cases, the creation of reports is also carried out by end users. The designer role requires technological skills allied with an understanding of business needs. This is the role most likely to be undertaken by the IT department. Although the most visible output of this role is the creation of models and reports, the most demanding part is likely to be the organisation of connections to the data sources. This involves negotiating access, determining schedules and ensuring that the structure of the available data supports the requirements of the models. The designer needs good model building support from an OLAP tool, combined with the option of providing advanced analytics for those models that require them. The administrator The administrator, sometimes known as the manager, has the responsibility for maintaining the system. One of the regular tasks is the scheduling and maintenance of stored data. This is of particular importance if the data is stored in a multidimensional database, but also relevant if data is cached by ROLAP tools. In all cases, when data is uploaded an administrator must ensure that the operation is completed satisfactorily, and deal with it if this is not the case. The administrator also needs to set up and manage user and model security. If the OLAP system was static, this would be the main focus of the administrator’s role, but there is inevitably a high degree of volatility in the system and the administrator must deal with this. Part of this is the need to tune the system for performance gain. OLAP tools offer a range of support for this, some to the extent of enabling the administrator to choose between MOLAP and ROLAP type access. In general terms, the administrator seeks to tune performance by maximising the speed of response of the system while minimising the load time for any stored data. The OLAP system requires that the data sources, the models, the metadata, and reports based on them, are all kept synchronised. There will be pressures from the business users to change the models and reports, and the data sources are unlikely to stay static. The administrator of an OLAP system has to have good organisational skills as well as tool support. A final responsibility, although one which is outside the scope of most systems, is that of cleansing and organising the data for the multidimensional business models. In many systems this is entirely delegated to the data warehouse, but in some cases further data manipulation is carried out within the OLAP tool. The application developer There are different contexts for application development, such as in-house customisation, in-house application development and software development by ISVs. The needs are similar, with ISVs requiring additional support such as a clearer separation between development and run-time versions and a need to completely hide the origins of the engine. The main division is between: • application developers that want to simplify the OLAP tool so it presents an EIS dashboard-type interface whose main characterisation is ease-of- use • those wanting to produce more complex applications containing multidimensional models tailored to a particular purpose. There is an increasing trend towards the development by ISVs of vertical and horizontal applications containing embedded OLAP functionality.

Multidimensional business model maintenance – are they volatile? In some large companies that make extensive use of OLAP, there may be more than 100 different business models to build and maintain. Each of these will be in response to a particular set of departmental or line of business needs. A further issue is the volatility of the models. Are the user requirements constantly changing, requiring administrative effort to ensure synchronisation of the data sources, metadata and models? If there are a large number of models and, particularly, if these are constantly being adapted, the tool must provide strong management support. Figure 2 indicates which parts of the evaluation framework show the level of support that tools provide for this requirement.

Is there a requirement for customisation? Customisation enables the tool to be tailored to a particular group of users or business function. It might be important to: • present the user with the standard company ‘look-and-feel’ • enable OLAP functionality to be embedded within an application focused on a particular business function • include highly sophisticated analysis methods without the user being aware of the complexities involved. For instance, a credit-scoring application might involve a neural network application to predict a credit score without the user knowing this • enable ISVs to develop new products. Alternatively, if the need is for a tool that requires no development effort and can be deployed quickly, then a shrink-wrapped, out-of-the-box tool is more suitable. Out-of-the-box tools have the further advantage of producing quickly tangible results, which might be necessary to convince prospective users of the benefits or to get management buy-in for further or extended projects. Figure 2 indicates which parts of the evaluation framework show the level of support that tools provide for this requirement.

How will OLAP be delivered to the user? The desktop was once the primary delivery mechanism. Now, all the tools we have evaluated also offer web access. The attractions of a web interface are that: • it frees the user from having to use a machine with the OLAP tool installed • it provides platform portability • it is easier to manage if there are a large number of users and/or they are very geographically dispersed • it is cheaper to manage (the term ‘zero maintenance’ implies an unlikely degree of stability) • for some users it is the preferred and familiar interface. The primary use of the Web is to access models and reports and allow users to interactively explore these. This is generally well supported by the tools we have evaluated, although in most cases the functionality offered by the web interface is slightly less than that offered by the desktop one. Less well supported is the ability to build models via the Web. A different use of the Web and the Internet is as a distribution medium. If collaboration and sharing are important, then this is an important consideration. Figure 2 indicates which parts of the evaluation framework show the level of support that tools provide for this requirement.

Is there a need for integration with other OLAP and data warehousing tools? If you wish to wish to adopt a best-of-breed approach to your OLAP solution, you need to ensure that all the components work together. Ideally, they should do more than this, and have a degree of integration that enables metadata generated by one part of the process to be used by products from other vendors. Within the OLAP part of the data warehousing process, the main split is between server and client components. This has long been possible, but its use has been limited by the lack of a standard interface between the server and the end-user tool. All the multidimensional databases had proprietary interfaces, and thus end-user tool vendors had to make choices about which of these to support. The first attempt to produce a standard API was the OLAP Council’s MD-API, but this has not been widely adopted. More recently, in February 1998, Microsoft released the OLE DB for OLAP specification. While this is still proprietary, it is rapidly becoming the de facto standard because all the major OLAP vendors, apart from Oracle, have expressed support for it. The emergence of this de facto standard makes it much easier for users to plan for an integrated best-of-breed solution. In the Deployment section of the evaluation framework, we describe the interface standards that the tool supports. Another aspect to integrating components is the support given for metadata exchange between components from different vendors. While both Microsoft and Oracle have plans to provide a metadata repository that can be used as the basis for sharing metadata, neither of them has yet been able to deliver this. It is therefore left to individual vendors to develop technical integration between their products to facilitate metadata exchange. In the evaluation framework, in Adaptability, we describe any support the tool offers to access metadata generated in earlier stages of the data warehousing process by third-party tools.

The anatomy of an OLAP tool

Overview ...... 2 Why is multidimensionality so important? ...... 4 MOLAP, ROLAP and HOLAP ...... 9 Multidimensional OLAP ...... 9 OLAP architectures ...... 14 Overview In this section we describe the functionality that an OLAP system provides, and the terms and concepts that are important in understanding this. We explain industry terms such as ‘multidimensionality’, ‘dimensions’ and ‘business measures’. There has been much controversy about the relative merits of MOLAP, ROLAP and HOLAP methods of storing and accessing the data required for analysis. These terms are explained and a comparison made between the two main alternatives of MOLAP and ROLAP. HOLAP is a combination of both; it promises (but has not yet delivered) the best of both worlds. There are four main OLAP architectures, differentiated by the data storage method used and whether the processing takes place on the client, the mid- tier server or in the relational database. In this section, the architectures and their advantages and limitations are described. In the evaluations we describe if (and how) each of these configurations is supported.

The functionality of OLAP systems The functionality of an OLAP system is provided by three main components: • a multidimensional, business-level model for interactive analysis • an OLAP engine that processes multidimensional queries against the target data • a storage mechanism for holding the data to be analysed (this might be external to the tool; for example, an RDBMS) Figure 1 shows this in a simplified form. In practice, OLAP tools vary considerably in how they implement these functions. All three functions, for example, may be carried out on a single server, or the model and engine may be PC-based with data extracted from a RDBMS on-the-fly. This core functionality of any OLAP tool is enhanced by three other functions: • end-user access • application development • the sharing and distribution of the results of analysis. Each of these functions may be integrated within the OLAP tool or be provided in conjunction with other products. For example, application development might be supported by links to third-party development tools. OLAP is also associated with two other technologies that aid analysis and dissemination of information: data mining and the Web. Figure 1 A simplified OLAP architecture

OLAP client OLAP client OLAP client

Multidimensional business model

OLAP engine

Data store (MDDB, data warehouse) Why is multidimensionality so important? A multidimensional view of data offers important advantages. Interactive analysis against a multidimensional model: • enables you to view your business measures from various perspectives • is an intuitive way of working with aggregated or summarised data • makes it easier to identify patterns and trends in that data • makes the user an explorer of the information presented to them, rather than a passive recipient. Many query tools are now available that hide the physical characteristics of data from end users, allowing them to frame their queries without needing to use SQL or to know obscure technical names for tables and columns in the database. Multidimensional analysis provides much more. It allows users to execute complex queries and analytical functions involving many facets of their business, without assistance from the IT department.

Multidimensionality explained A simple database query that lists all cars sold in February may have a use in an operational context, but it does not a give a view of how well the business is performing. Decision makers rely on summarised data to give them a picture of the business at a relevant level of detail. A manager does not base next year’s budget on a list of products sold, but rather on a summary of sales of products over the year in different categories and different markets. A more useful view is that shown in Figure 2, with sales aggregated for each model.

Figure 2 A typical management report

Month Model Region Number of sales ...... February Blackbird North 30 February Blackbird South 30 February Griffin North 11 February Griffin South 10 February Robin North 14 February Robin South 11 ...... March Blackbird North 55 March Blackbird South 35 ...... Dimensions Even in a report such as that shown in Figure 2, it is still difficult to identify any pattern or trends in the data, particularly if the report runs to many pages. The problem arises from the fact that there are several dimensions to our information, but these are hidden in this single dimension report. By simply cross-referencing this information we obtain a multidimensional table that provides much more information about our sales. For example, the question ‘how many cars of different models have we sold over the last three months?’ can be answered by a table such as that shown in Figure 3. We now have two dimensions to our sales figures, product and time, represented by the row and column headings in our table. Typically, however, users want to ask queries that cover more than two dimensions. For example, a sales manager may want to know how many cars of each model were sold in the different sales regions over the last three months. We need to add another dimension to our table in order to answer this question. We now have three dimensions to our sales figures: product, time and geography. Figure 4 represents this three-dimensional view.

Figure 3 A two-dimensional table

January February March

Robin 2025 22 Griffin 30 21 15 Bluebird 20 60 90

Figure 4 A three-dimensional table

North South

Jan Feb Mar Jan Feb Mar

Robin 1114 17 9 11 5 Griffin 18 11 8 12 10 7 Bluebird 0 30 55 20 30 35 A three-dimensional view is easily envisaged as a cube, as shown in Figure 5. It is more difficult to envisage a four- or five-dimensional model. This can be thought of a series of inter-related cubes. Six dimensions are not uncommon in a business model, although most people have problems working with more than nine. Business measures Reviewing the data in Figure 4, a sales manager may want to add sales by revenue to the query, or be able to compare planned with actual sales side- by-side. Quantities such as ‘sales revenue’ or ‘units sold’ are called business ‘measures’. Each measure is understood in terms of the dimensions to which it is related in a query. Geography, time and product are the three most common dimensions in a multidimensional business model, but the specific dimensions used vary from business to business and from department to department. Other common dimensions include customers, departments, promotions and suppliers. Indeed any factor that you need to track in relation to your business measures may be considered a dimension. Aggregation hierarchies Our data is now summarised by a number of cross-referenced dimensions. However, these dimensional categories themselves need to be grouped and summarised to provide clear information at a suitable level. Our sales manager may want to break the information down to a finer level of detail (for example, to see sales by city). Similarly, a higher level view may be required (for example, annual or quarterly sales).

Figure 5 A three-dimensional cube

Time Q3 Q2 Q1

Robin

Griffin

Product

Bluebird

Falcon

North South East West

Geography Each level of summarised detail can be imagined (and implemented) as a separate dimension, but this quickly becomes overly complex. It is more common to see each dimension as having a number of different summary levels which are relevant to the business. Together these summary levels build up a ‘hierarchy’ through which a user can navigate. Figure 6 shows a simple hierarchy for geography. Other hierarchies may be much more complex than this. Product management, for example, requires products to be classified under a number of different hierarchies as shown in Figure 7. This means that, for example, sales revenues for a product can be aggregated (or ‘rolled up’) by category or by supplier, depending on the query. Together, dimensions, measures and aggregation hierarchies form the constituent elements of any multidimensional model. Of course, the multidimensional table need not include summarised information. It might, for example, include budgeted quarterly expenses for each department. However, even here the principal benefit of multidimensionality is the ease with which these figure are ‘rolled up’ to provide information on budgeted expenses for the whole company over the year. In general, multidimensionality is most relevant to data that needs to be summarised in one form or another. A list of staff salaries is an example of data that is not suited to multidimensional view, because there is a one-to-one correspond- ence between each employee and their salary. Multidimensional analysis, however, becomes useful once salaries are aggregated as wage costs for each department.

Figure 6 A simple aggregation hierarchy

Country

North Central South

Store A Store B Store C Store G Store H Store I

Figure 7 A multiple hierarchy

Product category Supplier

Roll-up

Production Navigating a multidimensional model Dimensions, measures and aggregation hierarchies are the constituent elements of any multidimensional model. As a model becomes more complex – involving many dimensions, several measures and various hierarchies – it becomes more difficult to conceptualise and navigate through. OLAP tools must make working with complex multidimensional models an intuitive and efficient process. Three basic operations – drilling, rotation and slicing & dicing – are required to simplify the task of working with a multidimensional model. To enable intuitive, interactive analysis, each operation must be simple to define and be implemented without significant delay. Drilling Drilling is the ability to move up and down between different aggregation levels. You drill-down, for example, from annual to quarterly sales figures or drill-up from stores to regions. Drilling is usually invoked by double clicking at the relevant point in a multidimensional table or chart. Rotation Rotation (also known as pivoting) allows dimensions to be viewed from any perspective. For example, Figure 8 shows how we can rotate a three-dimensional cube to show different aspects of our data. Rotation is much easier to use in practice than it is to envisage in the mind. The user of an OLAP tool does not need to think about cubes or rotation, they simply indicate that the wish to see sales by quarter for the northern region. This is usually achieved by dragging and dropping a dimension to a new position: the OLAP tool rotates the perspective automatically. Slicing and dicing A user may only want to see sales figures for January, or for regions where sales were below $100,000. The process of selecting the required data is referred to as ‘slicing and dicing’, in reference to the necessary operations on a multidimensional cube to pick out the required information. As well as simple selections, OLAP tools should allow users to select specific items from a dimension, select items by ranking (for example, the top five selling products) and combine selection criteria to build complex queries.

Figure 8 Rotating a multidimensional cube

Sales for Q1 by region Rotate to view sales by Rotate to see sales for product quarter for the North region A by region and quarter

D Q4 Q3 C Q2 B Q1 A A A North

B B South

C C West East East D D South West North North South East West Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 MOLAP, ROLAP and HOLAP The core data for multidimensional analysis has to be stored in a structure that provides high performance querying, scalability and multi-user access. Relational databases are optimised for frequent, simple queries. They are less suited to supporting complex, multidimensional queries. Many queries cannot be handled by a single SQL query (for example, the selection of the top five products by market share). Multidimensional queries require more table joins and full table scans, both of which drastically reduce performance. Three data storage strategies can be employed that overcome the limitations of the relational model for multidimensional analysis: • the use of a specialised multidimensional databases (MDDBs), which provide optimised storage and retrieval of data for OLAP queries • the use of a data warehouse, built using relational technology but optimised for decision support rather than transactional operations • a combination of these approaches. OLAP tools that provide multidimensional storage are often referred to as MOLAP tools, while those that access data stored in relational databases are referred to as relational OLAP or ROLAP tools. Tools that combine the two approaches and work with data stored in a multidimensional structure as well as data retrieved from a RDBMS are known as Hybrid OLAP tools (HOLAP).

Multidimensional OLAP A multidimensional database (MDDB) stores data in a series of array structures, indexed to provide optimal access time to any element in the array. These structures can be envisaged as multidimensional cubes similar to that shown in Figure 5. MOLAP vendors originally fell into two groups – those using a hyper-cube architecture and those using multi-cubes. Increasingly, the distinction is being blurred as hyper-cubes can be partitioned (effectively becoming multi-cubes) and multi-cubes can be joined to form a virtual hyper-cube.

Hyper-cubes

A hyper-cube architecture provides a single ‘cube’ in which each measure is cross- referenced against all dimensions. The advocates of this approach emphasise its simplicity and the consistency of performance, whatever dimensions or parts of a dimension are selected in a query. However, it uses more disk space and requires good sparsity handling if the cube’s size is not to become unmanageable.

Multi-cubes

A multi-cube architecture allows measures to be cross-referenced against selected dimensions only. One cube may include sales revenues dimensioned by time, geography and product. Another cube may dimension costs by department and time. This approach uses less disk space and provides better performance for each cube; therefore, it tends to be more scalable. However, performance may not be consistent for queries that require access to more than one cube, and more complex processing is required to ensure consistent results across multiple cubes. Pre-consolidation One of the advantages of an MDDB is that it can pre-consolidate the answers to many queries. Whereas a relational database will normally need to search all relevant records and aggregate data in order to answer a question such as ‘how many packets of soap powder did we sell last quarter?’, an MDDB can calculate such totals quickly as it only has to add up the cells in the relevant rows and columns of a multidimensional array. Once calculated, totals can be stored in the array structure. Most MDDBs have strong array handling functions, which further speed up calculations. MDDBs can pre-consolidate all data; so, for example, totals for each level in each hierarchy are calculated when the database is loaded. This approach gives very fast response times to most queries, but it requires considerable disk space and makes the load time longer. An alternative is to consolidate only commonly used totals and to calculate others on-the-fly. An important optimisation decision for the MDDB administrator is which data should be pre-consolidated and which calculated at runtime by the OLAP engine. Retrieval of detail information Most MDDBs also provide SQL access to data in an attached RDBMS for retrieving rows of detail information (for example, ‘all orders that contribute to an aggregation’). This information can only be displayed and not used for further analysis. Sparsity MDDBs store data collected from a series of detailed transaction records. Sparsity is a by-product of cross-referencing those records to provide a multidimensional model. This is best explained through an example. A large retailer sells thousands of products in its stores across the country. It wishes to analyse those sales on a daily basis by store. Not all products, however, will be sold in all stores every day. A cube created by dimensioning sales numbers by store by product by day will, therefore, have many empty cells where no products were sold. If only 20% of the cells in a cube are populated, then the data is said to be 80% sparse. It is not uncommon in some application areas for data to be more than 90% sparse. An MDDB must have a way of dealing with sparse data to prevent storage space being swamped by null or zero values. Each vendor has its own mechanism, but in general they compress the database so that null values do not need to be stored. Although there are some performance costs when decom- pressing the data, in recompense access times for very sparse data are improved because of more efficient indexing. An MDDB must have optimisation strategies for dealing with both sparse and dense datasets and, ideally, be able to combine these in the most effective manner. Some applications tend to generate very sparse data. Product management applications, for example, often require analysis of many attributes of a product (such as size, price, colour or package size). A multidimensional cube that cross-references such dimensions against other dimensions (such as geography and time) will tend to have a high degree of sparsity. Other applications, however, will not produce sparse datasets (financial applications for budgeting and planning, for example, tend to create dense datasets). Sparsity also tends to prevail at certain levels of aggregation (when analysing product sales by region there will be fewer ‘gaps’).

Relational OLAP Although relational databases are not optimised for multidimensional analysis, they do have advantages over MDDBs in other areas. In particular, they scale to larger datasets and include support for replication, rollback and recovery. Moreover, most organisations already have in-house skills and significant experience with their strategic RDBMS. With large data warehouses (in the hundreds of gigabytes or terabyte range), the advantages of an RDBMS over an MDDB becomes clear. Some OLAP tools can provide multidimensional analysis of data stored in a relational database. These relational OLAP (ROLAP) tools, provide a business model and an OLAP engine that sits above the data warehouse. A metadata layer is used to map the warehouse structure onto a multidimensional model. The tool then generates the SQL necessary to retrieve data to satisfy the user queries. ROLAP tools work with the RDBMS in significantly different ways: • some tools use the RDBMS to do all the data processing. To do this they generate multi-pass SQL statements and create temporary tables in the DBMS where necessary to process complex queries (this is the approach adopted by MicroStrategy) • some tools provide calculation functionality outside the RDBMS. SQL is still generated to retrieve data, but calculations (including some joins or aggregations) will be carried out by the ROLAP tool (this is the approach adopted by Information Advantage). Relational OLAP tends to provide strong support for applications that access very large datasets, such as product or customer management applications for large retailers. Such applications require analysis of many items (thousands of products and possibly millions of customers) by a large number of dimensions and therefore push the capacity limits of MDDBs. Although ROLAP tools do not need to be concerned with sparsity directly, aggregation values tend to be stored in the data warehouse for better performance, and so sparsity remains an issue albeit one dealt with by the data warehouse designer rather than the OLAP vendor.

Hybrid OLAP Hybrid OLAP is most easily defined by saying that it is not pure MOLAP or pure ROLAP. The reason for defining it in this way is that there are several variants of HOLAP. In summary, the main ones are: • an MDDB that can retrieve and analyse detail information • a multidimensional store, optionally with some pre-consolidation. Increasingly, the term is associated with the first definition and the wish to combine the advantages of MOLAP and ROLAP. An MDDB that can retrieve and analyse detail information This definition of HOLAP is likely to become the generally accepted one, as it is the term used in Microsoft SQL Server 7.0 OLAP Services. In the Microsoft tool, HOLAP is defined as storing the summary data in the MDDB and the detail data in the RDBMS. The user works with one model, which transparently accesses two types of storage. The significant aspect of the data storage option is its ease of use, rather than the approach. Other vendors have supported analysis against data retrieved from the RDBMS using ‘reach through’ SQL. When the data is needed it is dynamically retrieved and processed by the MDDB engine. A multidimensional store Some client-based OLAP tools extract a selection of data from an RDBMS and then construct a multidimensional cube (sometimes called a micro-cube) on the client. The functional difference between a multidimensional store and an MDDB is that the latter provides a database manager layer that shields the database users from the technical implementation. MDDBs thus provide a data manipulation language (DML) that is used to access the data. Each of the MDDBs offers a proprietary query language. There are also architectural differences between MDDBs and multidimensional stores in terms of how the data is stored. Some vendors using this approach give the option of including pre-consolidated values within the store (for example, Cognos), while others just store the data and do consolidations as required. (for example, Business Objects). Some vendors (such as Seagate) offer a wide variety of storage options for these multidimensional stores.

Relational or multidimensional? There has been a great deal of debate about the respective merits of the two architectures. As outlined in Figure 9, each technology has its advantages and disadvantages. While a few years ago there was an implicit assumption that one approach was superior and would ‘win’, there is now an acknowledgement of the strengths of both approaches. The focus of the debate is now shifting towards defining the criteria to use in choosing one or the other, and evaluating the extent to which HOLAP does, in practice, combine the best of both worlds. The most important issue is to understand your business requirements and the implications for the type and size of the data you need to access now and in the foreseeable future. It is important to consider how this specific goal fits into the wider strategy for decision support in your organisation. Is this a departmental application or the first step towards a larger enterprise-wide information delivery system? Is the aim to provide information to a wider user base or to provide strong analytical functionality to a much smaller set? These are not contradictory aims, but it is important to know which has priority. Armed with this information it is possible to look for an OLAP tool that suits your needs. No single approach is sufficient for all situations and needs will increasingly be met by a combination of tools from a variety of vendors. Figure 9 reflects the generic advantages and disadvantage of MOLAP and ROLAP technology. Individual OLAP tools, of course, vary in the extent to which they match the relevant profile.

Figure 9 A comparison of MOLAP and ROLAP

MOLAP advantages MOLAP disadvantages

Optimised storage for multi Generally limited to the analysis dimensional analysis of summary data

Fast and consistent performance Scalability limited by the capacity for interactive queries of the MDDB

Can act as a departmental datamart Lack of support for parallel platforms, data replication, systems management software

Easy to set up and manage Time for data loading acts as limit on scalability

Strong support for advanced Multidimensional model can be analytical functions inflexible to rapidly changing business needs

Support for what-if analysis requiring Requires an additional data write access to the database management layer

Support for sales and marketing and A proprietary storage format (although budgeting applications OLE DB for OLAP is emerging as the de facto standard for data access)

ROLAP advantages ROLAP disadvantages

Close integration with the data Query performance is not as fast warehouse as MDDBs

Exploits existing DBMS skills Requires a data warehouse

Able to analyse data along many Limited range of modelling and dimensions forecasting functions

Capable of handling very large Not suitable for departmental datasets (up to terabyte range) applications

Analysis is possible to the level of Needs work-around for what-if type transactional data analyses

Particularly suited to product and Not suited for budgeting and financial customer management applications planning applications OLAP architectures The components of the OLAP system are implemented in various ways, so there are several possible architectural configurations. The main differentiators are: • where the data is stored in a multidimensional store that is part of the OLAP tool or in a relational database (or similar) that is outside the scope of the tool • where the processing takes place on the client, in the mid-tier server or on the relational database.

The OLAP engine does the processing At the heart of any OLAP tool is a software ‘engine’ that turns corporate data into consistent and widely-available business information for interactive analysis. The OLAP engine takes a user’s requests for information, executes them against a database, and returns the results. Users’ requests are defined through a GUI-based client and are framed in terms of a multidimensional model as operations on business entities such as products, sales, stores, profits and costs. An OLAP engine is itself composed of two important elements: • the metadata that maps the multidimensional model to the source data. The metadata provides the user with a business-level view of that data. It also provides the information the OLAP engine needs to formulate queries. • a calculation and query engine which retrieves and processes the requested data for viewing by the client. The engine uses the metadata information to formulate the data access commands and execute the request. As well as selecting data, the engine may need to do further processing to satisfy the request, such as additional aggregations, comparisons of different items (in order to select the top five products by sales revenue, for example) or the application of forecasting algorithms.

The main OLAP architectures There is no ideal architecture: the right choice depends on your requirements. Here we describe the main options and the strengths and limitations of each configuration. Figure 10 shows the main architectures. Figure 10 Four OLAP architectures

Full mid-tier architecture Light mid-tier architecture Desktop architecture Mobile architecture

Client interface Client interface Client interface Client interface

Metadata Metadata

OLAP engine OLAP engine

Data

Web server Web server

Metadata Metadata Metadata Metadata

OLAP engine OLAP engine

MDDB

Mid-tier serverMid-tier Client

SQL DataSQL DataSQL DataSQL Data

DW/operational DW/operational DW/operational DW/operational server

Database sources sources sources sources

Full mid-tier architecture This is the architecture used by MOLAP tools, and the main feature is the use of an MDDB. The mid-tier server holds the metadata describing the models, the OLAP engine and the data for the models. Data is regularly loaded into the MDDB from a data warehouse or operational sources. Que- ries from the user are processed by the OLAP engine using metadata about the models, and the results are then passed back to the client for presentation. A web server can be added to the mid-tier to enable end-user access via a desktop or web browser interface. Few tools offer load sharing between distributed OLAP servers. The advantage of this architecture is that the server offers a central point of control and it is thus easier to manage. As metadata about the models is held centrally, there is no issue about synchronising multiple copies. Finally, as the OLAP client merely has to deal with presentational issues, this architectural model easily supports the thin client architecture required for the Web. The limitations of this architecture are those of MOLAP, as described earlier. Light mid-tier architecture ROLAP tools use either the light mid-tier or the desktop architecture. In the light mid-tier architecture, the OLAP engine is on the mid-tier server, but the data source is not an MDDB. It is usually an RDBMS or it may be a multidimensional store (for example, Cognos’s PowerCube). In contrast to MDDBs, which all have their own data access language, ROLAP tools use SQL to access data in relational tables. The OLAP engine has to find a way of compensating for the limitations of SQL for multidimensional analysis. The tools address this in one of two ways: • creating multi-pass SQL statements and creating temporary tables in the database in order to process complex queries such as those involving inter-row comparisons • using SQL for the basic manipulation of data, but completing any further calculations in the engine itself. The first approach reduces the network traffic but makes demands on the database, while the second has the opposite effect. This architecture is server-based, so it can support web access by the addition of a web server on the mid-tier. As with the full mid-tier server architecture, few tools offer load sharing between distributed OLAP servers. One of the main limitations of this architecture is that queries take longer than if they access the optimised storage of the MDDB. Also, if the query is complex and processing is done on the source RDBMS, the creation of many temporary tables can slow it down. One widely-used solution to this is to cache retrieved data either on the mid-tier server (where it can be shared) or locally on the desktop PC. This introduces a range of management issues. The strength of this approach includes the ease of handling large and complex datasets, as described in the section on ROLAP data storage.

Desktop architecture The principal difference between this and the light mid-tier architecture is that the OLAP engine is on the desktop. The main consequence of this ‘fat client’ architecture is that it cannot support web access. Vendors that originally had only desktop architectures have had to add a mid-tier server so that the processing of queries can be moved off the desktop to support a thin client. As with the light mid-tier architecture, access time is reduced if the data for queries is stored either locally or on a mid-tier server. Most tools support this. This architectural configuration requires good management support to ensure that models on the desktop are synchronised to reflect one version of the truth. The main limitation is the overhead of managing a large number of fat clients, rather than centralising control on a mid-tier server. The appeal of this architecture is that it is usually quick to deploy, with minimum dependence on IT staff to configure the mid tier. This speed of deployment is, however, predicated on the data for the models being in a ready-to-use state. Mobile architecture The mobile architecture is similar to the desktop one, except that it must be possible for the end user to download a useful subset of the data, sever the link to the main data source, and still have the same functionality as if the link has been maintained. This is less easy to implement using tools with ROLAP architecture because, by definition, the data is stored in a relational database. The crucial questions for these tools are whether they support a caching mechanism that is independent of the mid-tier server, and whether there is any reduced functionality when the cached data is being accessed without the use of the mid-tier server. A requirement of this architecture is the need to ensure that downloaded models are synchronised with the model from which they were derived when the mobile user next logs in. The user must be able to reconcile any changes they have made to the structure of the model – such as the addition of new calculated measures – to the latest version of the data. If write-back is supported there is a further level of complexity to ensure synchronisation. The advantages of this architecture are, as its name suggests, that it can be used by a mobile workforce and thus extends the user base. The limitations are the dangers of using out-of-date data and the need for sophisticated mechanisms to ensure co-ordination with ‘the mothership’.

Guide to the evaluations

The evaluations – an overview End-user functionality ...... 3 Building the business model...... 4 Advanced analytical power ...... 5 Web support ...... 6 Management ...... 7 Adaptability ...... 8 Performance tunability...... 9 Customisation ...... 10

Format of the evaluations At a glance ...... 11 Terminology of the vendor ...... 11 Ovum’s verdict...... 11 Product overview ...... 11

Future enhancements Company background ...... 13 Customer support ...... 13 Distribution ...... 13 Product evaluation ...... 13 Deployment ...... 13

The evaluations in detail End-user functionality ...... 15 Building the business model...... 17 Advanced analytical power ...... 19 Web support ...... 22 Management ...... 23 Adaptability ...... 26 Performance tunability...... 29 Customisation ...... 31 Evaluation methodology In this section, we describe the framework used in evaluating the products and the assumptions underpinning it. It is this structure that makes the product evaluations consistent and comparable. We have built an evaluation framework, populated it with detailed criteria and then applied it rigorously to each of the products we evaluated. The individual criteria are not mere checklists of surface-level features; they are questions about the core capabilities of the product – capabilities that affect its business benefit and applicability. For each evaluation we work closely with the vendor’s technical and marketing personnel and wish to thank them for the time and effort they have taken in ensuring we understand the whole picture. However, it is important to note that this extensive research programme is funded entirely by Ovum. We do not write to please vendors, nor do we rely on them buying our reports. We write to inform prospective buyers. We give our frank opinion, backed up with ascertainable facts; we question; we challenge. Our aim is to help you decide which OLAP product is most appropriate for your business. The evaluations – an overview In this section we describe each of the eight perspectives used to evaluate the products and give the rationale for its inclusion. Not all of these will be of equal importance in every setting. We indicate the features that differentiate products, including why and when they are important. The tools under evaluation have come from different backgrounds and have been developed to meet different needs. This is not a homogeneous set of products, nor is there a standard OLAP application. The strengths and weaknesses reflect both the genesis of the product and the differing customer bases of the vendors. Therefore, we caution you not to add up all the scores of a product and deem the product with the highest total ‘best’. Pro- spective purchasers are advised to aim for a ‘best fit’ between their requirements and the strengths of the tool. (In How to choose the right OLAP tool/s in the OLAP fundamentals section, we describe how to use the evaluations to achieve this.) You should decide: • which perspectives are the ‘least’ and ‘most’ important • what functionality is critical within these perspectives. The product scores indicate the impact of choosing the product as a strategic OLAP solution within a typical IT user organisation. If you have specialised needs, or are choosing the product for a specific decision support project, then we give enough detail for you to decide whether your particular requirements are met.

End-user functionality

End users, particularly those that do not use the system on a regular basis, need to be able to easily find and use a previously created multidimensional business model.

A high score is important if the tool is to be used by occasional users and those with minimal IT skills. While power users will always get to grips with the tool, casual users require more support. The score in this dimension will be of less importance if the intended users of the tools are an elite group and there is little concern with distributing resulting information to a wider audience.

Ease of use is a particularly important issue in OLAP because: • the main users are business workers, not IT staff • compared to order entry systems, where there is no choice, the use of OLAP tools may be optional, because there are several routes to business decision making • the front-end user tool is the most visible part of a data warehouse, and the choice of tool will greatly increase the perceived usefulness of the data warehouse. There is a logical separation between creation and use of the business model; while the same user may carry out both processes, the skill sets are different. Support for occasional users requires that the skill set necessary to find and use the model is minimal. Having identified a relevant model, the user needs to be able to interactively explore it. We briefly summarise the tool’s ability to offer core OLAP features such as drill down/up/across, table pivoting, charting and changing the dimensions using point-and-click. This functionality is essential in an OLAP tool and we deduct credit if the tool does not provide it. Over and above this basic functionality there are a number of optional features that enable users to make more use of the models. For instance, tools are enhanced by support for the generation and distribution of interactive reports based on the business model. The term ‘report’ is used to mean a subset of the multidimensional business model. The report will always contain a table or chart that the user can interactively explore, and may be supplemented by text and objects such as bitmaps, sound or video. Examples of advanced report distribution features are the generation of dynamic address lists (for example, the top ten sales people), time and event- driven scheduling, and parameterisation of the content. Allied to report distribution is the need for users to be able to see the range of reports and subscribe to them on a conditional basis. Underpinning this section is the belief that models are useful to the individual and a wider audience, and that tools should provide support for end users to communicate their findings and, conversely, to access work created by others.

Building the business model

Designers of the multidimensional business model need tools that offer enough flexibility for the model to be built to fit the business needs.

A high score here is important if you want to ‘fine tune’ a complex business model using the OLAP tool. A high score indicates that the tool offers more support for tailoring dimensions and measures. A low score will not be of concern if the intended data model is simple and largely a reflection of the data structures in the data warehouse or data sources.

In contrast to End-user functionality, a more specialised skill set is assumed, so while ease of use is a consideration, there is more concern about what is possible. What are the limits of sophistication that the tool can support? The model designer may be an IT person with a business understanding or a business analyst with some technical skills. In some cases, the tool lends itself to IT personnel creating the mapping layer, and end users creating models from these. The first process in building a business model is defining the data from the data sources in familiar business terms. Sometimes this is on a per-model basis, but a better alternative is the creation of a universally available mapping layer. The advantages of the second approach are that it ensures consistency of terms. The layered approach reduces duplication of effort and is easier to manage, because many changes in the source system can be dealt with in the mapping layer and no change is required to the business models. Once the data sources have been defined in business terms, the dimensions and measures of the model are defined. A business model is based on a combination of data that is used ‘as is’ from the data sources (although possibly with some cleansing) and data that is derived. To use data sources effectively, the designer must be able to access the schema of these sources and metadata, generated either by extraction tools or database design tools. The developer should also be able to access a sample of the data to aid understanding. When building the business model there are separate requirements for defining dimensions and measures. Defining dimensions The lowest level of a dimension hierarchy will almost always be mapped onto an actual data source. This is a field or column in a relational data source, or its equivalent in non-relational sources (for example, comma delimited text files). We expect levels above to offer more options, so that they can be directly mapped onto data sources or be defined by the designer. The tool should offer support for alternative drill-down patterns in the dimension hierarchy. Defining measures The major issue with regard to measures is the support the tool gives for specifying calculated measures. Most tools will allow data to be defined using arithmetic, relational and logical operators. Minimal support is provided if the complexity of derived data is limited to what can be specified in SQL. It is preferable if the tool offers a range of functions that enable more complex calculations to be defined.

Advanced analytical power

Specialised users need a ‘ready to use’ selection of statistical, financial and forecasting functions, as well as the ability to write additional ones.

A high score is essential if your users intend to use the OLAP tool for complex analytical work, business modelling or forecasting, probably as a business analyst or a power user. A low score will be acceptable if your analysis consists of manipulating and presenting historical data, rather than applying complex formulae or algorithms to the data.

Some end users require specialised support, beyond the simple manipulation and presentation of multidimensional data. Spreadsheets (such as Microsoft Excel and Lotus 1-2-3) already provide much of this analytical functionality, and we expect the tools to provide tight integration with spreadsheet interfaces. Integration with other tools offering specialised analysis is highly beneficial to specialised users. Data mining capabilities, either inherently or through integration with other tools, give additional support to the user. In forecasting and budgetary applications, a critical requirement is the need to be able to write back data to the business model so that new values, dependent on this, can be calculated. This is equivalent to ‘what-if’ analysis using a spreadsheet, but with a more complex set of interacting factors.

Web support

To fully exploit the Web, tools should support web publishing and the exploration and creation of models via a web browser.

A high score is crucial if the intention is to empower a large number of users with OLAP at minimal cost, or if users require ‘access from any desktop’, but will be less important if the intention is to constrain the use to a small group of power users equipped with standard PCs.

There are many factors contributing to the growing importance of web access to OLAP, including: • the cost benefits of using browsers requiring minimal maintenance (over- optimistically dubbed ‘the zero administration option’) • the ease of providing OLAP functionality to a large number of users via the Web • the increasing expectation that intranets as well as the Internet will be used for information dissemination. Providing OLAP via the Web is now an intrinsic requirement. However, while all vendors offer web access, there are important differences in the web solutions. An important aspect is the comparison of end-user functionality delivered via the desktop and web interfaces. Web access should deliver the same functionality to the user as the desktop access, but this is now shifting towards recognising the different strengths of the two access routes. It is not necessary for the web interface to be a cut down version of the desktop interface. In some areas, the web interface can offer facilities not easily available to the desktop user (for instance, the incorporation of data from URLs and the use of Internet-based search engines). We give extra credit for a web interface that exploits web features and adds value over the desktop access. Being able to develop models via the Web is useful, but less important than providing interactive access to pre-built models, and thus receives only a small amount of additional credit. Management

Tools should offer support for the management of models, data and users that is easy to use and reduces the workload of the administrator.

A high score is essential if the management of the OLAP system is to be undertaken by a general administrator rather than a DBA specialist. The importance of the score here is also a function of the number of users and the complexity of models being maintained. A low score will not cause too much concern if there is little data to manage and security is not an important issue.

Management of models The main issues in managing models are security and query monitoring. Information from monitoring the use of models enables the administrator to respond to changing demands, and supports the process of tuning both the models and the data sources that feed them. Management of data The management of persistent data is more complicated. In MOLAP, by definition, the data used in the multidimensional business model is stored in a multidimensional database. In ROLAP, while the main data source is a relational database, all the tools evaluated also make use of a persistent data cache outside the RDBMS. This is required both to reduce query times and to minimise the number of calls to the source data. Regardless of the storage mechanism, all data organised for multidimensional analysis will explode in size if a large proportion of the potential consolidations are pre- calculated, and will therefore require support for size management. In the case of ROLAP tools, this is the responsibility of the data warehouse and outside the scope of the tool. With a MOLAP-type store, it is managed within the tool. The detailed management tasks associated with MOLAP, ROLAP and HOLAP are different, but the main issue in all cases is the quality of support the tool gives for ease of management. Some of the issues common to all types of persistent storage are scheduling, distributing the data and informing the user of the currency of the data. Management of users The tool should allow the administrator to define individual and group security profiles. Query governance should be provided to ensure that users are at least warned about, if not prevented from, making resource-expensive requests. Adaptability

Tools need to provide support to keep the data sources, multidimensional business models (and the reports derived from them) and the metadata about all of these synchronised.

A high score is important if the data sources or user requirements are likely to be volatile and the implementation is medium to large. A low score will be acceptable in the unlikely situation that both user requirements and data sources are considered to be comprehensive and stable.

The adaptability of the system to cope with changes at both ends of the process (that is, the data sources used and the business requirements) is important for all OLAP systems. Another aspect of adaptability is the extent to which the tool can mix-and- match data storage options; for instance, can data for a business model be stored in an MDDB and/or a relational database, and can these options be modified once the business model has been created? This flexibility gives the designer choices about how to optimise the system. An OLAP environment combines a number of potentially volatile elements. These include the requirements of the end user. Although it is essential that a requirement specification be drawn up before the models are built, new needs will emerge as users make use of the system. If the requirements change, it is a sign that the system is being used, not that it was poorly defined. Further changes will be required as different types of users are added to the system. It is therefore essential that the tool provides mechanisms to make it as straightforward as possible to adapt to changing requirements. One of the important aids to ensuring ease of adaptability as requirements change is comprehensive and well managed metadata. Most OLAP tools interpret metadata as meaning source table schemata and the business names given to columns. We regard this as a minimal requirement. The next step up, in terms of the quality of metadata, is to enable the designer to add descriptive text to some of the objects (for example, models, dimensions and measures). Ideally, the metadata should be much richer and with structures to capture author details, versions and changes, dates, data derivation and end-user annotations, and then give the user facilities to search the metadata. In general, few tools offer this quality of support. The ideal situation is that rich contextual metadata is available in a controlled repository, so that access and visibility can be managed from within the toolkit. The metadata should be searchable to allow users and designers to easily find relevant models and model components. You need to be wary of how this section is interpreted, because one benefit of limited metadata (for instance, if it merely captures the schematic details) is that it makes it much easier to keep it synchronised with the models. Thus, a high score in this perspective coupled with a low score for the business model perspective could indicate that the model is easy to synchronise because it is basic. Performance tunability

The administrator needs tool support to enhance performance by tuning the data extraction and the data manipulation processes.

A high score is essential whatever the scope or nature of the OLAP operation. A low score is only acceptable in the short term if the users have either not been getting the information at all or waiting for requests to be processed by the IT department. In the longer term, a low score is unacceptable for all tools.

Users’ performance expectations are always shaped by their previous experience. If static reports previously took a week, and the richer, more interactive OLAP model takes ten minutes to respond, the user will initially be delighted. However, this will not last and all users, ultimately, aspire to an instantly responsive system. There are some aspects of performance tunability that are applicable regardless of the architecture, but some that are particular to MOLAP and ROLAP tools. Shared requirements, regardless of architecture, are the need to: • extract data as speedily as possible from relational sources • distribute the processing of the users’ queries • speed up the processing by taking advantage of symmetrical multiprocessing (SMP). Tools with different architectures offer different challenges to performance, and different tuning options: • MOLAP how to trade off size and load time against speed of response and the support of multiple users • ROLAP query response time is the main performance issue. We give maximum credit when the tool offers the administrator a comprehensive range of methods for balancing performance against factors such as database size, number of users, load time and storage architectures. Customisation

We consider the support the tool provides to develop applications that include multidimensional data in tabular and chart format that the user can interactively explore.

A high score is essential if the organisation wants to develop in-house applications for OLAP or if the tool is to be used as the basis of decision support applications by an ISV or a VAR. There are situations in which a low score will not be of great concern; for instance, if the intended user group can handle an ‘out of the box’, non-customised environment and the functionality provided meets the users’ requirements.

The strongest support is provided by tools with a complete development environment, which includes the provision of components and a 4GL that can directly help build multidimensional applications. Although this requires developers to master a new environment, it reduces the build time. In contrast to this, there are tools that provide an open API through which data can be extracted from models and then manipulated in applications built using familiar application development environments (such as C++ or Visual Basic). However, with these tools more work is required to give the user the same facilities to dynamically explore the data. Some, but less, credit is given to tools that offer this facility. Multidimensional analysis may be the focal point of the application, or it may be an embedded part of a package whose main focus is the management of sales, financial or other types of information. In this case, the OLAP features are supplementary. In both cases, the advantages of building such OLAP applications in-house include the tighter fit with requirements, the greater complexity supported and a potential reduction in cost. We also look for support for a simpler form of customisation, which enables the user to interact with a model through a simplified front end with a reduced number of options. These are generally known as EIS-type applications. Format of the evaluations

At a glance This one-page overview summarises the product and its principal features. It includes: • the name and principal location of the product vendor • the name/s and version number/s of the product/s evaluated and their release date/s • three key facts about the product, generally the type of OLAP tool (for example, MDDB, relational OLAP or desktop), the platforms it runs on and an interesting fact about the product or company that might affect a purchasing decision • three strengths of the product • three points to watch, or aspects that may be weaknesses of the product in some circumstances • the ratings chart – a tabular summary of the product’s scores on the evaluation perspectives.

Terminology of the vendor The main vendor-specific terms that need explanation. OLAP, more than many areas of IT, is particularly prone to each vendor using different terms for similar things. To minimise the confusion, our policy is to use Ovum terminology throughout the evaluations to give consistency to the descriptions where possible, and explain how these relate to the vendors’ own terms.

Ovum’s verdict What we think A summary of Ovum’s opinion of the product (good, bad and neutral), with the reasons. When to use A description of the circumstances in which you should shortlist the product and those in which it is less suitable.

Product overview Components The main components of the product and their version numbers are listed and described. Figure 1 Four OLAP architectures

Full mid-tier architecture Light mid-tier architecture Desktop architecture Mobile architecture

Client interface Client interface Client interface Client interface

Metadata Metadata

OLAP engine OLAP engine

Data

Web server Web server

Metadata Metadata Metadata Metadata

OLAP engine OLAP engine

MDDB

Mid-tier serverMid-tier Client

SQL DataSQL DataSQL DataSQL Data

DW/operational DW/operational DW/operational DW/operational server

Database sources sources sources sources

Architectural options We describe whether the toolset can be configured in each of four architectures: • full mid-tier architecture • light mid-tier architecture • desktop architecture • mobile architecture. Figure 1 summarises the main storage and processing features of the architectures, which are described in more detail in The anatomy of an OLAP tool in the OLAP fundamentals section. Using the toolset We describe features that differentiate the product from others and include screenshots to give a feel for the tool’s interface and style of use. Future enhancements The vendor’s stated plans for enhancements to the product. Planned developments for the product can often also be used as a guide to what the vendor thinks is missing from the current product.

Company background History and commercial The company’s history, other product lines, revenue and profitability. Character and direction The main business of the company, its customers and distribution channels. The current marketing and product strategies and the way in which the company plans to change. We describe the vendor’s view of how OLAP should be implemented and the philosophy behind the company’s product set.

Customer support The support, training and consultancy available to purchasers of the product.

Distribution The name, address and telephone numbers for the company’s main contact in the US, Europe and Asia-Pacific. Includes the web address of the vendor.

Product evaluation Each of the vendor’s OLAP toolsets is evaluated along eight perspectives: • end-user functionality • building the business model • advanced analytical power • web support • management • adaptability • performance tunability • customisation. These are described in detail in the next section.

Deployment Platforms The platforms that the server and client component/s run on. Data access The data sources that the tool can access; for example, relational databases, comma delimited files, personal productivity tools (such as Excel), third- party MDDBs and data from ERP applications. Standards Whereas the relational database world has standardised on SQL, there are a various competing standards in the MDDB world. The OLAP Council offers a variety of standards including version 1 and 2 of the OLAP Council specification, Microsoft’s OLE DB for OLAP (both as a consumer and a provider) and, finally, there are proprietary but published standards for accessing MDDBs. We state which standard/s the product supports. The importance of this information is that it determines compatibility between products from different vendors. Published benchmarks We describe any published benchmarks for the product, but advise caution in attributing too much importance to these measures because: • the methods used are open to considerable interpretation and debate • the leadership pattern is unstable in an area such as OLAP, which is new to performance measurement. Price structure The pricing structure, as supplied by the vendor, is described. However, we advise prospective purchasers to contact vendors directly for details concern- ing site licences and volume discount deals. The evaluations in detail

End-user functionality Summary A brief discussion of the product score in this dimension. Finding and understanding the model Finding and loading a multidimensional model We give credit for tool support for logical groupings of models. Extra credit is given if the user can search for models using metadata. For example, a search for all the models to do with ‘customer returns’ or ‘designed by Joe Smith’. Metadata for end users End users that are unfamiliar with the model are helped by explanatory metadata about the model as a whole and the individual components of it. Some credit is given for a non-structured description, but more is given if the metadata also includes details such as the author, date of design, contact details and keywords. We give additional credit if the tool gives end users access to metadata generated upstream when the data is extracted from operational sources, as well as metadata entered by the developer. Annotation by the end user There are several potential sources of metadata. One generally ignored source is the end user, who might wish to annotate the model or its components either for their own future use or for selected others. We give credit if the end user can annotate in this way and additional credit if the visibility of such comments can be controlled. Using the model Basic OLAP functionality We confirm that the tool offers the expected facilities to drill-up and -down, change dimensions and measures using drag-and-drop, nest the measures and display the data in cross-tab or graphical format. We deduct credit if it does not. Additional credit is given if alerts can be defined using point-and- click. Changing the position of members in a dimension level When data is shown in a chart format, it shows the members of the dimension in the same order as in the table. For visual clarity, the user might want to re-order the members within a level so that the larger values are at the back of a three-dimensional chart. We give credit if the members within a dimension level can be repositioned. Visualising the drill-down hierarchies Some tools inform the user that they can drill-down on a dimension by changing the shape of the cursor. An additional optional feature is to be able to see your position within the whole hierarchy, so that the user instantly knows what is possible. Credit is given for ease of seeing the whole hierarchy and the current position within this. Drilling down to detailed data It is the nature of multidimensional tools to show summary data. However, users will sometimes want to drill-down to the detailed data. With a ROLAP tool, this requires an additional SQL query and some query governance to prevent ‘queries from hell’ impeding the performance of the source database. In a MOLAP architecture, where the detailed data is held in an RDBMS, the connection to the data source has to be established as well as the query generation. We give credit if this can be done, but reduce this if substantial preparatory work is necessary (for MOLAP architectures) or there are no controls (for ROLAP architectures). Range of front-end user tools Users should be able to visualise the business model using specialised and familiar tools. The de facto end-user OLAP tool is Microsoft Excel, and we describe and give credit for the use of this application and any additional front-end tools that can be used to view the model. Visualising the results All OLAP tools provide a wide range of charts, but not all provide maps that can be used to display geographic data. (We do not expect support for the display of GIS data.) We describe any distinctive ways in which data can be displayed and whether maps and charts can be displayed on the same page as tables. We give credit for such support depending on its usefulness. Saving and sharing results Designing a report The term ‘report’ in the context of these evaluations is used to mean an electronic document containing a table or chart that the user can interactively explore, which may be supplemented by text and objects such as bitmaps, sound and video. Note that this is different from the more typical use of the term meaning the presentation of static data without drill-down and pivot facilities. Credit is given for the ability to support nested cross-tabs and the ease of adding text and other objects (such as bitmaps, sound and video) to the model to create an informative and visually attractive report. Generally, the addition of objects is implemented through the use of Microsoft’s OLE, and full credit is given for this despite its limitation to the Windows platform. Another feature we expect is that the end user should be able to add locally- defined calculations (in addition to those created when the model is designed). Publishing a report OLAP is important in both individual and group decision making. If colleagues are to be involved, then it must be easy to embed a model in a report and make it available, either globally or within the organisation. This may be done in a variety of ways; the most usual being through the generation of HTML pages for the Web or through an FTP-based application. Regardless of the technology, the user requires ease-of-use and the option of scheduling the publication of the report. Credit is given for support for these functions that is suitable for use by end users as opposed to administrators. Extra credit is given for any other form of support for group work. Targeted distribution via e-mail As well as publishing reports for general availability, there is a need to target information at particular users. If the users have the necessary software installed, they will be able to make use of models or reports containing them. If not, they merely need a static picture of the data, either in tabular or graphical form. We give credit if the tool supports e-mail distribution of models, reports and static views from within the end-user tool. Extra credit is given if the tool supports dynamic address lists; for instance, ‘send the report to the top ten sales people’. Subscribing to reports The other side of the distribution coin is that users should be able to see what reports are available, ideally with some explanatory metadata, and then elect to subscribe to particular ones. Credit is given if this is supported and additional points are awarded if the subscriber can choose conditional receipt. For example, ‘only send the report containing the sales figures to individuals or groups of users if one or more of the targets is not met’. Other end-user features We describe and give credit for any other functionality that helps the user find and use models and reports.

Building the business model Summary A brief discussion of the product score in this dimension. Basic design Design interface We describe the design interface and give credit for an easy-to-use GUI interface. Visualising the data source We give credit if the tool enables the developer to see a sample of source data as well as the schema, because this informs decisions about mapping fields onto dimensions and measures. Universally available mapping layer The dimensions and measures in the multidimensional business model have to be mapped onto fields in data sources. This may be done directly or there may be a ‘mapping layer’ between the logical business model and the physical data. The mapping layer acts as a catalogue of the data sources, with the replacement of any cryptic column names with meaningful business names, sometimes with the addition of metadata above data transformation. The developer of the multidimensional business model then works from the data definitions in the mapping layer, rather than directly with the data sources. The advantages of this are that it is easier to build the cubes as the meaning of the data is clearer, and it can also be used to insulate the model user from changes to the source data. If the name or location of a source data field changes, the administrator only has to change the reference in the mapping layer, and all models using this field will continue to work without further modification. Credit is given for support for this facility. Prompts for metadata While metadata about structural details can be captured automatically, contextual details such as a description, author, contact details and rationale have to be entered manually. We give credit if the tool automatically prompts the developer for such details. Building the dimensions Selecting columns for the dimensions Credit is given if, as is almost always the case, columns for the dimensions can be selected using point-and-click. Selecting the members shown in a dimension level While almost all tools enable the selection of members in a dimension level, some do it with point-and-click and others with SQL. The former approach gets a higher score. Defining a dimension hierarchy We expect the levels in the dimension hierarchy to be defined using point- and-click and for the user to easily insert user-defined levels in the dimensions (for example, a regional level above the stores level). Extra credit is given if the developer can define different drill-down patterns (sometimes called unbalanced dimensions or ragged dimensions). Time dimension We describe the support given to define the time dimension and if a range of time hierarchies are provided. Credit is given if new time units can be defined. Extra credit is given if these can be defined dynamically (for example, for the last six months). Annotating the dimensions We expect the tool to enable the designer to provide both short and long names for the dimensions and to allow them to choose which is used for charts and visual displays. Credit is given for the support provided and the ease with which it can be implemented. Default level of a dimension hierarchy Credit is given if the developer can easily specify the dimensions and levels shown when the model is opened and if users can tailor this to their own requirements. Defining the measures Calculated measures As well as using measures that map directly to data sources, tools should be able to create new calculated measures. A very simple example is: ‘profit=revenue–cost’, but the expression defined may be much more complex. Credit is given if arithmetic such as this is done using a point-and-click environment, but reduced if an expression has to be typed in because this offers less support to the designer. Additional credit is given if there is an extensive range of functions available to use within the definition of the new measures. Support for multiple measures with a set of dimensions It is expected that all tools support multiple measures with a set of dimensions, and credit is deducted if tools do not support this. Multiple designers Multiple designers We describe the support the tool gives to prevent multiple users overwriting each other’s work. Credit is given for appropriate mechanisms. Support for versioning Versioning support is extremely useful whether one or several designers are working on a model. It is described and credit given for any versioning support directly provided by the tool. Other ‘building the business model’ features We describe and give credit for any other functionality that assists in building the business model.

Advanced analytical power Summary A brief discussion of the product score in this dimension. Third-party tool integration The tools should provide an add-in to spreadsheets (Excel or Lotus 1-2-3) that allows end users to directly exploit the analytical functions provided by these packages against multidimensional data. Extra credit is given to tools that integrate closely with specialised statistical and econometric modelling packages that provide their own comprehensive range of functions (for example, SPSS and SAS ETS). Defining specialised models Ranking and sorting Most tools offer the facility to rank and sort the members of a dimension on a particular measure. If there are any unusual features, these are described. We give extra credit for more sophisticated classification and sorting techniques supported by the tool, such as Pareto analysis. Mathematical methods The tool’s support for mathematical functions underpins the power of its analytical capabilities. We expect all the tools to support basic arithmetic functions and logical operators. However, more complex analysis is possible if the tool supports more complex and specialised mathematical techniques such as matrix algebra, linear and quadratic functions and polynomials. We give credit for specialised functions and their ease of application in analyses. Financial functions The performance of a business is principally assessed in terms of its financial standing. The world of finance has its own set of conventions, concepts and functions, including compound interest, depreciation, net present value, fixed interest schedules, discounted cash flow and internal rates of return. Some tools that are geared specifically for financial consolidation applications, and which have a substantial customer base in the financial world, offer a range of specialised financial functions. Any such functions provided by the tool are briefly described and evaluated for their usefulness. Statistical models While many OLAP tools provide basic statistical inference of data such as ‘max’, ‘min’, ‘mean’, ‘median’ and ‘mode’, some offer more specialised support for statistical modelling. These include standard deviation, variance, moving averages, correlation, frequency distribution, probability, confidence and significance testing. We give credit for specialised support provided by the tool or for close links to statistical modelling packages. Trend analysis Credit is given for the inclusion of functions to assist with identifying trends in historical data. Most tools offer some basic graphical support for straight- line (linear) trend analysis through an Excel plug-in, and a small amount of credit is given for this. However, we give extra credit for more sophisticated trending methods, including moving averages, exponential smoothing functions and specialised curve fitting techniques (exponential, geometric, hyperbolic and quadratic). Simple regression Users look to their OLAP tools to help them forecast future trends as well as to analyse past ones. We identify the forecasting algorithms that are supported by the tool and how easy they are to use. As a minimum, the tool should be able to support simple regression techniques for forecasting future events on the basis of historical data. Various regression algorithms that can be used, including auto regression, linear/non-linear regression and univariate/multi-variate regression. Time series forecasting A frequent use of forecasting is to use time-series data to make comparisons of values in one time period with another. We describe and give credit for including a time-series analysis in forecasts and supporting; for example, simple averages of the past, smoothing functions, seasonal adjustment, cyclical variation and random (residual) factors. Extra credit is provided if the tool supports time series forecasting methods such as Box Jenkins and Holt-Winters extrapolation functions. User-definable extensions Experienced analyst users may wish to develop their own analytical functions or modify and extend existing functions provided by the OLAP tool using procedural logic. We give credit if either is possible through a friendly graphical user interface, and if the new functions can be documented and stored in a universally accessible library for sharing and re-use. Write back for ‘what-if’ analysis Just as spreadsheets enable users to write new values back into a cell, enabling the user to explore ‘what-if’ scenarios by examining how this new value affects other cells, similarly in a more complex multidimensional model users may wish to write back values to explore the consequences. This is sometimes called sensitivity analysis. Write back is a not a feature that is likely to be appropriate for a large community of users, and is primarily of interest to the power user, particularly in the analysis of budgets. Credit is given only if permission to use the write back facility is well controlled or if the changes are made in a local and temporary version of the model. Incorporating non-numerical data Although OLAP tools are geared towards the analysis of numerical data, such data is not the only source of information. Credit is given if the tool can support the incorporation and analysis of non-numerical data (for example, using Logit and Probit analysis) and other information sources such as free format text documents, online news feeds and Internet pages. Data mining OLAP tools adopt one of three positions with regard to data mining: • they ignore it • they provide some built-in data mining functionality • they integrate with another tool. The built-in data mining functionality may be based on decision tree algorithms, inductive reasoning, pattern matching, cluster analysis or neural network technology. If the support is provided by integration with another tool, credit is given regardless of whether the tool originates from the OLAP vendor or a third party; the main issue is the need for ease of integration. No credit is given for joint marketing ventures with no technological substance behind them. We describe the nature of the support and give credit for both provision within the tool and close integration. Data mining tools and methods are evaluated in greater detail in Ovum Evaluates: Data Mining. Other analytical functionality We describe and give credit for any other complex analytical functionality provided by the tool that is particularly useful for general business performance measurement and assessment (such as Monte Carlo goal seeking functions) or geared towards horizontal applications (sales and marketing) or specific vertical sectors. We expect that users of the specialised facilities described in this section will be familiar with the functions on offer, but if a wizard facility for guiding the user through the use of a specialised feature is provided, we give extra credit because this makes it more accessible to the non-specialist.

Web support Summary A brief discussion of the product score in this dimension. End-user functionality via the Web Functionality of Web access to explore models Most OLAP tools were developed for use on the desktop, and web access was developed as an additional feature. As a result of this background, most products tend to offer inferior versions of the desktop client via the Web, rather than having a ‘made for the Web’ appearance. Credit is given in this section for web functionality that most closely mimics the desktop access and is reduced if this is less than that of desktop access. Extra credit is given for web-specific features that exploit the differences of web access. Supports both registered and unregistered Web access It should be possible for known users and guests to access business models via the web interface. Known users should have the same access rights as when they use the desktop, and new users should be able to log on as guests with a restricted set of rights. We describe and give credit for support for both types of user. In some cases, there is no technological restriction on giving open access to the model, but the licensing agreement with the vendor may prevent it. In this case, we score the tool as if unregistered access is not possible. Range of users supported by the web interface While the preceding two points are concerned with web access to the business model, here we consider the extent to which the tool supports the production of EIS-style reports for viewing with a web browser; these are characterised by simplicity and ease of use. We consider how easy it is to provide such pages in which the available options are restricted. The provision of EIS-style pages enables a wider audience to be supported by the web interface. Creating models via the Web Editing the mapping layer There are far more users than developers, so access to the business model via the web interface is generally more important than the means of creating it. However, there are situations in which the creation of the mapping layer and models via a web browser is important; for instance, if a large number of power users are supported or if the designer works by sitting next to users at their machine. We describe and give credit for support to edit the mapping layer. Building and editing models This complements the point above and covers the support provided for building and editing models via a web browser. Distributing via the Internet and the Web Generate HTML and Java Publishing on the Web requires the tool to generate HTML and/or Java code. The drawback of HTML is that it does not offer good support for tables and graphical displays and can result in a visual display that is substantially less elegant than the desktop one. However, its advantage is that it is accessible to the large number of corporate users that have not upgraded to a 32-bit operating system (usually Windows 95 or 98), which is required to support Java. Ideally, the tool should be able to offer users the option of generating HTML or Java to support web publication. Full credit is given if the tool supports both of these options. Corporately organised distribution via the Internet We consider whether there is support for distributing models and reports to assist core business processes. Whereas in the section on End-user functionality we considered how personal productivity could be enhanced by targeted distribution, here we are considering the support for centrally organised targeted distribution using either the Internet (e-mails) or web pages. For instance, when a user logs in using a browser they might be directed towards a collection of reports targeted at them, as well as being able to access a corporate repository. Alternatively, reports might be e-mailed to them. We give credit for support for distribution within a corporate context and additional points if the recipients can be defined dynamically. Include URLs in a report Credit is given if URLs can be incorporated in reports so that, for instance, users can be directed to related information Distribution of Web server processing The OLAP Web server can be a bottleneck for queries coming from Web servers. Here we describe and give credit for any means of distributing the Web server processing, such as integration with a middleware product. Other Web support features We describe and give credit for any other features that support Web usage.

Management Summary A brief discussion of the product score in this dimension. Management of models Separate management interface There are separate roles with different skill sets necessary to support OLAP. For ease of administration it is preferable that the management of the system is done via an interface designed for this purpose. Here we give credit for a graphical interface designed for the management function. Security of models OLAP information requires the same level of security as database information. While it is self-evident that data stored in MDDBs requires access control, it is also the case with ROLAP tools as all of them store data persistently to enhance performance. We do not give credit if the tool relies on the security of the databases supplying the data, but only if there is a separate and convincing security mechanism within the OLAP tool. Query monitoring Query monitoring is required both to tune the system for performance and to tailor its content. The most popular queries may need to be optimised in various ways, such as the provision of pre-calculated aggregate tables or caching the data locally. Query monitoring also assists in ensuring user satisfaction by helping the developer tailor the content of the business models according to usage. Query monitoring should provide the administrator with details about the use of reports (for example, which reports are run when and by whom) as well as processing details (for example, average, mean and mode times for processing, number of records processed and so on). Credit is given for support for these functions, but the score is reduced if there is poor integration with the rest of the toolkit. Management of data How persistent data is stored (not scored) Here we describe the storage mechanism used by the tool; this is not scored. Scheduling of loads/updates As all the tools we have evaluated have some form of persistent data store, they require scheduling support to control the update process. We give credit for an easy-to-use interface offering a wide range of options. We do not give credit if the tool relies on the scheduling facilities of the operating system or third-party tools, unless these are very well integrated with the rest of the tool’s management facility. Rather than having to individually define schedules for each business model it should be possible to name a specification and then re-use it as required. Extra credit is given if there is support for this. Event-driven scheduling Being able to define scheduling as contingent on events, such as the completion of a data load process in the data warehouse, gives extra flexibility to the tool. Credit is given for an easy to use means of doing this. Failed loads/updates If an update fails the administrator needs to know this, needsto know why it has happened and should ideally be able to specify that the failed update is automatically resubmitted a set number of times. Comprehensive error reporting is extremely important to assist in resolving the problem. Credit is given for the breadth and depth of scheduling support. Distribution of stored data The administrator should be able to specify whether the stored data is held on a local client, a central server or anywhere on the network. We give credit if the administrator has these options. Sparsity (only for persistent models) We expect tools that include consolidated aggregates to have a method for handling sparsity and thus minimising the data explosion that results from the storage of aggregates of sparse data. Here we describe the way in which the tool handles sparsity and deduct credit if the tool does not combine ease of use with effectiveness. (ROLAP tools, for which the management of this within the OLAP tool is not an issue, get full credit.) Methods for managing size This is less of an issue in ROLAP, where the decisions about aggregates and indexing are the responsibility of the data warehouse or data source administrator and outside of the scope of the OLAP tool. In MOLAP, the issue of how to deal with the explosion in size resulting from pre-computed data has to be dealt with by the OLAP tool. The final size of a multidimensional structure is primarily a function of the number of stored pre-calculated aggregates, which is made more acute if the data is sparse. Credit is given if you can select which aggregates are pre-calculated and additional points are awardedif there is wizard support for this. In memory caching options Credit is given for support analogous to that provided in mature RDBMS products, which allow the DBA to configure the size and use of the cache to optimise performance; for example, for particular users or tables. We give credit if there is some support to enable the administrator to adjust the size of the cache, and extra credit if there is wizard assistance to reduce the skill set necessary to make these adjustments. Informing the user when stored data was last uploaded The user should be able to find out the currency of the data; for instance, ‘when was customer credit rating last updated?’. This may require the system to reach back and retrieve upstream metadata from the data warehouse. Here we describe any facilities the tool offers to support this and give credit if it is easy for the user to ascertain when the data in the model was last refreshed. Additional credit is given if this can be supplemented with information from load processes further upstream. Management of users Multiple users of models with write facilities Relational databases generally offer facilities to prevent update errors when multiple users access the same data. If an MDDB allows users to write values back, it must provide similar locking mechanisms to prevent lost updates. We describe the mechanism used by the tool and give credit if it locks for writing but allows read-only access. User security profiles We describe the way in which individual and group profiles are defined. Credit is given for a system that supports a heterogeneous user community with a granularity which allows visibility, read and write permissions to be controlled at an individual level. Query governance If it is possible for users to issue ‘the query from hell’ that monopolises the processing capabilities of the system, then it is necessary to have some form of query governance to prevent inexperienced or overly demanding users from bringing the system to its knees. (MOLAP tools, for which this is not a problem, receive full credit.) Effective query governance has several levels, from the ability to inform users of the time a query will take, to the prevention of queries above a defined threshold. Credit is given depending on the range and sophistication of available options. Restricting queries to specified times A feature that can be useful to allow for maintenance work or to control the usage pattern is to be able to restrict users to certain days or times of the day. Here we describe and give credit for the available options. Management of metadata Controlling visibility of the ‘road map’ There is a need to be able to hide politically sensitive data and credit is given if this is supported. So the general manager may be able to ‘see’ a dimension relating to personal productivity, but the other employees cannot. Other management features We describe and give extra credit for other functionality, that assists in the management of the system.

Adaptability Summary A brief discussion of the product score in this dimension. Change in business requirements Adding new dimensions to a model The nature of OLAP is that end users will request additions to the model, no matter how thorough the requirements phase. This is a sign that the system is being used, not an indication of a poor requirements spec. Here we give credit for the ease with which new dimensions can be added to the business model and any change management facilities to support this. We give extra credit for a system that incorporates a mapping layer, which defines the data in the data source in business terms. When the business model is created, the developer uses the resources defined in the mapping layer rather than the original source data. The advantages of this approach are that it ensures consistency of terms, it reduces duplication of effort and the layered approach is easier to manage. Re-use of dimension definition Adaptability is facilitated if dimension definitions can be re-used. Credit is given if the newly created dimension can be named, described, stored and easily retrieved. Adding new measures to a model Just as end users will request the addition of new dimensions, they will also want to incorporate new measures into the model. This is credited as in Re- use of dimension definition. Re-use of calculated measure definition Adaptability is facilitated if calculated measures can be reused. For maximum flexibility, these should allow the base measures to be referenced by either a name or an index. Credit is given if the newly created measure can be named, described, stored and easily retrieved. Changing the architecture to reflect business needs A high level distinction between MOLAP and ROLAP architectures is that the first is optimised for speedy retrieval but has limited scalability, whereas the latter can deal with datasets with large numbers of dimension members but is slower. If user requirements always clearly fell into one camp or the other, the choice of tool could be heavily influenced by its mode of use. The reality is that users’ needs do not always point clearly to one mode of storage. For instance, a system that initially seems to require a MOLAP-type solution may then incorporate data sources that put pressures on the scalability of the MDDB. Conversely, end users may, in practice, only use parts of what originally appeared to be data sources with many millions of dimension members and could benefit from conversion to a MOLAP-type solution. Another solution is a HOLAP one, in which summary data is held in a MDDB or similar and the detailed data is held in a relational database and retrieved as required. The user should be unaware of the source of the data being viewed. Here we describe and give credit for the ease of changing the architecture to align it with new user requirements. Changes to data sources Keeping the data source and model schema synchronised The business model the end user works with is a logical entity mapped onto data sources in various ways. In any mapping arrangement such as this there is the potential for divergence between the actual data sources and the representation of them. Here we describe how the two are synchronised and give credit if, when opening a model or report, the user is informed when opening a model or report if some data for the model is unavailable. If there is no possibility of these getting out of synchronisation (for example, in a MDDB) then full credit is given. Automatic updating of members in a dimension While the members of some dimensions are unlikely to change (for example, there will always be 12 months in the time dimension) many will be much more volatile (for example, new stores and products will be added). Develop- ers need to be able to specify how such new members are dealt with. The basic requirement is that members are automatically updated in this situation. However, in some situations the developer may want to lock levels so that new members are not added and it should be evident to the end user that this is the case. The score here reflects the flexibility of the system. We have not considered the support for slowly changing dimensions (for example, change of marital status in customers), as this is the responsibility of the data warehouse rather than the OLAP tool. Metadata Synchronising model and model metadata The model and information about the model need to be synchronised. Some parts of this process can be automated (for instance, if the description of the dimension includes the number of members it contained), but inevitably much of the metadata is manually entered. The simplest, and probably most effective, way of ensuring synchronisation is if the system automatically prompts for new metadata when edits are made. Credit is given for the effectiveness of ensuring synchronisation. In cases where there is no metadata to synchronise full credit is given. Impact analysis Changing the data sources affects on the business models and this, in turn, affects on any reports based on these. Credit is given for tools that support impact analysis so that the consequences of changes can be anticipated and dealt with in advance. Metadata audit trail (technical and end users) If the history of the metadata is stored then end users and technical developers can use this to get an understanding of how the models have changed over time. Additional credit is given here if end users can easily carry out such an audit. Access to upstream metadata Adapting the system is easier if the designer has access to full information about the data. Here we describe any integration with third-party tools that gives access to metadata generated during the extraction part of the process. Ideally this metadata will capture details about the sources, upload details, transformations and the quality checks carried out on the data, as well as descriptive text. Other adaptability features We describe and give extra credit for other functionality that assists adaptability.

Performance tunability Summary A brief discussion of the product score in this dimension. ROLAP Multipass SQL ROLAP tools issue SQL queries against relational databases to retrieve the data required to build the business model. The data can either be retrieved by a single SQL query or using multipass SQL. With multipass SQL, as its name suggests, multiple SQL queries are generated and processed, stored in temporary files and finally combined after processing is complete. The advantage of multipass SQL is that more complex queries can be supported. For example, calculations requiring aggregation at different levels within a dimension; so to show sales at regional level as a percentage of sales at country level requires two passes, one to get the sum of sales at regional level and the second to get the sum of sales at country level. These are then combined to get the percentage. Credit is given if the tool supports multipass SQL. Options for SQL processing SQL processing, such as sorting and ranking, can either be carried out on the database server or the OLAP server. The advantage of using the database server, particularly for operations such as filtering for the top ten, is that the network traffic can be reduced. However, the drawback is that complex processing requires the creation of many temporary tables which can cause a bottleneck. We give credit if the developer has choices about whether the data processing takes place on the database server or the OLAP server, or if the system intelligently balances the processing. Speeding up end-user data access Retrieval time is an issue for ROLAP tools. There are two parts to the process: the retrieval of the data and the calculation of the cross-tab results from it. Data access can be speeded up by the storage of data in relational tables once it has been retrieved, or storing it once it has been further processed for cross-tab display, that is, in a more optimised form. However it is stored, the end user needs to be aware that they are using stored rather than freshly retrieved data, and should be informed about the currency of it. Credit is given if data can be stored for re-use and the user is always aware that stored data is being accessed. Aggregate navigator Aggregate navigators process SQL queries so that they automatically make use of summary tables and thus speed up retrieval time by minimising the processing. Credit is given if the tool offers integration with an aggregate navigator or equivalent built-in functionality. MOLAP Trading off load time/size and performance Load time, rather than end-user performance, is a particular issue for MDDBs. A major contributing factor is the re-calculation of stored aggregates. Credit is given if the tool offers support so the administrator can decide how to trade off the poorer performance resulting from an incomplete set of precalculated aggregates against the faster load time and reduced size resulting from this. Extra credit is given if the tool minimises the effect of adding new data by making use of metadata about the dimension and recalculating only those values that are affected. (This is sometimes called incremental roll-up.) Support for multiple users We describe the limits on end-user numbers and ways in which these can be overcome. Credit is given in proportion to the size of user community the tool can support. Processing Use of native SQL to speed up data extraction From an OLAP tool vendor’s point of view, OLE DB or ODBC is the simplest way to connect to data sources for the extraction of data, as it means that only one set of SQL commands has to be produced regardless of the type of database being accessed. However, if the OLAP engine can generate native SQL for data extraction, the extraction process can frequently be speeded up. Credit is given if native SQL can be used to extract data from the major RDBMSs. Distribution of processing The OLAP engine is responsible for data extraction, the calculation of aggregations and the creation of cross-tabular data. There is the danger that as the number of end-user queries increases it creates a bottleneck. The most obvious way of avoiding this is to distribute the processing with automatic load balancing. Here we describe and give credit for such facilities. SMP support Parallelism speeds up processing. Here we give credit if the server component of the tool is based upon a multi-threaded architecture that can take advantage of symmetric multiprocessing. Other performance tunability features We describe and give extra credit for other functionality that assists in performance tunability.

Customisation Summary A brief discussion of the product score in this dimension. Customisation Option of using a restricted interface Although most of the tools described in these evaluations are easy to use, the range of options available means that there is always a learning curve for the new or occasional user. What is needed is a means of producing a suitable interface for such users. There are two approaches to the problem: • for the tool to offer a restricted interface option • for the developer to be able to produce, essentially using point-and-click rather than programming, a simple-to-use front end to selected models. The option of a restricted interface is evaluated here, and the second option in the next section. Ease of producing EIS style-reports We describe how a simple-to-use front end is created within the tool and give credit if it is straightforward. Applications Simple Web applications The problem this section addresses is identical to the one described above under Ease of producing EIS-style reports, except that we are now considering the production of such an EIS-style report that can be viewed with a Web browser. Credit is given for ease of producing such an application. Development environment The development of applications is greatly facilitated by the provision of an OLAP-specific development environment that includes components such as tables supporting drill-down, and linked chart and visual display options. General application development languages such as Visual Basic and C++ do not provide such components: they are only provided in the specialist OLAP application development environments. Credit is given for the nature and quality of such specialist support. Use of third-party development tools The drawback of the specialist development environment is that it requires the programmer to learn another language. Here we describe whether applications can be developed in a familiar programming environment such as Visual Basic, C++ and/or Java. Credit is given for the number of such environments supported. Other customisation features We describe and give extra credit for other functionality that assists in customisation, including support for localisation.

Growth, transition and change

Trends in business intelligence and implications for the OLAP market

Summary...... 2 Growth of the business intelligence market...... 4 Trends in business intelligence ...... 10 Key messages for the market ...... 14 Article: Market analysis and forecast Ovum Evaluates: OLAP

Summary Growth and uncertainty The OLAP software tools market is worth $1.5 billion in 1999 and will grow steadily to a $4 billion-plus industry by 2003. The market is complex and is characterised by a lack of clear leaders, a large number of vendors and a complex web of alliances and partnerships. Accompanying this growth is uncertainty. New entrants, such as Microsoft and SAP, add a new dimension to the market and have radically changed its structure. The OLAP market cannot be viewed in isolation – it overlaps with a wider ‘market’ defined as business intelligence. Business intelligence is a high growth sector which Ovum predicts will have an overall spend (including software, hardware and services) of over $20 billion at the start of the new millennium (see Figure 1). Underlying the steady growth are radical changes in the way that business intelligence is packaged and delivered. There are significant trends in: • how business intelligence systems are built • what is being built. The most significant change is the transition from a ‘build’ to a ‘buy’ paradigm.

Figure 1 Overall business intelligence spend ($ billion)

$ billion $ 20

0 1999 2000 2001 2002 2003

Implications for the OLAP market The emerging trends in business intelligence have important implications for the OLAP market. The successful vendors and IT users will be those that can use the new trends to implement business intelligence systems. For OLAP vendors, the maturation of the market, combined with the entry of influential new players has spurred a great deal of movement. These vendors are increasingly having to reconsider their existing market strategies. In order to remain competitive, they need to supply solutions rather than ‘point’ tools. They also need to exploit new channels and target new markets (vertical applications and CRM) to gain market share. For IT users, the growing number of strategy options for implementing business intelligence systems has increased the complexity of buying decisions. They are faced with a wide choice of strategies, ranging from traditional data warehouse development using best-of-breed point tools, end-to-end infrastructures and packaged analytical applications purchased from speciality solution providers. They must assess the value of buying solutions from various vendors and steer a careful course between ‘buy’ and ‘build’ solutions, reconciling the benefits and pitfalls of each approach. Systems integrators are benefiting from the increasing trend to outsource the implementation of business intelligence systems. They need to be aware of the changing trends and tailor their existing service offerings accordingly. Most of the large consultancies have already invested heavily in one or more of the competing high-level strategies, and will be anxious not to be caught out.

Growth of the business intelligence market OLAP complements a larger ‘market’, defined as business intelligence. In this section we define the scope of this market, examine its growth, and assess the impact of changes to the future development of the OLAP market.

Business intelligence Business intelligence is a wide term that requires definition of both processes and technology. It includes: • technology-related processes, such as extracting data from a company’s operational systems and databases and integrating that data into a coherent whole in order to make it suitable for analysis • business-related processes, such as determining the appropriate forms of analysis required to support decision making, disseminating and interpreting the results of analysis, and determining how best to feed back the results into a company’s operations. These processes are supported by a wide range of closely related tools and technologies that make up the business intelligence market: • OLAP – including multidimensional databases, relational OLAP engines and front-end OLAP clients • query and reporting • data mining • data extract, transform and load (ETL) • relational DBMSs. Typically, a large business intelligence implementation will use many, if not all, of these tools, as well as a significant amount of consulting. Market growth Ovum’s estimate of the industry spend on business intelligence software (excluding services) over the next five years is shown in Figure 2. We expect business intelligence to be a more popular area for new development in 1999 than OLTP. But there are still a significant number of user organisations that are holding off development until after 2000, when the market will experience rapid growth. Services are growing faster Services are the fastest growing segments of the business intelligence market (as shown in Figure 3), representing a significant portion of overall spend on business intelligence.

Figure 2 Growth of the business intelligence software market ($ billion)

$ billion 10

0 1999 2000 2001 2002 2003

Figure 3 Growth of the services market for business intelligence ($ billion)

$ billion 5

0 1999 2000 2001 2002 2003

Business intelligence is now seen as a major growth area by the IT service industry. All the large systems integrators and global consultancies are targeting business intelligence as one of two key growth areas (along with e-commerce). Most have made significant investments in the expertise to deliver advice and implementation services in generic data warehousing, specialised decision support (for example, customer churn and risk analysis), and packaged analytical applications. They are also acquiring specialised business intelligence consultancies to strengthen their business intelligence services. Data warehousing Business intelligence is often closely associated with data warehousing. While there is considerable overlap in terms of definition, data warehousing mainly focuses on the technical process of building and maintaining a store of data that is specifically intended to be used for decision support. There are back-end processes for loading data into the data warehouse using ETL tools, and front-end processes for accessing and analysing data using OLAP and data-mining tools.

For some years now, OLAP vendors have benefited from the surge of interest in data warehousing (and datamart) implementation. Most medium to large organisations have a data warehouse strategy in place, and most of those data warehouses are feeding data to one or more OLAP tools.

OLAP software licences Market growth The OLAP tools business has been flourishing for several years and will continue to do so for the next five years at least. Revenue from the sale of end-user OLAP software licences (not including implementation and consultancy services) is shown in Figure 4. As with the overall business intelligence market, there is steady, higher than average growth. However, the market is mature; many organisations will have already built data warehouses and will, in most cases, continue to maintain and develop them. For many, this will involve either buying new OLAP tools, replacing old tools or choosing which OLAP tool to consolidate on where there are many in use in an organisation. A superficial analysis of this consistent market growth suggests ‘more of the same’. However, accompanying growth is a radical shift in the way in which OLAP tools are being packaged by vendors and delivered to customers. Market composition A survey of the OLAP market shows that it is very fragmented. There are more than 30 vendors providing a wide spectrum of OLAP products, though not all are direct competitors. Despite the consolidation that might have been expected, and the number of mergers and acquisitions that have recently occurred, the number of major players in the market has remained more or less the same. New entrants into the OLAP market are Microsoft, SAP and the merchant database vendors, and their impact is already being felt.

Figure 4 Growth of the OLAP software market ($ billion)

2 $ billion 1

0 1999 2000 2001 2002 2003

Most OLAP vendors are increasing their revenues, even though they may be losing market share. There is no clear-cut leader; the largest player is Hyperion Solutions (formed by the merger between Hyperion Software and Arbor Software in August 1998), which has just over 20% of market share. Market segment analysis OLAP suppliers can be grouped into four principal categories, although many span multiple categories with different products or by unbundling and bundling components. Multidimensional databases (MDDBs) This segment has existed since the late 1980s and consists of unbundled multidimensional or hybrid databases. Most MDDBs are sold at a premium price by specialist vendors that do nothing else, leaving partners to supply tools and application components. The three leading vendors in this segment are Hyperion Essbase, Applix TM1 and Oracle Express. In late-1998, they were joined by Microsoft with its OLAP Services module bundled into SQL Server 7.0. Vendors in this segment are competing head-on as MDDB vendors, and it is unlikely that more than two or three vendors can survive with this strategy. MDDBs are generally evolving to support more flexible ‘hybrid’ OLAP architectures, thereby taking the heat out of the MOLAP versus ROLAP argument that polarised the market in the early 1990s. Relational OLAP (ROLAP) Relational OLAP (ROLAP) represents the smallest sector. The two leading proponents in this field are MicroStrategy and Information Advantage. ROLAP tools have generated a great deal of publicity thanks to some astute marketing from these vendors, and increased interest from merchant database vendors such as Oracle, Sybase and Informix. The two leading ROLAP vendors show high revenue growth, and the market will continue to be sustained by the rising popularity of customer- focused applications, such as customer churn analysis and customer relationship management (CRM), which require analysis of large volumes of data. However, ROLAP will remain a niche market, characterised by fewer ‘big-ticket’ sales. Typical implementations tend to be large and expensive and almost always come with a significant amount of consulting either from the vendor or an external services organisation. Desktop OLAP Desktop OLAP clients have existed since 1990, but it has only recently become recognised as a distinct segment of the OLAP market. This segment is especially crowded and the leading vendors – such as Business Objects, Brio Technology and Cognos – are all direct competitors. The leading vendors have established a large and complex web of alliances and reseller agreements; the bulk of sales are made via OEMs and VARs, which bundle low-cost desktop OLAP tools as part of a complete business intelligence solution. Once the overall OLAP market growth slows down, a major shake out seems inevitable in this segment. The leaders will be those that can establish thousands of user seats in an organisation – but only performing simple OLAP analyses.

Analytical applications Analytical applications are sold either as packaged ‘ready to go’ applications (conceptually a ‘datamart-in-a-box’) or complete toolkits. There are several specialist vendors that provide these types of products: Hyperion Solutions, Comshare, Kenan, Gentia Software, SAS and Seagate Software. ERP vendors such as SAP and PeopleSoft are also entering this segement by selling packaged vertical datamarts, which include specialised OLAP applications that analyse data from the OLTP systems. As many of these analytical applications are aimed at vertical or horizontal markets, there is room for many vendors to happily co-exist. But the successful vendors will be those that can identify a lucrative niche market and gain significant market share within it. In the long term there may only be room for two or three major players in each narrow niche. Packaged analytical applications represent the newest market segment in business intelligence. We expect the number of applications to increase dramatically over the coming years. Market development Over the next five years, the market will be shaped by large, influential players, notably Microsoft and SAP. Microsoft’s SQL Server 7.0 OLAP Services, and SAP’s Business Information Warehouse (SAP BW) add a new dimension to the market: • Microsoft’s entry will serve to raise the profile of OLAP considerably and accelerate its adoption beyond its traditional niche (namely large corporate finance departments) • Microsoft’s competitive marketing strategy of bundling OLAP capabilities into SQL Server 7.0 will expand OLAP at the low- to mid- end of the market • the backing of a large influential vendor also has the potential to address the interoperability issues that have dogged OLAP for some years. The OLE DB for OLAP API standard, spearheaded by Microsoft, is rapidly becoming an industry standard that is enabling OLAP servers and clients from different vendors to work together, and is stimulating ISVs to produce a new generation of tools, applications and clients • SAP BW raises the stakes by providing a low-cost, high return on investment packaged data warehouses with integrated OLAP analytical applications. This is a direct challenge to vendors (both of OLAP and ETL origin) that previously managed by selling point tools to access and analyse SAP data. Microsoft’s entry drives OLAP closer to a commodity software market. Using a combination of simplicity, pricing and bundling, Microsoft is aiming to make OLAP servers almost as widely used as relational databases. OLAP servers will be increasingly treated as commodity components that can be bought, configured and embedded without a great deal of time and effort. SAP is also positioning its BW product as a solution that can be used out of the box, where OLAP is a value-added component.

However, Microsoft’s product is not a complete business intelligence solution in its own right. Although it has forced a sharp drop in the average cost of OLAP, the market will continue to be sustained by volume growth through its wider use. Users are also starting to recognise the limitations of SAP’s BW product for providing a tightly focused point analytical solution. The overall market boundaries will continue to expand. Internet technologies (which dominated all aspects of the OLAP market in 1999) and emerging markets, such as customer relationship management (CRM) and e-commerce, have encouraged wider deployment. Vendors will have to face some difficult choices over the next year to cope with the new market dynamics. A major shake out in the OLAP market is likely to happen and we expect to see a great deal of changes in the fortunes of OLAP vendors over the next five years. Vendors will either ‘reinvent’ themselves as specialist solution providers; merge; acquire one another or simply disappear.

Trends in business intelligence Since 1998 there have been significant changes in how business intelligence solutions have been built and maintained. The pace of evolution remains as fast as ever; the only thing that has changed is the nature of the trends. This section examines the changing ways in which business intelligence systems, including data warehouses, are being built and delivered.

The overall trend from build to buy A number of trends in business intelligence are having a profound affect on the OLAP market. The most significant is the transition from best-of-breed point tool solutions to packaged ‘ready to go’ business intelligence solutions. Underlying this trend is the emphasis on a ‘buy’, rather than ‘build’, paradigm as shown in Figure 5. IT user organisations embarking on new business intelligence projects are now expecting to concentrate more on data analysis, while more of the data plumbing work is to be done for them via: • the increased use of integrated toolsets • the use of external service organisations to implement solutions • an out-of-the-box solution, bought from either specialised analytical application vendors or packaged datamart and application vendors (such as SAP). Underlying this shift are six individual trends that affect how business intelligence solutions are built and what type of system is built.

Figure 5 ‘Buy’ not ‘build’

% of projects requiring tool selection

% of projects which are ‘buy’ rather than build

Time

How solutions are built The emergence of tools Before the data warehousing/OLAP boom of the mid-1990s, business intelligence solutions were based on hand-coded solutions (or on pioneering OLAP tools such as IRI’s Express) at the front-end. At the back-end, hand coding was inescapable since there were no commercially available tools to help developers. The introduction of a wide selection of OLAP tools for the front end and ETL (data extract, transform and load) tools for the back end, and the slow, but steady integration between the two, has increased the proportion of business intelligence solutions assembled in-house from best-of-breed point products – traditional data warehousing – to decrease initial development and subsequent maintenance costs. The shift towards ‘end-to-end’ infrastructure A logical continuation of the increasing use of tools is the trend to selecting integrated sets of tools that cover both the front- and back-end processes, rather than using best-of-breed point tools that only cover subsets of the required infrastructure. IT users wishing to implement a new business intelligence/data warehousing project are increasingly expecting to be offered ‘end-to-end’ solutions rather than a ‘grab-bag’ of loosely integrated tools that are often sourced from different vendors. The range of vendors claiming to offer complete end-to-end business intelligence solutions has only recently become significant. Most of the major database software vendors (IBM, Informix, Sybase and Oracle) have created their own end-to-end offerings by including OLAP (as an OEM component) with their DBMS engines, where it becomes a standard database function. A further manifestation of the end-to-end trend is the growth in the number of systems integrators offering data warehousing and business intelligence systems expertise. But each solution offered has varying levels of success and there is still considerable scope for improvement in terms of integration, usability and service provision. The increasing use of systems integrators for implementation At the same time as increasing tool use and expecting increased end-to-end integration, IT user organisations have increasingly been outsourcing implementation. In some cases, this includes the whole business analysis function, including infrastructure and analysis expertise. In the mid-1990s, there were hardly any consultants with any useful business intelligence implementation experience, and companies wishing to implement a data warehouse had little choice but to use in-house skills to build it. This has changed. The large-scale IT user organisations with the confidence and in-house resources to undertake data warehousing themselves have largely done so. But new implementers are increasingly turning to systems integrators, and business intelligence is now seen as a major growth area by all the IT services industry, as shown in Figure 3.

What type of system is built The swing from specific to enterprise wide solutions … and back again Prior to the mid-1990s, business intelligence projects were treated as ‘conventional’ IT projects, driven by specific line-of-business requirements (for example, to enable interactive drill-down on sales figures) and implemented as ‘one-off’ solutions to a specific business problem. The data warehousing movement in the early to mid-1990s encouraged companies to take an enterprise-wide approach to business intelligence infrastructure, to improve data quality and consistency and to increase the business value of analytical information. A substantial proportion of large companies have taken this route, but many still continue to develop line-of- business systems parallel to their data warehousing projects and/or have returned to this paradigm as a reaction to a general dissatisfaction with the centralised warehouse. The move back to specific solutions is best illustrated by the current wave of interest of customer-focused applications under the banner of CRM. The emergence of packaged analytical applications Starting in the 1970s, a community of vendors started to offer packaged off- the-shelf solutions to common business intelligence problems – for example, risk/fraud analysis in the financial market and, more recently, customer churn analysis for telcos. These vendors’ products competed with the in- house development of business intelligence solutions. Several of these companies still exist, and the market for problem-specific packaged analytical applications (or packaged datamarts) has remained healthy into the late 1990s. Packaged analytical applications (sometimes called packaged datamarts) claim to expedite the process of building a fully functional data warehouse. Though limited in functionality in comparison, they have proved particularly popular in budgeting, forecasting, sales and marketing, customer analysis (churn and CRM) and ‘balanced scorecard’ – a method of aggregat- ing multiple performance indicators into a composite index of business performance measures. The problem in implementing such applications has always been in providing suitable data for analysis – the ‘back-end problem’. Some packaged application companies have actually disappeared as a result of demanding specialised data for analysis without adequately helping their customers with the back-end problem. Nevertheless, partially as a result of a backlash against unrealistic expectations for data warehousing, there has been an increasing demand for packaged analytical applications throughout the late 1990s. This has been acknowledged by data warehouse infrastructure vendors, both of OLAP and ETL (data extract, transform and load) origin, in: • their sales and marketing positioning – they are now offering solutions rather than infrastructure • acquisition of/by, or very close partnership with, specialist packaged analytical application vendors. A prime example is the merger between Hyperion and Arbor.

The emergence of analytical applications from operational application vendors Packaged analytical applications have traditionally been developed by, and purchased from, specialised vendors in the business intelligence sector. But operational ERP (Enterprise Resource Planning) application vendors are picking up on this trend and are quietly moving into this sector. In 1997, SAP took the lead among packaged OLTP application vendors in promising to provide packaged analytical functionality via SAP BW. Similarly, PeopleSoft announced its ‘Enterprise Warehouse’ in 1998, and Oracle is also developing a similar product as part of its Strategic Enterprise Manage- ment Suite. For IT users, this seems an attractive proposition as it provides: • a ‘one-stop shop’ for analytical functionality as well as OLTP – surpassing even the perceived benefits of a one-stop shop for business intelligence solutions • an apparent solution to the ‘back-end problem’ if the information to be analysed by SAP BW is sourced mainly from SAP R/3 systems – in practice, things are not so simple.

Key messages for the market The emerging trends we have outlined point to a general reduction of business intelligence projects based on best-of-breed tool selection and an increase in the number of bought solutions – though there is still only a limited number of ‘off-the-shelf’ solutions and many of these are early versions. The trend to ‘buy’ rather than ‘build’ business intelligence provides a new set of opportunities and challenges. In this section we examine the implications of these for IT users, OLAP vendors and systems integrators.

Implications for IT users IT users are faced with an increased number of high-level strategic choices – some new, some resuscitated from the 1970s, some genuine and some partially illusionary – for their business intelligence needs. These include: • ‘classical’ bespoke data warehousing using best-of-breed point tools • in-house assembly using end-to-end product suites and infrastructures • the use of packaged analytical applications supplied by speciality solution providers – without intervening data warehouses • the use of packaged analytical solutions combined with packaged operational systems – implicitly from the same vendor • the dependence on any level of service provider, consultancy or systems integrator involvement for any of the above options. However, the transition to buy rather than build is much less straightforward than some vendors would like potential customers to believe. Two key issues arising from these changes are how to: • assess the amount of added value from ‘one-stop shop’ solutions • steer a careful course between the advantages of point analytical solutions and the dangers of unintegrated ‘islands’ of information. Whatever option is chosen, and whatever level of service provider involvement, outsourcing investment decisions frequently run into millions of dollars. The pressure on IT user organisations to make the ‘right’ decision has never been higher and depends on several factors, including the type and structure of the organisation – especially its IT department. Assess the added value of ‘one-stop shop’ solutions The added value of a ‘one-stop shop’ solution over an infrastructure based on best-of-breed components depends on whether: • the individual components of the one-stop shop solution are a good technical fit for user requirements – in almost all cases, the OLAP and ETL components will be the most critical • the one-stop shop provider offers any value-added integration between the components of its solution. Often the OLAP and ETL components will be OEM’d from other vendors. Even if all the components are ‘owned’ by a single supplier, they are likely to have been developed in isolation by different R&D groups, and users should be very cautious about assuming that they will be well integrated

• the vendor has the ability to provide a one-stop shop for the ‘soft’ components of the system – licence negotiation, customer support and services, stability and longevity – and the value of these to the IT user organisation. Some vendors are much more capable than others in these areas. Moreover, the very wide range of functionality and maturity of the components of one-stop shop solutions makes it very important for users to check the technical suitability of the solution before considering the added value of ‘soft’ factors. There are several vendors offering to relieve IT users of the pain of OLAP tool selection and the subsequent integration. We assess the different sets of players and their offerings, and assess their worth to user organisations as well as the pitfalls of each approach that you should be aware of. ISVs A number of independent software companies position themselves as offering a complete ‘turnkey’ business intelligence solution using their own products. These range from the relatively small start-up companies (generally associated with datamarts) such as Sagent and Broadbase, to the larger, well established software producers, such as Platinum Technology and SAS Institute. While it is not easy to generalise about the added value of these infrastructures, three observations can be made: • in a small scale business environment, with limited access to IT support, there is undoubtedly added value over an unintegrated set of tools • not all components are equally strong, so a best-of-breed infrastructure would not necessarily deliver greater functionality • the benefits of integration, particularly metadata, have not been fully exploited. Merchant database vendors A number of database companies have assembled ‘end-to-end’ solutions by OEM’ing components from point tool vendors. Examples include: • Sybase, which includes WhiteLight Systems’ WhiteLight ROLAP server as an OEM extension to its Warehouse Studio (under the brand name ‘Power Dimensions’) • IBM, which licenses Hyperion Solutions’ Essbase multidimensional server for its IBM DB2 OLAP product and is a core element in its Visual Warehouse offering. These vendors claim to offer above and beyond what OLAP vendors are offering. The real question is the extent to which there is real integration of the components or whether the integration is primarily in the presentation layer. In most cases, adopting the database vendor’s package is effectively adopting the database vendor’s own best-of-breed selection. The primary benefit is the reduction in decision-making effort, but seldom does the value of the whole much exceed the sum of the value of the parts in technical terms. Systems integrators Increasingly, systems integrators are developing business intelligence (and data warehousing) solutions as one of their key service offerings. Often the

systems integrators are the delivery mechanism for one of the business intelligence solutions described above. Where implementation is done by a systems integrator it is often difficult for the customer to know just how ‘out of the box’ the solution really is. The added value of using a systems integrator to provide a one-stop shop solution is that it insulates the user from tool choice, implementation and integration issues. Again, the degree of added value depends on the amount of integration needed. When, and if, packaged analytical solutions deliver on their promises, the amount of added value will diminish for integration, and service providers will concentrate more on providing assistance with data analysis and using the results to improve business performance. Hardware vendors The ‘one-stop shop’ offerings of hardware manufacturers are typically a collection of components from third-party vendors; the added value is limited to the benefits of dealing with one party and non-technological issues such as licensing and pricing. Many of these hardware vendors act mainly as systems integrators – though this is not clear from their marketing. Steer a careful course between build and buy Vendors are offering packaged analytical applications that appear to be ‘out of the box’ (for example, customer churn analysis) and do not seem to relate to data warehousing at all. Packaged analytical applications promise a great deal that appeals to line-of-business managers. But as the number of packaged analytical applications – bought from either OLAP vendors or packaged operational application vendors such as SAP – increases, there will be a greater temptation for users to adopt them as short-term point analytical solutions, without full consideration of the long- term consequences. There are a number of potential pitfalls associated with the packaged approach: • users need to reconcile the advantages of point analytical solutions with the dangers of unintegrated ‘islands’ of information that may result from tightly focused analytical applications. These point analytical solutions threaten to re-issue all the warnings about unintegrated ‘stove pipe’ datamarts, with the substitution of ‘packaged analytical applications’ for the term ‘independent datamart’ • users need to recognise the limitations of the packaged approach. Only a limited number of applications are available out-of-the-box – mainly targeted at sales & marketing and financial applications. If a more specialised application is needed, such as outcome analysis for healthcare or seat utilisation for the airline industry, then users may have to build it themselves • the packaged approach assumes that the data required for the application is in a single, static and ready-to-use form. It further assumes that competitive advantage can be gained without a great deal of further customisation. If your information needs exceed the capabilities of the packaged applications, then a cross-functional data warehouse or datamart would be a more flexible option.

We would expect, in the short term at least, the lure of packaged analytical solutions to override such concerns for many user organisations. But in the longer term, IT users will need to ensure that vendors of packaged analytical solutions have a convincing story about integrating point solutions with an enterprise-wide business intelligence strategy. Otherwise, many users who buy the packaged analytical strategies will find themselves building data warehouses as well, to address the inflexibility of this approach.

Implications for OLAP vendors OLAP vendors will have to face difficult choices over the next few years as they adapt their strategies to effectively compete in the newly defined market. Many have already positioned themselves to exploit new opportunities. But the successful OLAP vendors will be those that manage the implications of the trends in the business intelligence market in their favour. They will need to: • rethink their existing market strategies and pricing models – in light of Microsoft’s entry into the market • offer more complete business intelligence solutions – not just point tools • take advantage of new opportunities in packaged analytical applications • exploit new channels for delivering OLAP • embrace the emerging CRM market for OLAP. Learn to live with Microsoft ... whether you like it or not Few vendors would want to compete directly with Microsoft’s aggressive marketing. OLAP vendors are now faced with a number of difficult choices to take advantage of, or simply survive, Microsoft’s entry into the market. They can: • specialise and try to dominate a profitable vertical, or niche, market with analytical applications. OLAP server vendors such as Gentia Software, Pilot Software and possibly Arbor (following its recent merger with Hyperion) have shifted their focus towards specialised analytical applications rather than selling point OLAP tools. They are now building OLAP functionality into business applications as a value-added component. Hyperion Solutions is one vendor that focuses on this application-oriented approach. It offers solutions that integrate with major packages from JD Edwards, PeopleSoft and i2 Technologies • endorse the Microsoft OLE DB for OLAP standard and fill niches that Microsoft does not want; Applix’s TMI MDDB server was the first OLAP offering to support OLE DB for OLAP as a data provider and targets specialised financial users; WhiteLight and SAS have also committed to support the standard. Over 50 OLAP vendors – mostly of desktop origin – have also announced support as consumers

• exploit the new market created by Microsoft’s OLAP Services product; while Microsoft may have washed away most of the OLAP server market, it has left the door wide open for ISVs on the client side to meet the demand for new tools, applications and clients. A number of start-up software companies are riding on the back of SQL Server 7.0 and focusing on developing value-added modules. Portola Dimensional Systems, Maximal, Decisionism and PowerPlan are also developing SQL Server-exploitative client tools – these companies are betting that users can do a better job from scratch rather than by adapting. Established OLAP vendors have also responded: Knosys has discontinued its OLAP server and focuses on adding value to Microsoft’s product; Cognos has developed NovaView, a client tool specially designed to access OLAP Services cubes • find the next frontier: ROLAP vendors such as IA and MicroStrategy are now targeting their products at Internet business intelligence portals and information broadcasting business respectively. In March 1999, Hummingbird acquired PCDOCS, a document management company, and plans to integrate its OLAP and document management technology within a knowledge management framework. Rethink your pricing strategy – the cost of OLAP is tumbling The greatest impact of Microsoft’s entry into the market is on the price of OLAP, particularly for the low- to mid-end of the market. The high cost of OLAP technology has been under pressure for some years now, and Microsoft’s SQL Server 7.0 product will accelerate the trend towards lower pricing. The tumbling cost of OLAP is undoubtedly good news for users. But it has significant implications for OLAP vendors: • the most direct challenge is to MDDB vendors. Given that the entry level price for existing MDDBs has been in excess of $40,000, Microsoft SQL Server will have a radical impact from a pricing standpoint alone. These vendors will now struggle to justify their premium-priced products • ROLAP tool vendors are less directly affected. They are still benefiting from the growth of data warehousing and most implementation comes with a significant services and consulting overhead. However, they too cannot afford to ignore Microsoft in the long term • desktop OLAP vendors will be short-term beneficiaries because of the increased demand for OLAP clients. But they will face increasing competition from Microsoft in upcoming versions of Office 2000 which promises stronger OLAP capabilities • packaged application vendors that can or already use OLAP should be long-term beneficiaries; they now have access to low-cost OLAP technology on a profitable OEM basis. This increases the possibility of more applications becoming ‘OLAP-enabled’.

Provide complete business intelligence solutions – not just point tools IT users are no longer buying point OLAP tools. They are increasingly looking for a more complete solution. Only a handful of database and software vendors can justifiably claim to provide a complete end-to-end business intelligence solution. OLAP vendors should be seeking to expand their solutions offerings either by partnering closely with vendors and systems integrators offering complementary products or services, or by buying-in complementary technology to provide a suite of business intelligence products. Two examples of vendors that have opted for the latter route are: • Information Advantage, which acquired IQ Software and its enterprise query, analysis and reporting tools to complement its ROLAP system • Seagate Software, which has acquired or developed a number of business intelligence products, including the Holos development system, Seagate (Crystal) Reports, Seagate Info and Seagate Worksheet Web OLAP client. Take advantage of new opportunities in packaged applications The growing popularity of packaged applications that include OLAP as a value-added component provides immense opportunities for OLAP vendors to: • OEM their core OLAP products to specialist applications vendors – depending on the level of integration, users may not even be aware that a key component of the application is an embedded OLAP server engine or client • partner with systems integrators that are installing business intelligence solutions built using packaged analytical applications. Be prepared to exploit new channels In the early days of tool support for building data warehouses there was a direct channel between the tool providers and the IT users building the system. With the ‘build’ to ‘buy’ shift, this channel has now been supplemented by the indirect channels via systems integrators and software and hardware vendors providing end-to-end solutions. The implications of this are straightforward: OLAP vendors that create partnerships to exploit these new channels will gain market share. Embrace CRM for OLAP CRM (customer relationship management) represents a significant market opportunity for OLAP tool vendors. There are a wealth of definitions for CRM, but the generally agreed rationale is that knowing about your customers enables you to acquire them at less cost, service them more efficiently, cross-sell more effectively and retain them.

We differentiate between tactical and strategic CRM: • ‘tactical CRM’ is associated with customer-facing systems such as call centres and other solutions that directly address the customers’ needs. Such tactical CRM systems are often self-contained and rely on a datastore fed by the CRM application which is not integrated with other corporate data sources • ‘strategic CRM’ is associated with finding out about the customer and often uses OLAP and data-mining tools; for instance, to identify customer segmentation. Strategic CRM does not usually have its own data sources, but draws from multiple data stores from within the company. OLAP (and related technologies such as data mining) will undoubtedly ride on the recent wave of interest created by CRM, as there is a hidden market within it for CRM data analysis and presentation. Customers can benefit from integrated applications, and OLAP and CRM vendors can benefit from a wider market for their products. All the ROLAP vendors already have, or plan to provide, CRM solutions based on their technology. Turnkey datamart vendors, such as Broadbase, are also unveiling a number of packaged analytical applications designed specifically for CRM. CRM is also bringing data access, analysis and management companies into close partnerships. We expect to see significant mergers and acquisitions and alliances between CRM software applications vendors and data warehousing tool (OLAP and ETL) providers in the future.

Implications for systems integrators The trend in business intelligence is increasingly towards outsourcing implementation. This is of great interest to systems integrators, who have made significant investments in one or more of the competing high-level strategies to provide expertise and deliver advice and implementation services in generic data warehousing, specialised decision support (for example, customer churn and risk analysis), and packaged applications (SAP R/3, for example). Systems integrators need to give advice about the conflicting choices facing their clients. But with many large systems integrators the ‘buy from vendor X’ and ‘build a data warehouse’ options are handled by completely separate business units – each of which may only be able to implement one of the options. Therefore, they need to resolve how they should be sizing their investments in generic data warehousing advice and implementation services for SAP’s BW for example – a directly competing type of solution probably handled by a completely different business unit. Furthermore, a consultant’s business unit may only be able to implement one of the options.

Criteria

End-user functionality ...... 2 Building the business model...... 5 Advanced analytical power ...... 8 Web support ...... 11 Management ...... 14 Adaptability ...... 17 Performance tunability...... 20 Customisation ...... 23 End-user functionality

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BusinessObjects

DecisionSuite

DSS Product Suite

Essbase

Oracle Express Development Tools

PowerPlay

Seagate Holos

Gentia Platform for Analytical Applications

SQL Server 7.0 OLAP Services

Business Objects – BusinessObjects BusinessObjects’ main strength is its ease-of-use; it provides excellent ‘out- of-the-box’ functionality with little or no need for prior adaptation. It shields users from the complexities of SQL, providing access to data through an easy-to-use mapping layer. All OLAP functions are available through point- and-click, regardless of whether a user wants to analyse models or format a report. Reports can be shared and distributed by using the Document Agent Server, or via integration with e-mail systems. Cognos – PowerPlay PowerPlay is an extremely easy-to-use tool with a very intuitive interface. The metadata support to aid end-user understanding of the models includes a description of dimensions and measures. However, there is no support from within the tool to enable end users to schedule the distribution of reports to colleagues, or to subscribe to such reports. Gentia – Gentia Platform for Analytical Applications Gentia is primarily an application development environment, so the end- user functionality largely depends on what the developer builds into the application. Here, we have considered the functionality that is easily incorporated into applications (that is, there is component support) and that provided by the Microsoft Excel Add-in. The Gentia tools provide all the usual OLAP functions of drill-down and pivot. Distribution is supported by publishing reports, contained in ‘books’, ‘chapters’ and ‘pages’ to users and workgroups. The product would be enhanced by a wider range of front-end user tools, direct support for distribution via e-mail and the addition of a subscription service. Hyperion Solutions – Essbase The range of end-user analysis, presentation and reporting functions depend largely on the interface used. The Essbase spreadsheet interface can be used ‘out-of-the-box’, but does not offer the same flexibility as other OLAP front- end tools. Wired for OLAP provides a more graphical presentation interface for OLAP analysis and reporting. However, there is no direct support for sharing and distributing reports. Generally, this is provided by integrating with third-party tools, such as Seagate Info. Information Advantage – DecisionSuite DecisionSuite supports an extremely intuitive notebook-style interface that is consistent across all its client interfaces. Most OLAP functions are easily available from reports. Reports and calculations are easily defined by end users, without IS involvement. However, DecisionSuite’s real strength is its support for flexible group-working. It provides a number of useful features to promote the sharing and intelligent distribution of reports. Particularly impressive is the tool’s collaborative working environment, which allows end users to access shared reports via e-mail, customise them and store them in their own personalised workspace. Microsoft – SQL Server 7.0 OLAP Services The end-user functionality is almost entirely dependent on the end-user client tool used. The features described here are found in entry level tools based on Microsoft’s PivotTable Service, a COM component. More extensive features, such as subscription and distribution services, are outside of the scope of this component, but are provided by more powerful tools that can access OLAP Services models using the OLE DB for OLAP interface (such as Cognos’s PowerPlay and Business Object’s BusinessObjects, both of which are evaluated elsewhere in this report). Microstrategy – DSS Product Suite The initial/casual end user can access the multidimensional model via a desktop or web interface. Ease of access via the desktop interface is facilitated by the ability to select the ‘high-level user’ interface, which does not include development functionality. In general, the end-user interface provides the range of features users have come to expect. DSS Broadcaster adds some very useful distribution facilities. The toolkit is prevented from getting a higher score due to its inability to search the metadata for relevant reports and to add text or OLE objects to reports. This latter point results in reports that are perfectly functional, but lack many of the presentational features that other products provide. Oracle – Oracle Express Development Suite The end-user functionality depends on the tool used to access the model or application. Here, we mainly assess the functionality offered by Analyzer when accessing a multidimensional business model. Although the model is easy to use and offers the expected range of drill-down and pivot features, the product is prevented from getting a higher score for this criteria by the lack of support for cataloguing, publishing and distributing models and reports. The tool could benefit from absorbing some of the features of Oracle Reports. (There is no integration between the two tools.) Seagate Software – Seagate Holos Core OLAP analysis is well supported via the Seagate Worksheet or custom application interfaces. The Worksheet is well suited to power users wanting to take full advantage of Holos’s advanced forecasting and modelling capabilities from a ‘no-nonsense’ spreadsheet-like interface. However, Holos is a tool for IS developers building applications for diverse user requirements. Holos supports report distribution via the Web or by integrating with reporting tools such as Seagate Info. Holos data can also be exported to Lotus Notes databases as a shared resource, for distribution to group working environments. Report subscription services are not provided. Building the business model

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BusinessObjects

Essbase

Oracle Express Development Tools

PowerPlay

Seagate Holos

DecisionSuite

Gentia Platform for Analytical Applications

DSS Product Suite

SQL Server 7.0 OLAP Services

Business Objects – BusinessObjects The process of building a business model is split between DBAs (who create the mapping layer) and end users (who create reports by querying the database using the mapping layer). Easy-to-use graphical tools are provided for both types of user. The design tools provide extensive wizard support, and DBAs can readily exploit existing database schemas. Multi-designer environments are also well supported by concurrency and versioning controls. Cognos – PowerPlay The strength of the tool lies in its ease-of-use in defining the structure of a multidimensional model and populating it with data. There is automatic support for defining the time dimension and quickly producing a prototype if the data source is appropriately structured. Using Impromptu to access SQL data prevents a sample of data being available to the designer. Gentia – Gentia Platform for Analytical Applications In the Gentia toolkit, the multidimensional business model is built in GentiaDB, but because of the lack of end-user tools to directly access it, will almost always be embedded in a Gentia application. The multidimensional business model in the MDDB can be accessed through its API, but the company claims this is not often requested or required. Although much of the specification is done using point-and-click, it is less easy-to-use than other MDDBs we have seen. Other areas that could be strengthened include support for more specialised calculated measures and metadata. Hyperion Solutions – Essbase Essbase provides a very intuitive interface for business modelling. The graphical representation of the dimensional hierarchy in the database outline view works very well, and other graphical tools are provided to make the definition of measures and data load rules easy. This allows users to build quite complex business models without the need for specialist DBA skills. Models can be shared to support multi-designer environments, though there is no direct support for versioning control. Information Advantage – DecisionSuite In DecisionSuite, the report is just one perspective on the business model. Much of the work is done beforehand when defining a business-oriented map of the underlying database table structure (metadata tables). This allows developers to build a logical business model to simplify end-user construction of reports. The business model is flexible, and the use of filters and calculations allow for considerable adaptability. A wizard-driven interface guides designers through the process of describing complex drilling hierarchies and aggregation table information. However, a diagrammatic editor would ease the task of setting up and managing the metadata tables. Microsoft – SQL Server 7.0 OLAP Services The distinguishing feature of building a multidimensional business model in OLAP Services is the extent to which the designer is assisted by wizards. While the product cannot be faulted on ease of use, it is prevented from getting a higher score in this criteria by its limited support for customising the dimensions and measures. Additionally, and possibly because the model so closely maps onto the columns in the data warehouse, there is no support to collect metadata about the model or its components. A further weakness, if the tool is to be used in a corporate setting, is the lack of support for multiple designers. Microstrategy – DSS Product Suite Using the Microstrategy toolkit, much of the work that results in the multidimensional model is done upstream when designing the tables in the data warehouse and defining the mapping layer in DSS Architect. This makes the building of models easy, but limits the flexibility. It is, for instance, not possible to introduce user-defined levels in the dimensions. Nor is there any tool support for defining the time dimension; it is limited to what is available in the data warehouse. One of the reasons for the low score on this criteria is the dependency on the data warehouse for part of the model-building process. Oracle – Oracle Express Development Suite The tool provides a flexible and powerful interface to define the business model, and prototypes can be quickly built using the database wizard. It offers a full range of features for building the multidimensional business model. The main ways in which the tool could be enhanced are through more structured support for the collection of metadata (to collect richer information) and by the provision of version control. Seagate – Seagate Holos In Holos, business models are built exclusively by IS or developers. These users are well supported by the Holos language and a number of graphical tools for defining structures, hierarchies and adding calculations to models. The combination of structures, models and rules provides tremendous flexibility for designing a business model, and quite complex business models can be built by overlaying different types of structures. But a single view of the various types of structures and dimensions available would ease the process considerably. Advanced analytical power

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Seagate Holos

Oracle Express Development Tools

Essbase

Gentia Platform for Analytical Applications

SQL Server 7.0 OLAP Services

PowerPlay

DecisionSuite

DSS Product Suite

BusinessObjects

Business Objects – BusinessObjects BusinessObjects is limited in its support for complex analytical functions. Generally, the range of native functions provided is geared towards general business analysis. Advanced functions such as statistical modelling and forecasting rely entirely on integration with third-party tools. A bonus is the integrated and easy-to-use data mining facilities provided by the BusinessMiner tool. Cognos – PowerPlay PowerPlay provides multidimensional support, but for more specialised analytics the user would have to make an additional purchase of Scenario (although this is included if the business intelligence suite is purchased) for data mining, and 4Thought for business modelling and forecasting. While PowerPlay is promoted by Cognos as an easy-to-use tool and has few in-built functions supporting specialised analytics, the tool includes a scripting language that enables users to extend the capabilities of the product. The score reflects the facilities within PowerPlay, but the text indicates how the other two products could significantly enhance this. Gentia – Gentia Platform for Analytical Applications Analytical functions can be built into the multidimensional business model when it is defined in the GentiaDB and others added when the model is used within an application created in the Gentia VDE. Within the MDDB and the application development environment a few analytical functions are provided. The company’s philosophy behind the product is that if complex analytics are required by users they could be created as re-usable components using the GDL. The toolkit would be enhanced by a greater range of ready-to-use analytical functions. Hyperion Solutions – Essbase With Essbase, analytical functionality is built into the server model during design. The main advantage of this approach is to ensure a consistent analytical environment. Essbase is quite limited in the range of pre-built analytical functions it provides, which affects the score in this section. Although Essbase does not provide any advanced ad hoc analysis functions and models, it does support write back for ‘what-if’ analysis and budgeting applications. Users that require more sophisticated functions must integrate with specialist third-party tools, or build their own calculations using calc scripts. Information Advantage – DecisionSuite DecisionSuite provides limited support for advanced analytics, although a number of specialised functions geared towards customer-centric analysis are provided. Calculation templates could feasibly be used to add more powerful analytical capabilities to the product, but these need to be built into the model during the design phase. Users that wish to apply statistical analysis and sophisticated forecasting algorithms directly to model data will need to use specialist tools. There is no Excel add-in facility. Microsoft – SQL Server 7.0 OLAP Services OLAP Services’ limited support for advanced analytical power emphasises that this is a tool for ease and speed of implementation and should not, by itself, be used for powerful analytical work. It is possible to extend the analytical functionality by incorporating COM components; this requires an additional sophisticated skill set. When writing applications that access data stored in OLAP Services, the user can use some multidimensional expression (MDX) functions. However, the functions available within the tool are those that reference a member rather than those that process sets. It thus provides very limited assistance in defining complex analytical functions using OLAP Services’ Calculated Member Builder. End-user tools accessing OLAP Services could make use of the MDXs mentioned below to provide users with some specialised analytical support. Microstrategy – DSS Product Suite There is support for the core analytical functions (such as ranking and sorting), but Microstrategy’s products have limited support for complex and specialised analytics over and above that provided by the data warehouse. The designer can use functions available in the data warehouse database, but there is no scope within the OLAP tool for extending the range of functions available. This is primarily a consequence of its ROLAP architecture, which is geared towards using specialised analytics created and stored in the data warehouse, rather than creating them within the OLAP tool. Oracle – Oracle Express Development Suite Support for advanced analytics is one of Oracle Express’s strengths. It has a rich collection of ready-to-use functions supporting financial, time series and forecast analysis. Users, with appropriate permissions, can change the property of cells and write values back so that values in the model can be recalculated. If required, additional functions can be written in the stored procedure language. The product would be enhanced by the addition of data mining functionality. Seagate – Seagate Holos Holos provides an extensible set of advanced analytical functions for statistical analysis, trend analysis and time series forecasting. Write-back is also supported for ‘what-if’ analysis. End users have the flexibility to apply a number of specialised pre-built functions to model data directly from a custom application or the worksheet interface. The Holos language also supports advanced data mining capabilities, though the integration of this technology requires considerable programming effort. Holos does not help analysts to interpret results of analyses. Web support

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BusinessObjects

DSS Product Suite

Oracle Express Development Tools

DecisionSuite

Gentia Platform for Analytical Applications

Essbase

PowerPlay

Seagate Holos

SQL Server 7.0 OLAP Services

Business Objects – BusinessObjects WebIntelligence II provides strong support for web access. It supports most of the OLAP query, analysis and reporting functions as its desktop counterpart, including the ability to create new models and define format report formats. WebIntelligence II also benefits from the distribution of processing across multiple servers for increased scalability and load balancing. The web tools share the same metadata layer and security as the client-server tools, allowing for integrated management. Cognos – PowerPlay Web support is provided by PowerPlay Server Web Edition, which is purchased separately from PowerPlay. It allows browser access to PowerCubes. The functionality provided by browser access to PowerCubes is getting closer to that offered via the desktop. However, it does not yet offer facilities to edit or create models via the browser. While there are facilities for individuals to initiate publication and distribution of models and reports, the product would be enhanced by additional features to support centrally organised distribution. Gentia – Gentia Platform for Analytical Applications Web access in Gentia is supported by Gentia WebSuite, which uses a CGI gateway between the usual web server and the Gentia server. It thus enables a web browser to access Gentia applications and base models. Applica- tions written especially for the Web can offer excellent functionality. (There is a web version of Gentia’s flagship product, the Renaissance Balanced Scorecard.) However, when accessing base models via the Web, the user has less functionality than with desktop client access. One very useful feature is that write back via the browser interface is supported. A current limitation of the system is that if applications are to be accessed via the Web, designers need to modify them in anticipation of this. There is little exploitation of the Internet as a distribution mechanism. Hyperion Solutions – Essbase The Web Gateway provides the standard interactive analysis and HTML web publishing capabilities expected from an HTML-based implementation. Wired for OLAP uses Java applets for a more interactive experience, including reporting and charting options. Both clients offer write-back capabilities from the web browser. However, Essbase’s web capabilities have not been designed for data modelling. Web access is restricted to registered users and there is little support for utilising the Internet for dynamic distribution of reports. There is no support for balancing processing across multiple web servers. Information Advantage – DecisionSuite WebOLAP provides strong web access for accessing and analysing predefined reports. However, web users cannot define new reports or add new filters or calculations to the report definitions. Reports can be easily published and distributed to a wide range of users over the Web using Internet-based search engines, hyperlinks and e-mail. Microsoft – SQL Server 7.0 OLAP Services OLAP Services, by itself, does not provide web support. As with several of the features considered in this evaluation, if they are required, they have to be added on by the use of an appropriate third-party and/or end-user tool. Some of the entry level tools offer web access using a COM component or Active Server Pages on IIS. This enables users to explore models using browser access, but there is no support for the creation of models, nor for using the Web and the Internet as distribution mechanisms. Microstrategy – DSS Product Suite Microstrategy offers good web support via two products, DSS Web and DSS Broadcaster. DSS Web has several modes of action; it provides a development environment, is a server enabling thin client creation and access of reports, and provides administrative control of web usage. DSS Broadcaster is designed, as its name suggests, to distribute reports to users. A unique feature is the ability to distribute these to a range of different devices simply using a pull-down menu. The only significant omission in this criteria is the inability to generate HTML pages directly from within DSS Agent when creating or running reports. Oracle – Oracle Express Development Suite The Oracle Express development tools enable users to access models using Web Agent (which is bundled with Express Server) and to publish pages using Web Publisher. As with most OLAP tools, there is support for web access but not for the creation of new models. Seagate – Seagate Holos Holos supports both HTML and Java-based web interfaces. The HTML implementation is far from elegant, but does provide web users with a simple and effective means of accessing and navigating through reports, albeit in a restricted manner. The Java-implementation provides a more flexible interface for OLAP analysis. The web tools are aimed at end users; there is no support for designing models or developing applications. Management

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BusinessObjects

PowerPlay

DecisionSuite

DSS Product Suite

Gentia Platform for Analytical Applications

Oracle Express Development Tools

Essbase

SQL Server 7.0 OLAP Services

Seagate Holos

Business Objects – BusinessObjects The BusinessObjects Supervisor provides excellent graphical management tools that ease the administration of reports, metadata and end users. The tool provides strong query governance mechanisms and supports a sophisticated security model to restrict user access to models and other application objects. But query monitoring is only available with WebIntelligence II and there is no support provided for tuning the BusinessObjects client cache. Cognos – PowerPlay There are two main editions of PowerPlay, (client- and server-based). In both editions, user and PowerCube securities can be defined. The management features of the server edition are designed to support a large number of users. The score here reflects the good support given to the management of users. In version 6, the features for performance tunability of the data have been substantially enhanced, but to fully exploit these the administrator has to manually control the partitioning features. Gentia – Gentia Platform for Analytical Applications There is scope for confusion in this section as there are two locations in which data and users have to be managed: the GentiaDB and applications developed in the Visual Development Environment (VDE). The main focus here is on the management facilities within the application development environment as this has a wider scope. In the VDE there is a good range of management facilities for controlling access rights. Through the use of Agents the management of large systems can be largely automated. Hyperion Solutions – Essbase Essbase provides facilities for deploying multidimensional databases to end users. It provides strong security facilities for both models and users, and the tool’s sparse data handling capabilities and intelligent calculation options facilitate efficient data storage and retrieval. But data loading schedules are only supported through a scripting language; there is no point-and- click support. Event-driven scheduling is not supported. Essbase does not provide facilities for query monitoring or governance – though this is less of an issue for optimised MDDBs. Information Advantage – DecisionSuite All DecisionSuite application, metadata and user management is defined and maintained on the server through graphical interfaces. The security of reports relies entirely on the Unix and RDBMS security. Agents are used for scheduling report updates and can be based on times and events in the data warehouse. As expected from a ROLAP tool, DecisionSuite provides strong support for query monitoring and governance, and produces detailed usage statistics. Microsoft – SQL Server 7.0 OLAP Services OLAP Services has good support for managing size and partitioning the data, but poor support for scheduling uploads and providing user security and controls. OLAP Services provides a published API, Decision Support Objects (DSO), to control the management aspects of the tool. This is used, for instance, by the cube-building wizards. Microsoft has not yet produced easy-to-use functionality to support the scheduling of loads and updates, so users have to either do this manually or write their own applications using the DSO. The most obvious enhancement needed in this area is the provision of wizard support for the management of data. Microstrategy – DSS Product Suite Management of models and users is supported in several of the components of the toolkit and is easy to use. The toolkit focuses on query management and there is good support for monitoring the usage of the system. The score is reduced because some features, such as user security, are largely delegated to the data warehouse and not provided by the toolset. In ROLAP, persistent models are a pragmatic convenience rather than a fundamental aspect of the system. However, they still require management and the product would be enhanced by more information on failed refresh schedules. Oracle – Oracle Express Development Suite The Oracle Express development tools offer developers a large number of options and configurations, and managing these is unlikely to be a simple task. While support for most of the important management functions is available, it assumes a DBA mind and skill set. Some tasks (for example, defining securities) require the administrator to write stored procedures in the Express language, rather than doing most of the work in a GUI environment with the occasional need to script. Thus most of the expected features are available, but the task of the administrator could be eased with wizard support and more intuitive interfaces. Seagate – Seagate Holos Holos lacks a separate graphical management console for administering Holos models, data and end users. A command-line interface is provided to define scheduling and user security. The security of models relies heavily on the underlying operating system or database, though stricter access can be programmed. It will be up to developers to define and maintain these controls. Adaptability

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Oracle Express Development Tools

SQL Server 7.0 OLAP Services

Essbase

Seagate Holos

BusinessObjects

DecisionSuite

Gentia Platform for Analytical Applications

PowerPlay

DSS Product Suite

Business Objects – BusinessObjects In BusinessObjects, adaptability is generally a case of incorporating new members automatically and being able to modify the mapping layer to meet changing business requirements. All of this is well supported. Access upstream metadata from data warehousing tools can be used to synchronise the mapping layer with data sources. However, there are no facilities to inform end users of the updates or impact analysis for existing models. Cognos – PowerPlay In a client-based configuration, with minimum metadata, adaptability is generally a case of incorporating new members automatically and being able to modify the model to meet changing business requirements. All of this is well supported. Adaptability is more of an issue in a large-scale environment, where it is likely that a server-centric model would be used. Adaptability could be extended to support some of the features described below by the use of MDL, a fourth generation language, and C shell scripts. There is thus power and flexibility, but a requirement for additional skills. Gentia – Gentia Platform for Analytical Applications It is easy to adapt the application in the light of changing business requirements. It is possible, using Agents, to provide very sophisticated support to ensure that the data sources, models and applications using these are synchronised. This does, however, require some development effort. Gentia does not have ‘out-of-the-box’ adaptability, but it does have the tools to develop powerful mechanisms to support large installations. The tool does not provide support for impact analysis and change management. Hyperion Solutions – Essbase Essbase models can adapt to change, but there is limited support for the management of change. Users can take advantage of the drag-and-drop method for adding new dimensions and measures in models. Changes in underlying data sources can be automatically uploaded to the multidimensional database as part of a standard batch update process. But there are no facilities for ensuring that metadata remains synchronised with changes to models and/or data sources. Essbase does not provide any facilities for impact analysis and there is no integration with upstream metadata. Information Advantage – DecisionSuite DecisionSuite’s metadata layer allows for an adaptable business model. New dimensions and measures can easily be defined and re-used across models. All additions are automatically time-stamped. Model metadata can be referenced to ensure that reports are kept synchronised at all times, but there are no facilities for keeping data sources and models in line. There is no possibility to change the architecture from ROLAP to MOLAP mode. Microsoft – SQL Server 7.0 OLAP Services The most notable strength of the tool with regard to adaptability is the ease of changing the storage architecture. It is also easy to add dimensions and measures. There is effectively no metadata to synchronise, which – although it is a limitation in other areas – does at least make implementing the changes straightforward. The tool is prevented from getting a higher score by the lack of support to track and predict the impact of changes. Microstrategy – DSS Product Suite There is support to add new dimensions and measures to a model and re-use these definitions. The low score reflects the lack of facilities for keeping the data sources, multidimensional business models and the metadata all synchronised. There is also no possibility of adapting the architecture should the business needs suggest this. Oracle – Oracle Express Development Suite The most useful feature in Oracle Express supporting adaptability is the ability to change the data storage architecture. The system can be configured so that all data is held in the multidimensional database or, using Express Relational Access Administrator, data stored in a relational database can be used in the model. Finally, the system can be configured using Express Administrator, so that some (usually the summary) data is held in the MDDB and the rest is retrieved using SQL commands (HOLAP). Although the toolset offers a great deal of flexibility, it does not offer wizard support and requires a competent DBA to manage it. Adding new dimensions and measures is straightforward, but the tool lacks facilities to re-use and track these changes. The simplicity of the metadata, while in other contexts a negative feature, does simplify the process of adapting the model. Seagate – Seagate Holos Within Holos, it is easy to adapt a model to support changing business requirements; all model design operations produce Holos language scripts that can be stored and re-used. Holos can also incorporate different types of structures in a model and easily adapt from ROLAP to MOLAP modes and vice versa. However, there is no direct support to ensure that data sources are automatically synchronised with Holos structures and applications. Nor does Holos provide any support for impact analysis. Performance tunability

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Seagate Holos

Essbase

Gentia Platform for Analytical Applications

Oracle Express Development Tools

PowerPlay

DecisionSuite

SQL Server 7.0 OLAP Services

BusinessObjects

DSS Product Suite

Business Objects – BusinessObjects BusinessObjects’ desktop architecture has potential restrictions on performance and scalability. To overcome the size and performance issues DBAs can tune BusinessObjects to exploit native SQL access, multipass SQL and aggregation tables. Special third-party technology can also be licensed to handle large and complex data sets. However, performance would be enhanced by a more scalable, server-based OLAP engine. Cognos – PowerPlay The original design of the PowerCube gave little scope for performance tunability, but this has changed in recent versions. The combination of breaking the model into separate cubes that are linked so users can drill through from one to another, and partitioning, give significant tuning options. Partitioning gives the administrator the option of trading off build time against execution time. The incremental update enables new data to be appended to the PowerCube rather than recreating the whole model. In the commonly used client-centric configuration, the PowerCube is loaded down to the desktop; the speed of the local processing is a function of the desktop hardware and the settings for the in-memory cache. Gentia – Gentia Platform for Analytical Applications The distributed client-server architecture of the Visual Development Envi- ronment offers excellent support for flexibly allocating processing. Within GentiaDB, performance tunability is largely dependent upon good design decisions, and the tool would be enhanced by some wizard support for this process. Hyperion Solutions – Essbase One of the strengths of Essbase is its fast multidimensional database engine. Therefore, query performance is taken for granted. For data loading, performance can be tuned by specifying incremental data loads and dynamic calculations at runtime. Processing times can also be enhanced using a combination of the partitioning capabilities and SMP. However, there is no native SQL access to RDBMSs, which affects the score in this section. Information Advantage – DecisionSuite DecisionSuite utilises the strengths of relational database technology, while ensuring that processing is optimised between the DecisionSuite Server and the database. It also provides a number of performance-tuning services aimed primarily at minimising access times, such as multipass SQL, native SQL access and SMP parallelism. Microsoft – SQL Server 7.0 OLAP Services The administrator has most scope for performance tuning when the tool is used in MOLAP mode. The most useful feature is the visualisation of the relationship between database size and performance. In ROLAP and HOLAP mode there is limited scope for performance tuning. Microstrategy – DSS Product Suite In a ROLAP model such as Microstrategy’s, the main performance issues are how to minimise access time, particularly with complex queries. This is achieved by providing options to cache data and using multipass SQL, enabling queries of greater complexity to be handled. The Microstrategy approach is for all the processing to be done on the database server so that the minimum data is retrieved which reduces the network load, although the load on the database server is increased. The product is prevented from getting a higher score in this dimension by its inability to tune for speed of access through the pre-calculation of aggregates (a consequence of its ‘ROLAP-only’ architecture), distribute the processing of the SQL queries and the lack of support for load balancing. Oracle – Oracle Express Development Suite Express Server can operate in both MOLAP and ROLAP mode, so it could potentially be finely tuned for both approaches. As expected, its tunability strengths are as a MOLAP engine. The appropriate design of multi-cubes can enhance performance, but there is no automatic support for this. The tool supports SMP. One weakness of the tool, when used in ROLAP mode, is that the users have no direct way of knowing how long the data they are viewing has been cached. Seagate – Seagate Holos Holos provides strong tunabaility features for both MOLAP and ROLAP operation. For ROLAP mode, Holos supports the generation of multipass SQL and native SQL access to all the major relational databases. For MOLAP configurations, multidimensional structures can be loaded incrementally. The loading and precalucation of data can also be distributed across multiple processors simultaneously using SMP technology. Customisation

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Gentia Platform for Analytical Applications

Oracle Express Development Tools

Seagate Holos

DSS Product Suite

Essbase

DecisionSuite

PowerPlay

BusinessObjects

SQL Server 7.0 OLAP Services

Business Objects – BusinessObjects BusinessObjects is positioned as a ‘ready-to-use’ tool for end users – the client modules do not require any customisation or adaptation. Support for specialised application development is limited to a procedural Visual Basic- like scripting language, for customising aspects of the tool’s interface and behaviour, or links to Windows development tools via OLE automation. Cognos – PowerPlay PowerPlay is designed as an ‘out-of-the-box’ toolkit and thus has few features for building standalone executable applications with multidimensional features. PowerPlay supports OLE automation and thus applications that make use of PowerPlay components can be developed in third-party languages. The usual division of responsibilities is that IT-based staff create and maintain the multidimensional models from which PowerCubes are generated, and business operators use these to produce customised interactive reports. These reports can be considered as applications, but they do not disguise their PowerPlay genesis. Gentia – Gentia Platform for Analytical Applications Gentia is primarily a platform for analytical application development and offers a comprehensive range of services to support this. It enables specialised OLAP applications to be quickly developed in an easy-to-use GUI environment. As a result of its distributed architecture, the applications can run on both Unix and Windows using data stored on a variety of platforms. While it is possible for the developer to produce sophisticated applications for the Web, the development environment is still more geared to the production of applications for desktop access. It is the responsibility of the developer to note the differences between developing for the desktop and the Web. The toolkit would be enhanced by more direct support for the development of web applications. Hyperion Solutions – Essbase Application development is provided by a set of Active X controls and via Essbase’s published API. The controls can be assembled to build custom EIS- type applications. They can also be integrated with third-party development tools. The API is functionally rich, and is extensively used by third-party tool vendors and VARs to integrate with Essbase. Information Advantage – DecisionSuite DecisionSuite provides limited support for application development. Add-ins and a procedural scripting language are available to customise applications and reports. Application development relies on the tool’s API, and using external development tools that can use the same DLL that links the DecisionSuite client modules to the server. Microsoft – SQL Server 7.0 OLAP Services The low score in this criteria reflects the absence of support provided by the tool to develop customised interactive applications. However, if viewed as a component within an application, then the ubiquity of the API and the low cost makes it attractive to developers. It cannot be used to customise, but is itself customisable. OLAP Services is a component, with an open API, that can be used within a customised application. The development of the application can be carried out in any COM-compliant environment such as Visual Basic or C++, but OLAP Services itself does not provide an environment for developing these. Microstrategy – DSS Product Suite The toolkit provides three development environments: • an API to support application development in OLE-enabled application development languages such as Visual Basic, Visual C++, VBA and Delphi • development for the Web using DSS Web • the production of EIS reports using DSS Executive. None of these environments provide ‘OLAP-aware’ components (such as cross-tab and graphical objects), which limits the type of application that can be easily produced. Oracle – Oracle Express Development Suite Oracle Express and its associated products provide excellent support for application development. Reports with multidimensional features can be developed using Oracle Express Analyzer’s visual development environment. Using Oracle Express Objects, fully featured applications can be developed combining ease of use with the power of a procedural language. Finally, Web Publisher enables ‘browser aware’ applications to be created. Seagate – Seagate Holos Holos is a powerful 4GL application development tool. The Holos language is purpose-designed for building business intelligence applications, and is supported by an integrated and easy-to-use set of graphical development tools. The range of development options should satisfy most development needs, from simple EIS reporting systems to advanced analytical applications. Applix TM1

Summary

At a glance ...... 2 Terminology of the vendor ...... 3 Ovum’s verdict...... 4 Product overview ...... 6 Future enhancements ...... 14

Commercial background

Company background ...... 15 Distribution ...... 16

Product evaluation

End-user functionality ...... 16 Building the business model...... 20 Advanced analytical power ...... 21 Web support ...... 23 Management ...... 23 Adaptability ...... 25 Performance tunability...... 26 Customisation ...... 27

Deployment

Platforms ...... 29 Data access ...... 29 Standards ...... 29 Published benchmarks ...... 29 Price structure ...... 29 Evaluation: Applix – Applix TM1 Ovum Evaluates: OLAP

At a glance Developer Applix, Westboro, Massachusetts, USA Versions evaluated Applix TM1 version 7.1 Key facts • A MDDB that runs in memory and works with standard spreadsheets or OLE DB for OLAP clients • Server runs on Windows 95, Windows 98, Windows NT and Unix; clients run on Windows 3.1, Windows 95, Windows 98 and Windows NT • TM1 was the first OLAP vendor to support Microsoft’s OLE DB for OLAP as a data provider Strengths • Extremely quick response times provide fast OLAP analysis for small datasets • No time-consuming precalculation or batch loading of the MDDB • TM1 architecture is well suited for remote and mobile computing Points to watch • Does not yet support its own web client – web access relies on third-party tools • Performance can be adversely affected by large concurrent user loads when accessing models with high volumes of data and complex OLAP calculations • Simple modelling tools that are best suited to small data volumes Ratings

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End user functionality

Building the business model

Advanced analytical power

Web support

Management

Adaptability

Performance tunability

Customisation

Terminology of the vendor Cube This is constructed from two or more multidimensional structures (dimensions). A cube is defined by combining a series of dimensions into a matrix of cells, where each cell is a unique intersection of dimension elements. In TM1, a collection of cubes forms a multidimensional database. Database This contains one or more multidimensional cubes and associated dimensions, security assignments and other metadata. Data dictionary A set of Windows folders that contains TM1 cube, dimension, rules and other system information that gets loaded into memory when TM1 runs. Data point A unique data value held in a cell. Data points can be sourced from a RDBMS, a flat file, by a manual input, or they can be derived from a rule. Dimensions Dimensions define the structure of cubes. They are simply used to organise how data is stored in cubes and to generate consolidations. Each dimension contains a hierarchy of elements. Elements These are individual items within each dimension. The intersection of element values for two or more dimensions identifies the location of a cell in a cube. In TM1, there are two types of elements: base-level elements (such as sales and receivables) or calculated elements (such as net income or gross margin). Level This refers to the position of an element in the dimension hierarchy and is used to specify what data is requested in a query. Base-level elements contain input data only. Higher level elements are aggregations of lower level elements. Real-time OLAP An Applix term used to describe a multidimensional database that runs in memory and performs calculations on demand, rather than according to precalculation. Rules Rules are formulas that perform specific calculations on data. Rules calculate values for cells in the cube when they are requested by a query or are required to create values for a query; the values are stored (temporarily) in memory.

Ovum’s verdict

What we think TM1 takes to heart the concept of OLAP as ‘a spreadsheet on steroids’. In this respect, it will be most appreciated by financial users wishing to combine the flexible display and ad hoc calculations of spreadsheets with the performance of a multidimensional database. TM1 scores modestly according to our evaluation criteria, but in some specialised applications it has definite strengths. Its best feature is its memory-resident OLAP engine, which is unique on the OLAP market and provides exceptional performance. TM1 multidimensional databases can be put in memory and calculated quickly in real time. TM1 only stores the lowest level of model data in the OLAP engine and calculates aggregations on demand, so it avoids the ‘data explosion’ problems associated with MDDBs. It also removes the need for batch recalculations each time fresh data is uploaded. TM1’s small data footprint is ideal for mobile computing environments and strong data replication and synchronisation capabilities are provided for disconnected analysis. TM1 performs best as a ‘local’ desktop OLAP solution; its exclusive calculation-on-demand approach is optimised for small data volumes and small numbers of users. Query performance can be affected by large numbers of concurrent read-write users working with models that contain high volumes of data with deep hierarchies and complex calculations. However, new features in version 7.1 make the product more credible as a scalable server product. The spreadsheet interface provides a simple and flexible OLAP front end, but TM1 would benefit from more robust modelling tools. TM1 exploits the OLE DB for OLAP interface to provide web access and advanced analysis and reporting functions through third-party tools. Hence, the level of functionality is dependent on the front-end tool used. There is little support for developing custom analytical applications, although TM1 can easily be embedded into third-party systems. Overall, TM1 is a mature OLAP tool, with many reference sites, but it has suffered from poor marketing in recent years. Applix will need to shout loud and clear to get its ‘real- time OLAP’ message across in an increasingly competitive OLAP server market.

When to use TM1 is suitable if you: • want to support financial planning, budgeting and forecasting applications that require relatively small data volumes • want to build large business models of five of more dimensions, where traditional MDDBs would ‘explode’ the data • have dynamic applications that require frequent input of data, recalculation, or analysis of ‘what-if’ scenarios • have existing spreadsheet skills you want to exploit • want to support mobile users for offline analysis.

It is less suitable if you want: • to develop ‘ROLAP-style’ applications running against large volumes of data • to rollout OLAP to large numbers of casual business users with simple OLAP needs – TM1 is primarily aimed at analysts • integrated development tools for building highly customised analytical applications.

Product overview

Components TM1 version 7.1 consists of the following components: • TM1 Server • TM1 Perspectives • TM1 Client • TM1 Architect • TM1 Data Control • TM1 Process Objects • TM1 API. Figure 1 shows the primary functions of the components and how they relate to client-server systems. TM1 Server A multidimensional database and OLAP engine that stores and provides access to multidimensional models (cubes) managed in local or remote TM1 Servers. TM1 Server works with memory-based cubes and its most significant feature is its memory-resident calculation engine; all OLAP consolidations and calculations are performed on-the-fly, rather than working with precalculated cubes stored on disk. The engine can be run as a multi-user remote server, or from within the TM1 Perspectives and TM1 Architect clients in local, ‘standalone’ mode. TM1 Perspectives A 32-bit client interface, for spreadsheet users, to the TM1 Server. TM1 Perspectives is provided as an add-on to Microsoft Excel and Lotus 1-2-3 spreadsheets. It has three main components: • TM1 multidimensional database engine, which stores cubes in memory and performs OLAP calculations on demand

Figure 1 Component functions

Data loading OLAP analysis Administration Customisation

Client TM1 Data Control TM1 Perspectives TM1 Architect TM1 Client

Server TM1 Process Objects TM1 Server TM1 API

• Server Explorer, which provides DBA-like tools for creating dimensions and cubes and administering TM1 Server, data replication processes and end-user security. It includes a data acquisition module for creating dimensions, cubes and loading data • Spreadsheet Integration, which adds a TM1 menu item to the Excel and Lotus 1-2-3 programs for analysing cubes. It provides dialogues for filtering data and performing OLAP analysis. It also supports an alternative worksheet-oriented environment for developing cubes from flat files. TM1 Perspectives can run against a multi-user remote TM1 Server, or in ‘standalone’ mode against a local TM1 Server. TM1 Client TM1 Client is an independent version of the Spreadsheet Integration component found in TM1 Perspectives. The TM1 Client is an exclusive interface for spreadsheet users that want to access predefined TM1 cubes and does not provide the development or administration capabilities of TM1 Perspectives. TM1 Client can also run against a multi-user remote TM1 Server, or in a ‘standalone’ mode against a local TM1 Server. TM1 Classic is a 16-bit version that integrates with Excel 5 and Lotus 5 spreadsheets, but does not support a local TM1 Server engine. TM1 Architect TM1’s standalone development, deployment and administration tool. TM1 Architect provides the same functionality as TM1 Perspectives, with the exception of Spreadsheet Integration. It supports the same Server Explorer GUI, including a functional Cube Viewer and Dimension Subset dialogues that are designed to aid development. TM1 Architect is also used to manage applications deployed through third-party clients (OLE DB for OLAP and Java). TM1 Data Control An optional front-end tool for automating and scheduling TM1 back-end processes for building and maintaining dimensional hierarchies, creating cubes and importing data. Data Control combines TM1 Server’s OLAP engine and ODBC access to source data and is hosted by the spreadsheet. Spreadsheet users can define and execute ODBC queries that load or update data into TM1, create rudimentary transformation processes to map source data to TM1 cubes, and also export data from TM1. Individual processing tasks can be grouped together in a single Data Control ‘job’ and run on demand or at a scheduled time. Data Control was developed by Revelwood, a New York-based consulting firm that specialises in TM1 application development. The product is written in Visual Basic, and is marketed and resold exclusively by Applix.

Process Objects Version 7.1 provides new server-based Process Objects. These objects eclipse the client-based data import and update facilities by providing server-side capabilities for handling complex event-driven scheduling. Process Objects can be linked to the TM1 rules language for conditional trigger of back-end TM1 Server processes, such as mapping, transformations and creating and updating TM1 cubes and dimension hierarchies. TM1 API The TM1 API is the element that allows native TM1 clients or third-party applications to interact with the TM1 Server. TM1 supports four APIs: • API 7.0, which provides public documented access to TM1’s own language, and all the calls necessary to develop, manage and use TM1 applications. It is available in C++ and Visual Basic • JavaBean API, which allows third-parties to develop Java-based OLAP applications. The TM1 Java API contains all the functionality of the C++ API • OLE DB for OLAP as a data provider. This opens TM1 for access from any OLE DB for OLAP consumer tool • API 1.5, which translates applications written to TM1 Server 6.0’s 1.5 API specification into the appropriate 7.0 API call.

Architectural options Full mid-tier architecture This is the ‘natural’ architecture for TM1, consisting of TM1 Servers and clients running TM1 Perspectives, TM1 Architect and/or TM1 Client. The TM1 Server loads data in memory on the mid-tier server and services requests for data from TM1 Client programs. If the client is an OLE DB for OLAP consumer, it uses a MDX parser facility to interpret requests and return data. A web server can be introduced to provide access to the TM1 Server using supported OLE DB for OLAP clients that offer web access. The OLAP engine and MDDB store run in memory on a mid-tier server, where all OLAP calculations are carried out. Cubes can be stored persistently on disk, in either a proprietary disk-based structure or any ODBC- compliant database. This provides the option of storing cubes in relational tables and working with these tables through relational query tools. Depending on the configuration, TM1 clients may have exclusive access to a single local TM1 Server, which acts as a repository for their private data, or shared access to one or more remote TM1 Servers; the level of access depends on the security group assigned by administrators. TM1 supports multiple cubes that can be distributed across several servers. Data can also be replicated from/to another remote satellite server, and updates can be synchronised back to the source server.

Light mid-tier architecture A ‘light’ mid-tier architecture is not supported. Desktop and mobile architectures TM1 Perspectives supports a self-contained OLAP engine that can run as a separate thread or as an in-process server on a laptop computer. Asynchro- nous replication of data from a remote TM1 Server into the local OLAP engine allows users to perform complex analyses while detached from the mid-tier server. Updates made locally can also be synchronised back to the remote server.

Using TM1 Real-time analytical processing TM1’s most distinctive feature is its support for ‘real-time analytical processing’ (RAP). This is made possible by its RAM-based OLAP engine (for which Applix has a patent), which loads and runs the MDDB entirely in memory. The MDDB can be loaded into memory because all derived values in TM1 are calculated on-demand. This avoids both ‘database explosion’ and the lengthy pre/recalculation times when loading or updating the database. The downside is that the derived values take time to calculate – generally, the time taken is a function of the complexity of the calculations and the depth of the dimensional hierarchies in the model. Additionally, TM1 uses a compression algorithm to minimise the use of memory. Generally, the source database stores one number per record that will be input into the MDDB and requires up to 50–100 bytes per record. TM1 on the other hand stores one number (plus indices) in approximately 12 bytes. Hence, a TM1 multidimensional database is typically 10–25% the size of the data source. Performance, in terms of response time, is also enhanced by retaining calculation results in memory (as long as they are still valid) to support future requests. This prevents the same calculation from being repeatedly executed for a cell and can greatly increase performance. TM1 flags calculations as invalid when values in the cube are modified. The next time a value is requested, a fresh calculation is performed. The anatomy of a TM1 model It is easier to understand the TM1 approach to OLAP if key concepts underlying a TM1 model are clarified first. In a TM1 model, all data is stored on the server as cubes. A cube represents a combination of two or more multidimensional structures, and a collection of cubes forms a TM1 multidimensional database. Dimensions are the raw building blocks of TM1 cubes and are used to define the hierarchically structured axes of cubes. Dimensions contain no data and are stored independently of cubes. Dimensions consist of one or more elements that may be arranged in a sub-hierarchy. There are two main types of elements: base- level elements, which represent the lowest level of input data in a model, and calculated values, which are derived from formulas called rules.

Building a TM1 model TM1 supports two different approaches for creating dimensional hierarchies and cubes, mapping them to source data and loading in data. Spreadsheet-based approach The first approach uses a spreadsheet-based development interface and is supported by TM1 Perspectives. It is restricted to importing data in comma- delimited flat files (in ASCII format). The process of creating dimensions and cubes is driven directly from the spreadsheet through a series of special worksheets. These worksheets look similar to standard Microsoft Excel and Lotus 1-2-3 spreadsheets, except that they contain special TM1 functions for defining dimension structures and are linked to TM1 multidimensional databases. Dimension worksheets are used to layout dimension structures using standard spreadsheet columns. Cubes can then be created by selecting appropriate dimensions. Wizards are available to automate both the dimension- and cube-building processes. Calculations and inter-cube rules can be defined in a rules worksheet. TM1 supports a non-procedural language for evaluating rules. When a cube is created, it is still an empty matrix and data can be entered either manually from a Cube Viewer worksheet, or by creating a processing worksheet that reads records into spreadsheet rows and inputs field values into separate cells. ‘Database send’ formulas in the process worksheet map the cell values to an appropriate address in the TM1 cube. The processing worksheet can also be used for the transformation of the data because it is loaded into the cube. TM1’s Data Control facility can be used with the process worksheet to add a layer of automation and scheduling to the whole process; it can also be used to specify relational tables as data sources. Data Loader The second approach uses a dialogue-driven interface and is supported by TM1 Architect. It provides a more graphical method of defining dimensions and cubes and loading data directly from relational database tables or flat files. The process is supported by the following dialogues: • Dimension Editor, for manually defining small dimensional hierarchies. This approach assumes some knowledge of the structure of the source data • Dimension Loader, for importing dimension and element names from delimited flat files and RDBMS tables. It is used to identify one or more input columns that supply elements for a single dimension. It represents a faster approach for building a long list of elements • Data Loader, for creating dimensions during the process that imports data into a new, or updates an existing, cube. A dialogue box is provided to allow end users to selectively map dimensions to input columns and identify the value to be stored. However, input rows must contain element names that exactly match the spelling of elements in the cube’s dimensions; simple and complex transformations are not supported.

The new Process Objects in version 7.1 remove much of the manual effort, by adding a layer of automation and management for the data integration and loading process.

Spreadsheet-based analysis TM1 Perspectives and TM1 Client are both designed to exploit existing spreadsheet skills. TM1 is tightly integrated with standard Microsoft Excel and Lotus 1-2-3 spreadsheets via special add-ons; TM1 commands are available from a single pull-down menu from within the spreadsheet. Two main navigational and analysis tools are provided: the Cube Viewer dialogue and the Dimension dialogue. Cube Viewer The Cube Viewer dialogue, shown in Figure 2, allows end users to navigate through TM1 models from the worksheet.

Figure 2 Cube Viewer

The Cube Viewer facility represents the structure of the cube and shows the dimensions that make up the model. Each dimension is presented as a button; the arrangement of buttons determines a particular ‘perspective’ of model data that can be sliced into the spreadsheet. Version 7.1 also supports OLAP functions from an OLE object directly embedded in the spreadsheet. Dimension dialogue Double-clicking on any dimension in the Cube Viewer brings up the Dimen- sion dialogue shown in Figure 3, which allows end users to refine the subset of what appears in the Cube Viewer by selecting and filtering Dimension’s members. The advanced settings in the Dimension dialogue provide access to Dimension edit and query functions, and OLAP functions such as drill- down and roll-up and query data in the cube. By using the advanced browser features in TM1, a subset of the data can be defined that represents a useful ‘perspective’ of the data you might want to use in the future. This perspective can be saved as either: • a worksheet, called a ‘slice worksheet’ • a subset of the model data, called a ‘view’. A view provides slice-and-dice capabilities, but does not support charting or spreadsheet formatting options. Slice worksheets are similar to standard worksheets: end users can format them and add rows, columns or new formulas. Although the slice worksheet loses its pivot capability, it does allow users to place any cell from any cube within the worksheet. Slice worksheets remain linked to the TM1 Server. Therefore, if a number changes in the multidimensional database, that change is reflected in the worksheet when it is recalculated. Similarly, if the worksheet user changes a value, it is also changed in the corresponding cell in the TM1 database, provided they have the correct access privileges.

Figure 3 Dimension

Future enhancements Version 7.2 of TM1 is expected in the summer of 1999. It will contain two major enhancements: • scenario cubes, which support the creation of cubes that are ‘variants’ (scenarios) of other cubes. The scenario cube overlays the source cube and include changes made to the scenario cube by the cube user. This will allow users to make changes to the cube without affecting other users. Any changes can subsequently be committed into the source cube if desired. The same approach can be used to import large sets of data that are held in ‘suspense’ and incorporated on demand • dynamic subsets, which allow dimension subsets to be defined by an expression, rather than as a list of members. The subsets are dynamic and automatically synchronise with changes to its underlying data or metadata. A subset editor will be provided to define expressions and store them as objects in the server that get re-evaluated when data/metadata changes. As part of its web strategy, Applix is working with established partners to develop web-based analytical applications. Integration with Revelwood’s SmartSite development environment will be available in the second quarter of 1999. This will include integration with Microsoft FrontPage for web page development. The development of a Java version of Architect is under review.

Commercial background

Company background History and commercial Applix is a US company founded in 1983 to develop and market software applications for the Unix market. In 1986, it introduced Alis, its first office automation product. Alis was replaced by the Aster*x product, which provided the technology for Applixware, a suite of real-time decision support tools. In 1995, Applix acquired Target Systems, a developer of customer interaction software, and two major business lines were subsequently formed: Decision Support Systems (DSS) and Customer Interaction Software (CIS). All Applix’s products are based on the concept of real-time decision support: • Applixware, an integrated family of desktop and development tools for real-time decision support applications • TM1, a multidimensional OLAP server that runs in memory • Applix Enterprise, a suite of customer-interaction management systems including Applix Service (for customer support), Applix HelpDesk (for internal IT support) and Applix Sales (for sales and marketing support) • Applix Anyware, software that exploits Java and thin-client computing for deploying Applixware and Enterprise applications over the Internet. The original developer of TM1 was TM1 Software (founded in 1984 as Sinper) – a privately-owned venture specialising in OLAP products. Applix acquired TM1 Software in 1996 for $11 million. TM1 Software is now an operating unit of Applix based in Warren, New Jersey, US. TM1 was first released in 1984 as a single-user, DOS-based multidimensional engine. The product was completely re-architected for client-server systems in 1989. Applix is a public company quoted on Nasdaq. Revenues for 1997 dropped 5% to $48.5 million, and the company recorded a net loss of $0.4 million. Applix employs more than 300 people and is based in Westboro, Massachu- setts, US, with seven regional offices in North America and subsidiaries in Europe and Asia-Pacific. Character and direction TM1 has been available since 1984 and has amassed more than 3,000 customers spread evenly throughout the US and Europe. Therefore, it is sur- prising that TM1 has failed to gain any significant mindshare in the OLAP market. Two reasons are that TM1 Software was historically an engineering- led company, and TM1 was primarily seen as a desktop OLAP solution. However, Applix has boosted the marketing of version 7.1 of TM1 and is now starting to capitalise on ‘real-time OLAP’, an Applix term used to describe a MDDB that runs in memory and performs all calculations on demand. Its TM1 business is growing rapidly; Applix claims that TM1-related revenues more than doubled in 1997, fuelled by a number of enterprise deployments.

TM1 is sold directly and via channel partners, including systems integrators, ISVs, consultants, OEMs and more than 100 VARs. Historically, around 95% of sales were through partners. TM1 has a particularly strong presence in markets such as banking and telecommunications. Applix has bundling deals with a number of OLAP vendors. However, a long- standing licensing agreement with Hyperion Software has ended after Hyperion’s merger with Arbor Software in September 1998 – Essbase will now be the server of choice for Hyperion’s analytical applications. Other major TM1 partners include Comshare, Information Advantage, IBI, Platinum Technology and JBA.

Customer support Support Applix can provide around-the-clock worldwide support for TM1, via telephone, e-mail, fax and the Web. The company sponsors local, national and international user groups. Training Applix offers public training courses held at local sites or at its headquarters in Westboro, Massachusetts, US. Onsite training is also available. Consultancy services Applix offers performance tuning and site-specific implementation services for TM1. However, most implementations are done by partners. Consulting operations are based in the US, France, Germany and the UK, as well as those supplied by partners worldwide.

Distribution US Applix 112 Turnpike Road Westboro, MA 01581 USA Tel: +1 508 870 0300 Fax: +1 508 366 0995 Europe Applix (UK) 114 Middlesex Street London E1 7HY UK Tel: +44 171 426 0915 Fax: +44 171 426 0916

Asia-Pacific Applix 9 Raffles Place #27-01 Republic Plaza Singapore 048619 Tel: +65 435 0490 Fax: +65 536 4315 E-mail: [email protected]

http://www.applix.com

Product evaluation

End-user functionality Summary

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TM1 uses either Microsoft Excel or Lotus 1-2-3 as a front end. It will therefore appeal to experienced spreadsheet users, since it allows for the easy browsing of models in spreadsheets that can also become reports. OLAP functions are set up via a menu-driven interface and not directly from the spreadsheet. The spreadsheet lacks the flexibility to support large distributed environments and does not support advanced OLAP reporting and data visualisation functions provided by other dedicated OLAP front ends. How- ever, integration with a wide range of OLE DB for OLAP tools compensates for this. Finding and understanding the model Finding and loading a multidimensional model Models are accessed using the graphical Server Explorer interface. Models can be grouped into standard hierarchical directories. Related model files, views and subsets are grouped into a set of Windows folders called data dictionaries. However, there are no search facilities. Metadata for end users Most of the metadata provided by TM1 is structural and relates to dimension names, member categories and their co-ordinates in the model. Annotation by end user Models cannot be annotated by the end user. However, once data has been loaded into worksheets, footnotes and explanations can be added using standard spreadsheet functions. Using the model Basic OLAP functionality Standard OLAP functions such as drill-down and slice-and-dice are graphically supported via the Cube Viewer and Dimension dialogue windows; users simply click on a spreadsheet cell, which brings up the appropriate Dimen- sion dialogue window for OLAP manipulation and then switches back to the native spreadsheet view. Version 7.1 of TM1 provides an alternative to the Cube Viewer and Dimension dialogue windows by offering OLAP functions directly from an OLE object embedded in the worksheet. Changing the position of members in a dimension level It is not possible to change the position of members in a dimension directly from a worksheet. This is only possible using the model design dialogues or by editing the dimension worksheet.

Visualising the drill-down hierarchies Icons in the worksheet model provide pointers on drill-down paths. The overall structure of the dimensional hierarchy can be displayed graphically in tree-form in the Dimension dialogues. Drilling down to detailed data End users can drill through TM1 cubes and access transaction-level data in a RDBMS (provided that the TM1 architecture is configured to relational DBMS data storage). Range of front-end user tools All TM1 clients are add-ons to the Microsoft Excel and Lotus 1-2-3 spreadsheet environments. Additionally, TM1 exploits the OLE DB for OLAP interface to extend the range of front-end options to more than ten OLE DB for OLAP consumers, including Cognos PowerPlay, Knosys ProClarity, Brio Enterprise and Seagate Worksheet. However, most integrations are read- only. The OLE DB for OLAP support also provides links to custom front-end interfaces developed by other tools, including Gentia Software’s Holos, Comshare’s Decision and ArcPlan’s Insight development environments. Visualising the results The visualisation of results depends on the front-end tool employed. The TM1 clients rely on the charting and graphing conventions provided by Excel and Lotus 1-2-3. Data from several models may be included in a single worksheet view. Saving and sharing results Designing a report TM1’s report design capabilities depend on the front-end tool employed. TM1 client programs rely on the reporting facilities provided by the spreadsheet. Support is provided for stacked (nested) cross-tab spreadsheet views. End users can also exploit the macro functions of Microsoft Excel and Lotus 1-2-3 to add their own locally-defined calculations. Similarly, objects can easily be embedded into worksheets via OLE. Publishing a report There is no direct support for scheduling the publication of models across the enterprise. Targeted distribution via e-mail There are no additional facilities provided for end users to e-mail ‘live’ TM1 models from the spreadsheet client interface. Subscribing to reports TM1 does not provide any report subscription services.

Building the business model Summary

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TM1’s spreadsheet-based approach to modelling adds simplicity to the data mapping and transformation process. Although it sounds slow and limited, it is adequate for small data volumes – as typically found in the application areas that TM1 targets. An alternative (and much quicker) approach relies on a straightforward graphical mapping to relational and flat file sources, but is only suitable for basic cases, where the source data and metadata require no transformations. Server-based Process Objects add a much needed layer of automation and management to the model building process. Basic design Design interface TM1 supports two design interfaces for building models: • TM1 Architect, which provides a number of graphical dialogues for point- and-click development • TM1 Perspectives, which uses the spreadsheet as the development interface. Visualising the data source It is possible to view source tables. However, it is not possible to view the database schema or bring up a sample of data on screen. Universally available mapping layer TM1 does not support a universal mapping layer. Prompts for metadata TM1 does not provide any automatic prompts for including metadata during the model-building process. Building the dimensions Selecting columns for the dimensions Columns for dimensions can be selectively included (or excluded) via point- and-click. Each TM1 model can have a maximum of 16 dimensions. Selecting the members shown in a dimension level Members can be selected and hidden in a dimension level using point-and- click. Defining a dimension hierarchy Multiple hierarchies and consolidation paths can be built using graphical dialogues. TM1 Perspectives can edit the dimension worksheet to layout the dimension structure.

Time dimension TM1 does not require a cube to have a special time dimension, but can recognise time dimensions if defined. Aggregations and other OLAP calculations over time must be built-in to the model manually. Annotating the dimensions There is no support for the annotation of model dimensions. Default level of a dimension hierarchy When a model is created, the system default shows only the highest level of consolidated elements along the title dimensions. However, end users can create a subset of data for a specific dimension level. This can be saved as a ‘view’ for future access. Defining the measures Calculated measures A graphical Rules Editor dialogue can be used to define formulas for calculated measures (or any other dimension elements) in a model via point-and- click. Typically, a rules formula is an expression of arithmetic operators and parentheses, numeric constraints, numeric and string functions, conditional logic and cube references. The rules language allows for rules to inter-relate with multiple cubes. Support for multiple measures with a set of dimensions A model can store data against one or more measures. Multiple designers Multiple designers Other than simple locking mechanisms, there are no special check-out/in facilities to support multiple designer environments. Support for versioning There is no support for versioning files created by TM1.

Advanced analytical power Summary

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TM1 provides limited support for advanced prebuilt analytical functions. There are basic ranking, arithmetic and financial aggregation functions. Beyond this, it is expected that end users will either build their own custom functions using the TM1 rules language, or exploit native spreadsheet functions to extend the analytical capability.

Third-party tool integration All the TM1 clients are offered as Excel add-ons and therefore benefit from the analytical functions provided by the spreadsheet tool. There is no integration with specialised statistical packages. Defining specialised models Ranking and sorting Simple sorting and ranking functionality is supported via the OLE DB for OLAP interface. Mathematical methods TM1 only supports basic arithmetic operators such as addition, subtraction, multiplication, division and exponentiation. Financial functions TM1 supports standard financial aggregation and currency conversion functions. Statistical models There is no support for advanced statistical modelling. Trend analysis Through its integration with Microsoft Excel and Lotus 1-2-3, TM1 supports basic graphical support for straight-line (linear) trend analysis. Simple regression TM1’s forecasting functions rely entirely on the regression functions provided by Microsoft Excel or Lotus 1-2-3. Time-series forecasting There are no special functions for advanced time-series forecasting. User-definable extensions TM1 supports a non-procedural rules language for defining custom multidimensional and inter-cube calculations. Write back for ‘what-if’ analysis TM1 supports multi-user write back to the database for what-if analysis scenarios. At a local level, spreadsheet users can write-back new values in a worksheet for uncalculated values in a cube and immediately see the effect of changes in cell values across the current view. Changed data in a shared server is available immediately to all users of the server. Incorporating non-numerical data TM1 can store textual information as attributes or as cell values. These values can be used in simple counts or rules calculations. Data mining There is no support for data mining in TM1.

Web support Summary

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TM1 does not support its own web client for accessing TM1 Server. The TM1 web strategy is based upon providing a range of capabilities for TM1 application deployment and development across the Web from third-party tools using the OLE DB for OLAP interface. The level of OLAP functionality provided varies considerably from tool to tool, and most integrations are read-only.

Management Summary

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Most model administration tasks are achieved graphically using the Server Explorer interface. Strong data replication and synchronisation facilities are provided to manage distributed environments. The introduction of Process Objects greatly improves the back-end processing and scheduling capabilities of the tool. Because of its concentration on financial applications, TM1 security goes further than most OLAP products. Access controls can be defined on a per model-, dimension- or cell-level for users, groups or servers. TM1’s monitoring facilities log all OLAP transactions, but the presentation of metadata could be improved. Management of models Separate management interface The Server Explorer provides a graphical interface for managing models and administering local and remote TM1 Servers. Common model administration tasks are achieved through two windows: • one presents hierarchical lists of models and dimensions and other related server objects that are accessible via the TM1 Server • the other references the properties of the TM1 Server objects. Security of models Security controls can be defined for servers, cubes, dimensions and elements to restrict access to models: • cube-level security governs overall user access to models; privilege levels include read/write access, reserve access (provides exclusive rights to the model until it is released), lock access (means that other users cannot modify the model, but can access it as read-only) • element-level security governs access to cells identified by certain elements • dimension-level security governs the ability to add, remove and re-order the elements in a dimension.

Query monitoring TM1 Server tracks the transactions made by client OLAP requests in an ASCII log file. It provides information about who made the change, what model it was made to, when it was made and how certain cell values were affected. Management of data How persistent data is stored (not scored) TM1 stores data persistently on disk as compressed proprietary files. It also offers the option of storing data in relational tables. Only the lowest-level detail (base-level) data for a TM1 multidimensional model is stored persistently, and is loaded into memory on the server when requested by end users. All consolidations and calculations are done on-the- fly and are also stored in memory. Scheduling of loads/updates Process Objects provides an activity scheduler for controlling tasks such as defining and executing ODBC queries for loading and updating cubes from relational databases or flat file systems. Event-driven scheduling Process Objects supports external event-driven scheduling functions. Failed loads/updates Data Control provides a ‘key error report’ that provides a list of the key errors that occurred during the data load/update process and other background processing tasks. Distribution of stored data TM1 Server supports server-to-server replication of data. Replication is bi- directional and the ability to see or change data in replicated model data is managed through security assignments. Model metadata (dimensions and rules) is only replicated from the replication server to the planet servers, and cannot be changed on the planet servers. Sparsity For calculations across sparse dimensions, TM1 uses a sparse consolidation algorithm that skips over areas of the model that are zero or undefined. Methods for managing size TM1 only stores the lowest input elements in the model and does not precalculate any data. This serves to shrink the size of the MDDB considerably. Additionally, TM1 uses compression technology and algorithms for both disk and memory storage. In-memory caching options TM1 relies on the caching configuration options provided by the Windows operating system.

Informing the user when stored data was last uploaded There are no facilities for automatically informing end users of the currency of data accessed in the TM1 Server. Management of users Multiple users of models with write facilities TM1 supports concurrent multi-user read/write concurrent access. User security profiles User security is maintained by groups. Users can belong to multiple groups. User profiles are built upon six access levels and can be easily set-up and maintained graphically. Query governance There is no concept of query governance within TM1. Restricting queries to specified times There is no support for restricting queries to specified times of the day. Management of metadata Controlling visibility of the ‘roadmap’ TM1’s security schemes control overall access to models and metadata.

Adaptability Summary

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Adaptability in TM1 is generally a case of adding new cubes, dimensions and rules to TM1 models. All this is well supported using the dialogues of the worksheet development interfaces. There are limited facilities to ensure that the data sources, models and metadata remain synchronised at all times. TM1 has a rigid MOLAP-only architecture. Change in business requirements Adding new dimensions to a model New dimensions can easily be added to a model. Users are limited to 16 dimensions per cube. All changes are logged in an audit file. Re-use of dimension definition Dimensions can be shared between multiple TM1 cubes, databases and applications. Adding new measures to a model New rules can be written to calculate model data or to integrate additional data in inter-model calculations.

Re-use of calculated measure definition Rules defined in TM1 can be saved in a file and re-used across different models. Changing the architecture to reflect business needs TM1 is a MOLAP tool. A ‘relational storage’ option permits the storage of multidimensional data in relational tables. However, there is no support for configuring TM1 to operate in ROLAP mode. Changes to data sources Keeping the data source and model schema synchronised TM1 implements a MDDB datastore. As such, there is no possibility of the model getting out of synch with the data. Automatic updating of members in a dimension New members can be added to dimensions during scheduled incremental loads, without taking the TM1 Server offline. Metadata Synchronising model and model metadata There is very little model metadata to synchronise in TM1. Impact analysis There is no support for analysing the impact of changes in the data source on TM1 models. Metadata audit trail (technical and end users) TM1 automatically logs database changes, which can be viewed in a detailed log file. The information logged includes the date and time the transaction was made, the name of the client, the value changes before and after the transaction, and elements that identify the cells that have changed. Access to upstream metadata There is no access to external metadata sources or repositories.

Performance tunability Summary

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TM1 performs exceptionally well against small data volumes. Business models can be processed in memory, which allows for fast calculations of models in real-time. For large-scale deployment, TM1 supports SMP, multi- threading, load balancing and server clustering. However, performance can be adversely affected by large concurrent user loads, particularly where large numbers of users are writing back to the database and performing complex OLAP calculations. Adding RAM to the server running TM1 has a direct effect on capacity rather than performance.

ROLAP TM1 is a MOLAP-only tool. MOLAP Trading off load time/size and performance TM1 loads only the lowest level input elements of a model into the OLAP engine and does not precalculate and store data during batch loads. All calculations are done in real time as users request data. Model data is stored in a very efficient manner, allowing it to be easily loaded into memory. Subsequent calculations are also stored in memory for enhanced performance. Support for multiple users Typically, a single TM1 Server can support around 100 end users. However, in large-scale environments, performance is highly dependent on the number of concurrent users reading and writing back to the database, as well as the complexity of the OLAP calculations performed. Processing Use of native SQL to speed up data extraction TM1 uses ODBC to extract data from relational databases. Distribution of processing TM1 supports a multi-cube architecture and is able to distribute processing at multiple ‘regional’ cubes that feed a higher-level ‘consolidation’ cube. The cubes can be stored and processed across clusters of TM1 Servers. SMP support TM1 supports multi-threading and SMP parallelism.

Customisation Summary

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TM1 is an out-of-the-box OLAP solution, and there is limited scope for developing custom applications or interfaces. TM1 does not support a visual development environment, but an API is provided that allows third-party tools to access TM1 Server functions. Integration with third-party OLAP development environments is supported via OLE DB for OLAP. Customisation Option of a restricted interface The different versions of the TM1 client tools (Perspectives, Architect and Client) naturally lend themselves to providing restricted functionality. However, it is not possible to turn off specific functions for different users.

Ease of producing EIS-style reports There is no direct support in TM1 to produce EIS-style reporting interfaces. Applications Simple web applications TM1 supports a JavaBean API, which allows OEM-type development of bespoke Java applications that access TM1 Server. Development environment TM1 does not support a visual development environment. Use of third-party development tools The TM1 API allows for the development of custom front ends using Visual Basic, PowerBuilder, Delphi and C++. Other customisation features Localisation TM1 is available in English, German, French, Hebrew and Japanese language versions.

Deployment

Platforms Client TM1 clients (Perspectives, Architect, Client and Classic) run on Windows 95, Windows 98 and Windows NT. The TM1 Classic client also runs on Windows 3.1. TM1 Perspective and TM1 Client require Microsoft Excel (7 and 97) and Lotus 1-2-3 97 software. TM1 Classic supports Excel 5 and Lotus 5 software. Server TM1 Server runs on Windows 95, Windows 98, Windows NT, Unix (AIX, Solaris and HP-UX) and Linux.

Data access TM1 can access data from any ODBC-compliant relational database. It can also access Microsoft SQL Server OLAP Services MDDB.

Standards TM1 supports Microsoft’s OLE DB for OLAP API as a data provider. Applix has established a third-party certification programme for its OLE DB for OLAP partners.

Published benchmarks In December 1997, Applix published results of the OLAP Council’s APB-1 benchmark for TM1.

Price structure Pricing for TM1 Server ranges between $28,000 for five concurrent users, to $110,000 for 100 concurrent users.

Summary

At a glance ...... 2 Terminology of the vendor ...... 3 Ovum’s verdict ...... 4 Product overview ...... 6 Future enhancements ...... 16

Commercial background

Company background ...... 17 Distribution ...... 19

Product evaluation

End-user functionality ...... 20 Building the business model ...... 22 Advanced analytical power ...... 24 Web support ...... 26 Management ...... 27 Adaptability ...... 30 Performance tunability ...... 31 Customisation ...... 33

Deployment

Platforms ...... 35 Data access ...... 35 Standards ...... 35 Published benchmarks ...... 35 Price structure ...... 35 Evaluation: Brio Technology – Brio Enterprise Ovum Evaluates: OLAP

At a glance

Developer Brio Technology, Palo Alto, CA, USA

Version evaluated Brio Enterprise, version 6.0

Key facts • A desktop business intelligence tool that provides query, OLAP analysis and reporting • Servers run on Windows NT and Unix; clients run on Windows 3.1, Windows 95, Windows 98, Windows NT, Macintosh and Unix Motif. Web access is also provided • In June 1999, Brio acquired Sqribe Technologies, a provider of web-based enterprise query and reporting tools; it plans tight integration between the two product sets

Strengths • A tightly integrated OLAP suite that is easy to use and deploy • Sophisticated report distribution via ‘push’ and ‘pull’ web servers • Strong metadata integration with a range of data warehousing tools

Points to watch • Not the strongest product set for complex OLAP analysis • Inconsistent server administration facilities • Desktop architecture has scalability issues for analysing large datasets

Ratings

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End user functionality

Building the business model

Advanced analytical power

Web support

Management

Adaptability

Performance tunability

Customisation

Terminology of the vendor

Analytic application Brio defines an ‘analytic application’ as a customised solution that addresses a specific set of analysis requirements for either a horizontal or vertical market and/or a specific set of business users. DataCube A dynamic view of multidimensional data that is fed directly from the Desktop DataCache. Each Brio report has a DataCube as its basis. Data model A visual representation of database tables that provides end users with a business-oriented view of the data. Data models are saved as part of a document file and stored in a central repository (typically in a relational database). Desktop DataCache This stores a slice of relational data extracted from a database. The DataCache is stored in a compressed format on the client and provides the source data for analysis and reports. The DataCache also supports a local OLAP engine. Document A Brio file that stores data model and query specifications for retrieving data from a database, as well as the results set and reports created from the queried data. Documents can be stored locally on the client or remotely in a file server. Items Items are discrete informational attributes of topics (such as customer ID) and represent the column fields of data in database tables. Items are organised within topics and are used to query data. Computed items calculate a fresh value for each original value based on a computation; for example, revenue calculated from price and units. Repository A special set of relational tables that centrally stores data models and document security settings. The repository information is referenced each time a document is requested by end users. Topics Topics provide a visual representation of tables in the database and are an element of data models. Topics are organised in logical groupings, which reflect a particular aspect of the business, such as customers or sales. Each topic contains a list of items. Meta-topics are custom topics created from items in other topics. They are used to simplify views of the underlying data by creating ‘virtual’ tables that are independent of the underlying database.

Ovum’s verdict

What we think Brio Enterprise is a strong client tool for business intelligence that is both easy to use and quick to deploy, although its administration facilities could be improved. It offers a tightly integrated suite of tools that is best suited to large, geographically dispersed organisations that need to provide users with easy access to a wide range of data sources from a PC or web browser. Cross-platform support in conjunction with ease of use makes Brio Enterprise particularly suitable for deployment across large enterprises. It is designed to exploit the Internet or corporate intranets. Business intelligence can easily be deployed across the enterprise via a flexible ‘publish-and- subscribe’ model using ‘push-and-pull’ servers for report processing and distribution – although these capabilities come with a substantial price tag. Brio’s Adaptive Reports capability allows administrators to easily adjust the functionality of reports to match the diverse needs of users. End users are provided with an equally flexible set of tools that is suitable for general business use, rather than complex analytical applications. Visual development capabilities have also been integrated into the core query, OLAP and reporting environment to build analytical front-ends. The development tools adequately support simple application needs, but lack the sophistication of a fully-fledged development environment. Brio Enterprise easily connects to a range of multidimensional and relational data sources via its ‘snap-in’ APIs. It also sets the standard for metadata integration with data warehouses. This removes the need for Brio to maintain proprietary semantic layers, and ensures that views of data models are based on consistent metadata that is shared across the enterprise. Brio Enterprise’s server administration facilities are mediocre and dilute the product suite’s overall strength. The product’s client-centric architecture easily lends itself to the analysis of small parts of the database, which are automatically loaded onto the desktop for immediate OLAP analysis. While this is ideal for ease of use and quick deployment, scalability is limited by the size of the datasets being analysed and the complexity of the OLAP calculations; optimal performance is gained when working with results sets of less than 50,000 rows. However, the effective use of metadata means that users can query large data warehouses and often still work satisfactorily with results that fit within these criteria. When to use Brio Enterprise is suitable if you: • want an integrated out-of-the-box query, OLAP and reporting solution that is easy to use and quick to rollout • are a large, geographically dispersed enterprise that wants to provide general business users with easy access to data and the ability to develop simple analytic applications without IS involvement • want to exploit your corporate intranet for distributing corporate data • want easy connection to a wide range of data sources • already have a data warehouse or OLAP server in place and are looking for a flexible front-end tool that easily connects to it.

It is less suitable if you: • have applications that require the analysis of large datasets with more than 50,000 rows of data • want to support OLAP analysis based on dimensionally complex business models • require advanced financial budgeting and forecasting functions • want to develop analytical applications with highly specialised OLAP functions from scratch.

Product overview

Components Brio Enterprise, version 6.0, consists of the following components: • Brio Enterprise servers – OnDemand Server and Broadcast Server • BrioQuery end-user tools – Designer, Explorer and Navigator • web clients – Brio.Insight and Brio.Quickview. Figure 1 shows the primary functions of the components and whether they run on the client or the server.

Brio Enterprise Server Solution The Brio Enterprise Server Solution consists of two ‘push’ and ‘pull’ servers – Broadcast Server and OnDemand Server – and a unified administration tool. Data models and document security settings are stored in the Brio repository: typically, a group of specialised tables stored in a RDBMS server. Broadcast Server This is a query server that schedules and automates query processing and report distribution. Broadcast Server ‘pushes’ precomputed documents and reports in a highly compressed format out to web, client-server and mobile clients via FTP, e-mail, web servers, document file servers and network printers. The processing and delivery of documents can be based on time or event criteria. Typically, Broadcast Server is used to reduce the load on OnDemand Server by following the 80/20 rule – distributing weekly reports that 80% of users need. OnDemand Server This is a web application server that enables ad hoc querying via the Web. It is a ‘pull’ server for executing database queries, compressing results and transmitting data to Brio web clients (Brio.Insight and Brio.Quickview) for further analysis. Output documents created by Broadcast Server can also be accessed (on-demand) by web clients from a document file server. The web environment benefits from Brio’s Adaptive Reports technology, which ‘adapts’ query, analysis and reporting functionality based on user privileges.

Figure 1 Component functions

OLAP Web access Model design, application Distribution Analysis development and administration

Client BrioQuery Brio.Insight BrioQuery Designer Explorer Brio.Quickview BrioQuery Navigator

Server OnDemand Brio Server Administrator Broadcast Server Server

The OnDemand Server requires a Java runtime environment and supports CGI, ISAPI and NSAPI Internet APIs to connect to web servers. It works against relational data warehouses and third-party OLAP servers.

BrioQuery end-user tools These are sets of client-server desktop tools that integrate ad hoc query, OLAP and EIS reporting in a single interface. They also provide administrators with access to security and DBA-like services. The BrioQuery tools differ from the Brio web clients in that they access databases directly, using a combination of native and standard database connectivity APIs (Microsoft’s OLE DB and ODBC). The BrioQuery tools are ‘fat’ desktop clients and are available in three editions. Each edition incorporates core query, analysis and EIS reporting capabilities, but adds increasing levels of data modelling functionality. BrioQuery Navigator This provides business users with access to a repository of predefined data models and reports created by an administrator. The functionality is weighted towards ‘information consumers’ that do not have the technical ability (or the need) to access database tables to independently build their data models. Navigators typically use data models stored in the repository to create ad hoc queries, perform OLAP analysis and generate reports. BrioQuery Explorer This adds data modelling capabilities to the Navigator tool. It is designed for power users that need direct access to database tables to build their own data models and reports, on top of easy access to repositories of predefined data models and reports. BrioQuery Explorer also has graphical development tools to connect to data sources and map database tables and columns to Brio data models. The product also provides access to Brio’s Open Metadata Interpreter (OMI) functions, in order to access upstream metadata from a variety of data warehousing tools at runtime, including Informatica’s PowerMart Suite, BM Visual Warehouse, Ardent DataStage and Prism Warehouse Manager. BrioQuery Designer This is a graphical tool, similar to DBA, for administering the Brio environment. It adds repository creation, repository management, security and auditing capabilities to the Explorer feature set in order to build, maintain and distribute data models that can be accessed by Explorer users via the repository. The EIS section in Designer provides application development tools for assembling analytic front-ends. The environment uses a combination of drag- and-drop and a scripting language (JavaScript) for building simple analytic front-ends. The development capabilities are explored in detail in the Using Brio Enterprise section.

Brio web clients The web clients provide similar functionality to the BrioQuery Navigator and Explorer tools, but do not offer any data modelling or application development capabilities.

The web clients also use Brio’s Adaptive Reporting technology; both clients can ‘adapt’ their capabilities based on a combination of the content of each report and the user’s security profile. These capabilities can be restricted to simple report browsing or can provide users with full query and analysis functions. Brio.Insight A web-based query, OLAP analysis and reporting tool that is offered as a plug-in to existing web browsers. It provides a similar level of functionality to the BrioQuery Navigator tool, including ad hoc query and OLAP analysis. Brio.Insight works with both of the Brio Enterprise servers; it can manipulate documents posted by the Broadcast Server and allows ad hoc querying when used with OnDemand Server. Brio.Quickview This is a web-based report browser extension, which allows business users to access and view portfolios of precomputed and formatted Brio reports; it only supports the ‘view’ and ‘view and process’ capabilities provided by Brio.Insight. End users can navigate multiple reports by going through a series of tabs at the bottom of a Brio document. When used in conjunction with OnDemand Server, administrators can also grant end users the right to refresh the data on-demand on a report-by-report basis, or limit the view based on a set of criteria. Scripts and EIS-tabs can also be used to guide novice users through a series of reports. Architectural options

Full mid-tier architecture Brio Enterprise does not support a full mid-tier OLAP engine and MDDB store. However, it can easily connect to most third-party OLAP servers natively or through OLE DB for OLAP. Connection wizards are provided to facilitate the process of creating connection files (OCEs).

Light mid-tier architecture Two variants of the ‘light’ mid-tier architecture are supported by the introduction of OnDemand Server or Broadcast Server (or a combination of both). OnDemand Server The OnDemand Server is the ‘thin-client’ variant. It operates behind a web server. Communication is facilitated by three web broker components – NSAPI, ISAPI and CGI. OnDemand Server processes queries against a range of relational and OLTP data sources and MDDBs. SQL generation and processing is performed on the server, which then transmits compressed results data back to Brio’s web clients (Brio.Insight and Brio.Quickview) for further analysis. Broadcast Server The ‘fat-client’ variant uses Broadcast Server as a mid-tier processing engine for immediate or scheduled delivery of reports. The results are processed on the server and published to specified users or groups. Broadcast Server can be used in client-server environments or web-based intranets.

Desktop and mobile architectures The typical client-server configuration for Brio Enterprise is based on a local desktop OLAP engine, although most implementations are now via the Web, using the Brio web clients on the desktop and the Brio Enterprise Servers for database connectivity. When the BrioQuery client runs a query, the raw data is returned to the desktop in a highly compressed format called a DataCache. For OLAP analysis and reporting of the data, end users create temporary multidimensional structures (called DataCubes) on-the-fly in the local client machine’s memory. These DataCubes are ‘dynamic’ in the sense that they allow end users to perform OLAP functions (such as pivot and drill-down) to the data in order to gain different business views. There is seldom a need to re-query the database server, because all the data is held locally on the client machine. The Brio architecture naturally lends itself to mobile configurations. Data can be downloaded and saved locally on a laptop for offline analysis. Similarly, a ‘snapshot’ slice of a cube retrieved directly from an MDDB into a Pivot structure can be exported to, and retained in, an independent results grid within a Brio document for disconnected analysis. UsingBrioEnterprise

An integrated OLAP environment Brio Enterprise’s greatest strength is its intuitive and easy-to-use interface, which Microsoft 2000 users will feel comfortable with. Data modelling, query, OLAP and EIS reporting functions are seamlessly integrated, and end users can navigate through these modes in a non-procedural manner using the six report sections – Query, Results, Pivot, Chart, Detail and EIS – that are provided by the document map. Building the data model The BrioQuery Explorer and BrioQuery Designer tools provide a Query section and tools for creating the ‘data model’. The model provides a simplified representation of database tables, which allows end users to graphically query data (as opposed to using SQL). End users create data models by mapping tables and columns as topics and fields as items, and making their own custom joins. Connection to a database is via the source content area that provides a tree-like display of all the tables in the database. Selected tables can be dragged from the source content area into the Query section to build a topic, as shown in Figure 2. Users can view the topic as a simple list of data items or get a more detailed view by providing a sample of the underlying data. Data models are stored in a central repository and assigned access rights for controlled distribution to end users. Data models are referenced by ‘documents’ – physical Brio files that persistently store references to a data model, query specifications, results data and one or more reports. Documents can be stored locally on the client or remotely in a file server.

Figure 2 Building a data model

Query and analysis Once a data model is created in the Query section, it can be used to query the database and download results to the client desktop. A query is simply constructed by dragging topic items listed in the source content area to a ‘request’ line. End users can also drag topics to the ‘limit line’ to further restrict the information returned. Similarly, requested topics can also be dropped into the ‘sort’ line for a range of nested ‘sort’ functions. A single Brio document can contain multiple query sections and these may be any of the data access query types (relational, OLAP, imported datasets or local-join queries). The results data is downloaded to the client desktop and cached in memory in a relational data structure called a DataCache. End users can also apply data functions to aggregate and calculate totals, as well as derive new columns of data based on the results data returned. A results set can be used as the basis of one or more Brio reports. Four report sections are available – Pivot, Chart, Detail and EIS.

Figure 3 Pivot section

OLAP capabilities are provided by the Pivot section shown in Figure 3, through which drill-down, pivot and ‘slice-and-dice’ operations can be defined. Dimensions can be dragged from the source content area to an outliner section, to change the nested levels of dimensions. It is also possible to add a number of surface-level calculations to the tabular data. Another report section provides support for charting and detail reports. Each has its own outliner section for creating drillable business charts and designing professional reports. Tools are also provided to build briefing-book style EIS interfaces for casual end users.

Working directly against MDDBs When connecting directly to an MDDB, the document immediately opens the Query section. Rather than working with a data model, representing tables and columns, the Query section displays the structure of the MDDB as a hierarchical tree. In this instance, the query building process introduces dimension members and measures directly from the tree to the Pivot outliner tool. This query method bypasses Brio’s previous method of ‘flattening’ the cube. Instead, a slice of the cube requested from the MDDB is loaded directly into a Brio Pivot structure – each additional OLAP query to the Pivot report re-queries the MDDB for a new slice. When working in ‘hardwire’ mode, the database is re-queried after every user change to the outliner. A ‘process query’ mode allows users to introduce multiple items into the outliner, before the request is sent to the database to retrieve the cube slice – which is useful for building large OLAP reports without the need to continuously burst traffic to the server. A Slicer tool is provided to limit the scope of the server cube. It defines a logical slice of the cube, by instructing the server to ignore all values that are

not part of the slice. For example, if users want to limit the view of a cube to data only for 1998, the member value 1998 from the year dimension is simply dragged into the Slicer tool. A Filter Box is also provided for defining limits once levels have been introduced in reports. The Filter Box allows for the setting of comparison operators that act on the values for that member (similar in concept to member selection). Additional server-specific functions are available in the Filter Box to be included as part of the limit. Each MDDB supports its own list of functions – representative functions include top N and top N%.

Advanced OLAP reporting At the core of OnDemand Server is its unique Adaptive Reports capability, which is designed to meet the diverse needs of many business users across the enterprise – ranging from simple report browsing to advanced query and analysis. It is a useful tool for managing user access and system resources. Adaptive Reports is only supported in a web environment. On a report-by- report basis, Brio.Insight and Brio.Quickview can ‘adapt’ their capabilities, based on the intersection of the content of each report and the end-user’s security profile. Five different modes can be enabled, which cascade towards higher levels of functionality. These range from the simple viewing of – and navigation through – predefined reports, the refreshing of results by reprocessing the query over the Web, ad hoc analysis (create and edit Pivot, Chart and Detail reports) of retrieved documents, allowing new data to be queried, and changing existing queries upon which reports are based.

Metadata integration A key feature of Brio Enterprise is its ability to directly access upstream metadata from data warehousing tools and a number of third-party metadata repositories from Informatica, Ardent and others (an exhaustive list is provided on Brio’s website). Access to third-party metadata eliminates the need to define and maintain proprietary metadata within the Brio environment. As the tools source metadata dynamically at runtime from the underlying data sources, any changes to the associated metadata are automatically updated to the query tool at runtime. If a metadata source is available and stored in relational tables, Brio Explorer and Brio Designer can use the Open Metadata Interpreter (OMI) facility to link it to data models and apply metadata information automatically. The OMI is a feature of Brio’s Open Catalogue, which manages database connectivity through a graphical connection interface, as shown in Figure 4. The interface provides several tabs for adjusting connection preferences and accessing table, column, join, lookups and remarks metadata. Snap-in metadata templates are included and available to users via the Meta Connection wizard. These templates provide the definitions required for the ‘Remarks’ interface and are fully customisable. A ‘Remarks’ dialog, as shown in Figure 5, on a topic or item may have separate tabs displaying the definition of a column, the last update of the table, the number of rows in the table, transformation rules applied to the data and the source of the column. Administrators can define as many different elements of metadata to display as required.

Figure 4 Graphical connection interface

Figure 5 Remarks interface

Access to structural metadata about the data warehouse streamlines the model-building process, by automatically applying metadata table- and column-naming conventions and descriptive information to data models. Metadata can also help to build models that produce faster results – for example, finding distinct values for a column from a 50 million row table can be achieved quickly, because the SQL query is run against a look-up table instead of a fact table. Furthermore, changes in the metadata definitions can be synchronised with data models stored in the repository for increased adaptability and maintenance.

From an end-user perspective, contextual metadata (such as special database remarks inputted by the DBA, which describe the origin, derivation or detail about data) provide a business-oriented description of model components. Metadata can also be accessed to show from where the data was sourced.

Building analytic applications Brio Enterprise supports the development of custom analytic applications. The term ‘analytic’ in this context refers to the delivery of pre-packaged business intelligence information that is encapsulated within a graphical interface similar to EIS. The tools are aimed at two audiences: users that wish to extend or assemble their own applications to support specific business needs, and ISVs or VARs that want to create domain-specific applications. Application development is carried out in the EIS section (in design mode). The EIS section provides a number of interfaces and tools to support application development: embedded report components, EIS controls and scripting. Typically, a Brio developer combines embedded report sections with user interface controls using a visual layout tool, and then scripts interactivity between these controls and the native Brio application. Embedded sections Brio report components (Pivots, Charts and Tables) can be embedded directly into the EIS workspace for display. All embedded report components are ‘live’ objects and are automatically updated with fresh data. Embedded reports carry a property defining them as either: • view-only – a static image of the report with no interaction capabilities • active – allowing the end user to interact with the report data, providing drill-down, pivoting and other analysis functions via point-and-click • hyperlink – follows the browser model of a single-click, jumping directly to the original section. For Brio reports, this means switching the display to the native report section in the document. Extended EIS controls This is a set of user interface controls, which is also provided for inclusion in EIS applications. The controls include buttons, radio buttons, check boxes, text (edit) boxes, list boxes and drop-down list boxes. The controls can be populated with values at design time (or dynamically at runtime with values) by the scripting language. Each of these controls can have scripts attached to them to trigger events in the document. Layout tools, such as rulers, snap-to-grid and design guides, are provided for positioning controls onto the EIS screen. Object model and scripting JavaScript is the underlying scripting language for controlling Brio applications – Brio Enterprise has the Netscape JavaScript (version 1.4) built-in to the application. In addition, an object model is provided for manipulation by the internal and external script languages.

A BrioQuery application is composed of a series of objects, each with methods and properties. A set of application objects has been exposed to the programmability system, with constructs to query and set properties and invoke methods. The object model will typically be manipulated by JavaScript from inside an EIS section. The object model can also be accessible from external applications capable of making OLE automation calls. Several tools are available to assist in writing JavaScript and to manipulate the Brio object model. These include: a built-in (but somewhat basic) script editor window used to attach JavaScript to an EIS object • an object browser, which provides a tree-like display of control objects, methods and properties that can be included in a script via point-and- click. The browser is dynamic and displays the active state of the objects available in the application at any point in time • execution and console windows, for testing commands and debugging scripts for syntax. A console window is provided to display error messages and alert values generated by the JavaScript interpreter; application designers can write messages to the console window to track the state of variables and the progress of the script.

Future enhancements

The next release of Brio Enterprise will focus on integrating the software tools acquired from Sqribe. The release of Brio Enterprise version 7.0 is planned for the first half of 2000 and is expected to deliver a full integration between the two product sets. This will include a central user repository and enhanced and fully integrated administration tools for the servers. In particular, the transaction-oriented capabilities of Sqribe’s ReportMart enterprise information portal architecture will be integrated within Brio Enterprise’s web environment. For the core Brio Enterprise suite, OLE support, both as a consumer and provider (server), will also be provided. Connection to Oracle Express is expected in the second half of 1999 – after version 2.0 of the OLAP Council’s MDAPI is made publicly available. Brio is also planning more sophisticated data visualisation tools and will move the product to a much thinner client architecture. It will also provide snap-in metadata capabilities to the Microsoft and Platinum metadata repositories. Brio aims to deliver a number of vertically focused analytic applications; its subsidiary company, MerlinSoft (which Brio acquired in 1998), is considering vertical niche opportunities. As part of the company’s Private Label Partner programme, Brio expects an increasing number of vertical analytic applications ‘powered’ by Brio technology to be developed by partners, such as Broadbase.

Commercial background

Company background

History and commercial Brio Technology is a US company, which was founded in 1984. The company spent its first six years as a small decision-support consulting firm. Brio introduced DataPrism, a graphical end-user query tool, in 1990. This was followed a year later by DataPivot, a desktop-based OLAP analysis tool. In 1991, the consulting component of the company was terminated and it moved to a more traditional software product vendor model. The existing tools were later merged into BrioQuery in 1994. In March 1995, Swedish founder (and current CEO), Yourgen Edholm embarked on an expansion programme, bringing in new investors and building up sales & marketing, recruitment and product development. Since then, the company has consistently recorded year-on-year growth of more than 100%. Brio’s latest product suite, grouped under the ‘Enterprise’ banner, was first released in November 1997. This represented a significant shift away from the company’s initial desktop focus to enterprise-wide deployment. As a privately held company, Brio received significant investment from several venture capitalist firms. The company had an IPO in May 1998, which raised approximately $30 million and led the way for rapid growth. Brio’s revenues grew 74% to $46.5 million for fiscal 1999. Significantly, the company also achieved profitability in 1999, with net income standing at $776,000, compared to a loss of $6.7 million in fiscal 1998. In June 1999, the company acquired Sqribe Technologies (www.sqribe.com), a provider of web-based enterprise query, reporting and information portal software, in a transaction valued at $270 million, whereby Brio shareholders own approximately 55% of the combined company and Sqribe 45%. Sqribe is a venture-backed firm that has revenues of $39 million and 7,500 customers worldwide. Its main revenue-generating product is SQR, an enterprise reporting tool. The combined company, which retains the Brio name, will have revenues in excess of $80 million and employ over 500 people worldwide. The completion of the acquisition is planned for mid-1999. Edholm will retain his position as president and CEO, with Sqribe’s CEO, Ofir Kedar, serving as chairman.

Character and direction Brio Enterprise is aimed at the enterprise level and its functionality is designed to support the needs of a large number of general business users (that only use OLAP perhaps 10% of the time), rather than an elite group of analysts (that use OLAP 100% of the time). The tools are designed to be easily and quickly implemented, to scale from departmental deployment to enterprise solutions and to minimise the dependence on IS for business intelligence needs. While Brio initially positioned its tools as the ‘universal’ front-end for business intelligence, the company is reinventing itself to move beyond its desktop OLAP technology base, to become a provider of ‘enterprise-class’ business intelligence solutions. The recent acquisition of Sqribe brings together two growing and complementary players in the business

intelligence market; previously, Brio had forged a partnership with Sqribe’s chief rival, Actuate Software. However, Brio believes that integrating Sqribe’s server-based enterprise reporting capabilities into the core of Brio Enterprise suite gives it a better chance of gaining market leadership in the broadening market of (increasingly web-based) business intelligence solutions. The addition of Sqribe’s portal technology also gives Brio a headstart on the emerging business intelligence portal segment, which has few players. From a commercial standpoint, the deal triples Brio’s client base and provides significant opportunities to cross-sell its OLAP tools to Sqribe’s existing customers (around 7,500 sites) and vice versa. With version 6.0 of the product, Brio is also making a serious play for the analytic applications market. The company is positioning its tools as a development platform for delivering pre-packaged and custom business- intelligence applications to mass audiences. Although not an analytic application itself, VARs can build programs with Brio Enterprise 6.0 and sell them to vertical or other markets. As a result, Brio is preparing to sell its technology to integrators, ISVs and VARs, and is seeking partners with vertical sector expertise. Brio principally sells to large Global 2000 companies through a direct sales organisation in the US, Canada, the UK, France and Australia. Indirect channels (VARs, distributors, OEMs and systems integrators) account for 20% of revenues. Brio Enterprise has a strong presence in the high-tech, manufacturing/publishing, education and Federal US government sectors. Notable customers include IBM (67,000 users), several US Federal government agencies (50,000 users), Delta Airlines, Dell Computers, the Dutch Police Agency and the US armed forces. Brio’s BI Partnership initiative has forged partnerships with all the leading database and data warehousing tools vendors. Several partnerships have led to technical integration, such as Brio’s numerous metadata links, as well as collaborative marketing and bundling agreements. The company has signed up around 40 software vendors as part of its Private Label Partner programme, which encourages ISVs to embed Brio’s tools in vertical applications. Brio has also signed major reseller agreements with PricewaterhouseCoopers and has a close relationship with IBM (as part of IBM’s DecisionEdge solution for sales analysis, and eWarehouse, a program for delivering business intelligence applications for the Internet). It also has a partnership with Informatica, to integrate Brio Enterprise with Informatica’s Business Components and PowerConnect software products. Customer support

Support International around-the-clock support (GlobalPlus) is available via telephone, e-mail or the Web. Web support is particularly strong and includes the ability to monitor and control support requests internally and from distributors. Support centres are located in the US, the UK, France and Australia. Annual maintenance and support contracts range from 15–25% of the licence fee.

Training Brio offers a range of public and private on-site courses for all its products; one or two-day classes are available for casual end users, power users and administrators. Brio has a number of certified training partners and also provides computer-based training packages.

Consultancy services Consulting services are provided by Brio’s Expert to Expert group. A typical engagement involves working closely with data warehousing projects to provide advice on model design, metadata integration, connectivity and implementation. Brio also maintains numerous referral partnerships with external consultancies. Distribution US Brio Technology 3460 West Bayshore Road Palo Alto CA 94303 USA Tel: +1 650 856 8000 Fax: +1 650 856 8020

Europe, Middle East and Africa Brio Technology Sarl Immeuble Le Cristal 2, rue Hélène Boucher 78280 Guyancourt France Tel: +33 1 39 44 73 73 Fax: +33 1 39 44 21 00

Asia-Pacific Brio Technology Suite A, Level 10 121 Walker Street North Sydney, NSW 2060 Australia Tel: +61 2 9964 9533 Fax: +61 2 9964 9755 E-mail: [email protected] http://www.brio.com

Product evaluation

End-user functionality

Summary

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All the end-user tools have functional and extremely user-friendly interfaces to support general business analysis. The tools offer varying levels of sophistication and users can easily navigate between query, reporting and analysis using report ‘tabs’. The optional semantic layer speeds up queries by shielding users from the complexities of SQL and the database schema. All the tools support advanced WYSIWYG report construction and sophisticated groupware facilities, using scheduled agents and ‘push-and-pull’ servers to enable easy distribution across the enterprise. Wizards are provided to help users through complex tasks. BrioQuery is a capable tool, which suits the needs of small departments. However, there is little reason to use Explorer or Navigator, given the additional benefits provided by the server-based web tools.

Finding and understanding the model Finding and loading a multidimensional model The OnDemand Server provides an HTML page interface for easy navigation and location of documents. Available documents are organised in a folder structure and presented as a ‘document map’ in a pop-up window. Keyword search facilities are also provided. In a client-server environment, data models (and associated documents) can be organised in logical groupings in a file system folder and stored in the repository. End users can easily browse the tree structure and access data models, but there are no search facilities. Metadata for end users End users can only view metadata such as name, author, date created and a textual description of the data model. But power users designing models can access structural metadata using the Open Metadata Interpreter (OMI) functions in the query section. Wizards are provided for easy connection to metadata sources. Changes to metadata are automatically propagated to the query tool at runtime. Annotation by end user End users cannot annotate the data model directly. However, they can easily annotate any reports that are created from the model.

Using the model Basic OLAP functionality OLAP functionality is provided in the Pivot section. Standard OLAP functions, such as pivot, slice-and-dice and drill-anywhere and drill-out (to a

linked data model), can easily be performed on data using drag-and-drop. The outliner panel is used to change the nesting of dimensions. Colour-coded ‘spotlighting’ can also be defined for exception reporting. For flexible OLAP query, Brio supports two unique features: • member selection – another way to limit data retrieved from the server cube. Member selection is similar to the Slicer, with the key distinction that it introduces the member value in a report and multiple members may be selected from a single hierarchy level skipping – BrioQuery allows an OLAP query to be built that skips one or more levels in the dimensional hierarchy; other OLAP query tools usually require queries to include all levels of a dimension from the top down to the desired level of granularity. Changing the position of members in a dimension level A menu is available to sort data in ascending and descending order according to different calculations (for example, count, average or sum). Visualising the drill-down hierarchies Drill-down icons in the Outliner panel show which dimensions have drill- down operations. An overall representation of the hierarchies is provided, but there is no support to show the user’s position within it. Drillingdowntodetaileddata The ‘drill-out’ option allows end users to drill to detail-level data. A useful feature is incremental drilling, which combines predefined drills and drill-to- detail, so that end users can navigate relational databases in a similar way to navigating through a multidimensional cube. However, users can only drill within a single database – not across multiple databases. Range of front-end user tools Brio’s range of end-user tools provides cascading levels of functionality for different types of end user. An Excel add-in facility is provided, but only to access documents and load results into worksheets. Custom EIS front-ends can also be developed using the integrated development tools. Visualising the results The client tools support standard cross-tabular and band-style reports. Additionally, more than ten graphical charting options are provided, including pie charts, dual Y axis, scatter and line-bar combo charts. Charts include drill-down, rotation and zoom functions. Compound reports containing charts alongside tabular data can be created. An optional EIS tab allows for the controlled display of data in a ‘briefing book’-style interface. There is no support for displaying data in maps.

Saving and sharing results Designing a report The Brio reporting tools are easy to use and support advanced report construction. The ‘Reporter’ tool supports free-form WYSIWYG report design and allows multiple results sets from different data sources to be included in a single report. But there is no support for embedding OLE objects in reports. The EIS tab allows for the creation of EIS-style reporting interfaces.

Publishing a report The ‘report bursting’ option in Broadcast Server allows users to publish a single query and have the result tailored for, and delivered to, different people or divisions within the enterprise. Results data can be managed and distributed according to different rules and criteria. End users can also publish HTML reports for web access, using an HTML wizard and report templates. Web users also benefit from adaptive reports. Targeted distribution via e-mail Broadcast Server integrates with Microsoft Exchange and other MAPI or SMTP-based e-mail systems. E-mail can be used to distribute documents or for notification purposes (for example, to include a URL reference for an HTML report), but dynamic distribution lists are not supported. Subscribing to reports Brio Enterprise does not provide any direct support for report subscription. Building the business model

Summary

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The process of building a business model is split between creating the data model by mapping dimensions to database tables and columns, and then using the model to query and analyse data. Easy-to-use graphical tools are provided for both processes. However, model designers are expected to have a good understanding of the underlying table and join structures – here the tools would benefit from some wizard support. The functions provided are geared towards general business modelling, rather than building highly complex models – a single Brio query can only access data from a single database. Designers are also restricted by the lack of complex calculated measures that can be defined in models.

Basic design Design interface BrioQuery Explorer and Designer support a graphical workspace for building data models. Modelling tasks, such as mapping topics and items to database tables, columns and defining joins, are achieved by point-and-click. Visualising the data source The table catalogue provides a graphical display of the source tables, columns and data types. When tables are moved to the design workspace, the relationships (joins) between them are graphically shown. It is also possible to bring up a detail view of sample data. Universally available mapping layer A ‘master’ data model can be developed to provide an initial mapping layer, upon which subsequent query and analysis is based. The master data model provides a visual representation of the database, using familiar business terms that cannot be changed by users. Any query section that refers to the

‘master’ data model will automatically inherit changes made to the main data model. Prompts for metadata When loading a data model into the repository, end users are prompted to include metadata information, such as model type, author and a textual description.

Building the dimensions Selecting columns for the dimensions Columns can be selected via point-and-click. Selecting the members shown in a dimension level A list of member values can be selected using point-and-click. If the source is a relational database, the list will be filled by a Select SQL query. If the source is a MDDB, then the native APIs are used to show the member values. For OLE DB for OLAP-compliant OLAP servers, MDX is used. Defining a dimension hierarchy By default, there are no predefined hierarchies; end users are free to drill however they wish. But optional support for loading predefined hierarchies is available for more restrictive drill-paths. A check-box interface is available for marking a topic as a dimension and arranging the order of columns (top to bottom) to create a drill hierarchy. The Pivot tab supports a dialogue for defining custom levels. The definition of unbalanced dimension hierarchies is supported via point- and-click. Time dimension Time dimensions are not automatically recognised. However, designers can create month, quarter and year dimensions from a date field. Custom time periods can also be defined. Annotating the dimensions It is possible to substitute descriptive names for arcane database table and column names when creating dimensions. Simple data transformations (such as replacing underscores with spaces and displaying dimension names in mixed upper/lower case) can be automatically performed. However, it is not possible to provide short and long names for dimensions for different display purposes. Default level of a dimension hierarchy Predefined hierarchy levels are supported, but there are no facilities for specifying a default level when opening a report.

Defining the measures Calculated measures Complicated measures, such as cost of sales or profit margin, rely heavily on DBAs anticipating these requirements when designing the database structure, so that the database makes calculations as the raw data is queried. It is also possible to supplement calculations already stored in the database by creating computed items (calculated measures), which can be included as

part of a data model. The computed item is a value, variable, logic statement or formula that instructs the Brio client or the RDBMS to perform a certain calculation. Standard arithmetic and logical operators can be used to create computed items, either by typing into a formula panel or via point-and-click. Scalar functions, which calculate and substitute a new data value for each value associated with a data item, are also supported, although some are provided by the RDBMS. Support for multiple measures with a set of dimensions Up to 20 measures can be associated with a set of dimensions.

Multiple designers Multiple designers There is no special support for multi-designer environments. Support for versioning The repository provides a central, version-controlled database store of data models. Advanced analytical power

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Brio Enterprise provides only basic mathematical and statistical functions. For advanced analytics, it relies on exposing and exploiting native database functions (for example, RISQL functions in Red Brick) and builds analytical complexity into the SQL query itself. When connecting to third-party OLAP servers, the range of analytics available is determined solely by the MDDB. Custom functions can be built using a scripting language, but there is limited scope for re-use. Simple links to spreadsheets are provided for importing results data, but data cannot be queried directly from specialist analysis tools.

Third-party tool integration There is no integration with third-party statistical, forecasting and analysis tools or spreadsheets. However, a scripting language can be used to pass data back and forth between Brio clients and third-party applications.

Defining specialised models Ranking and sorting Simple descending/ascending ranking functions are provided. More complex ranking can be implemented in reports by adding custom measures using ‘if, then, else’ logic. Additionally, native database ranking functions found in Red Brick and Oracle can also be exploited. Mathematical methods A range of functions is provided, including standard arithmetic operators and absolute value, cosine, sine, exponential power and hyperbolic sine.

Financial functions There are no special financial functions. Statistical models The Brio client tools support basic statistical functions, such as median, mode, percentile, standard deviation and variance. More advanced statistical functions are only available through the Oracle RDBMS. Trend analysis There are no special functions for analysing trends. Simple regression Regression functions for forecasting are not available. Time-series forecasting There is no support for advanced time-series forecasting methods.

User-definable extensions Users can build or extend functions by using the existing arithmetic, logical and scalar functions and by exploiting native database functions through the SQL. JavaScript has been included as a function language within documents – JavaScript expressions can be written to define more complex computed columns in reports. But these functions can only be re-used within a single document – there is no scope for storing them in a central repository for wider re-use.

Write back for ‘what if?’ analysis Direct write-access to the database is not supported. A ‘process-to-table’ feature can be used to run a query against the RDBMS, and instead of pulling the results set to the Brio client, it will create (or append to) a table. This can be used as a simple transform or for staging results for complex reporting.

Incorporating non-numerical data It is possible to add alpha-numeric text fields as measures. By changing the calculation type to count-mode, end users can analyse non-numerical data; for example, how many people answered ‘red’ or ‘blue’. Non-numeric functions include non-null average, count distinct, null-count and non-null count.

Data mining Brio Enterprise does not support data mining.

Web support

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Brio.Insight and Brio.Quickview come very close to reaching functional parity with the client-server tools. Both sets of tools share the same drag-and-drop interface for query, OLAP and reporting, and access the same document repository. Modelling functions are not available via the Web. However, the web configuration introduces a mid-tier server for ad hoc querying and benefits from the Adaptive Reports feature for flexible reporting and distribution. Generally, the dynamic web publishing (via plug-ins) is more suited to intranet environments rather than extranets.

End-user functionality via the Web Functionality of web access to explore models The OnDemand Server and web clients fully support OLAP query (including dimensions and measure tree navigation), all query features and the ability to define local calculations in reports. The only difference is that query processing is in ‘process’ mode – a live ‘hardwire’ mode is not available. There are two types of web client, each offering different functionality: Brio.Insight, which provides web users with access to a repository of predefined data models and reports created by an administrator. The functionality is weighted towards ‘information consumers’, but through an HTML interface. It uses a drag-and-drop interface and includes the ability to access OMI-sourced metadata, create new queries, reports and charts and schedule documents. The only restriction is that it does not support drill-out Brio.Quickview, which provides a similar level of functionality as the BrioQuery Navigator tool, but through an HTML interface. Supports both registered and unregistered web access All Brio web users must be pre-registered. Range of users supported by the web interface The Brio.Insight interface provides capabilities for interactive query, OLAP and reporting. The Brio.Quickview provides an EIS-type interface for users that only wish to access and view predefined reports.

Creating models via the Web Editing the mapping layer There is no support for editing data models via the Web. Building and editing models Brio.Insight and Brio.Quickview can use, but not create, data models.

Distributing via the Internet and the Web Generate HTML and Java Brio clients can publish a report in HTML format. Charts can also be exported as HTML with links to JPEGs. An HTML wizard is provided to guide users through the process using custom web templates. Corporately organised distribution via the Internet Broadcast Server can be used to schedule and distribute ‘weekly’-style reports to individuals or groups of users via the Web. The HTML wizard can be used to create personalised web pages that allow end users to access a defined set of reports, each of which can be assigned its own analysis and formatting privileges for security. Include URLs in a report E-mail notifications can include multiple URLs, which can reference other Brio reports. But URLs are not supported in Brio reports.

Distribution of web server processing Processing can be distributed across multiple web and OnDemand servers. However, intelligent load balancing is not supported.

Other web support features Common security layer The web access levels are secured in the same way as they are handled in the client-server version. Brio supports a user-defined Java Bean authentication feature that enables integration of existing security with the OnDemand Server, which allows administrators to maintain a single source of log-in authentication. SSL support Brio web clients support SSL for all OnDemand Server communications with SSL-enabled web servers, including reprocessing a query to retrieve new results sets. Management

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Brio Enterprise provides flexible scheduling capabilities, but has an inconsistent server administration utility that is limited and cumbersome to use. Brio clients can register documents for web access via the OnDemand Server, but can only schedule them using the Broadcast Server or BrioQuery Designer administration tools. Also, when a report has been modified, it needs to be re-registered in order to see the modifications. Model security relies on the databases supplying the data, although levels of security can be applied via group, user and report metadata definitions. Strong monitoring and governance facilities are supported for analysing user activity, the utilisation of models and query duration and volume.

Management of models Separate management interface The concept of a ‘master’ data model lends itself to the centralised deployment and management for data models. The management of data models in the repository is carried out graphically using the BrioQuery Designer interface. The OnDemand and Broadcast Servers share a graphical administrator interface for defining end-user security and managing system-level settings. Security of models The security of models relies on the underlying database security schemes. Brio Enterprise focuses security on the document repository and the distribution layer. Query monitoring A SQL log monitors all queries, including all SQL statements generated and usage activity (such as the number of rows returned). The SQL shown can be edited to optimise the performance of a frequently requested query. Additionally, an auditing feature allows administrators to collect usage statistics about data models stored in the repository. The information can include how long queries take to process and which tables and columns are used most often.

Management of data How persistent data is stored (not scored) Persistent data is stored in document files either in the repository (which can be a relational database), in a remote file server or locally on the client. Scheduling of loads/updates The loading of data into the data warehouse is beyond the scope of Brio Enterprise. Broadcast Server provides graphical scheduling tools for periodic and batch-style processing and data refreshes. Schedules can be based on timed intervals (ASAP, daily, weekly, monthly, quarterly or custom) or user- defined events, and are supported by e-mail notifications. Event-driven scheduling Event-based scheduling is supported through the polling of data sources. The Broadcast Server can be triggered to refresh reports based on external events, such as the completion of an update to a data warehouse or a business exception rule. Failed loads/updates All report processing activity, including errors and failed refreshes, is monitored and logged in a job repository. Administrators and end users can be notified (via e-mail or pager) upon the completion or failure of a report processing activity. However, there are no automatic retry functions. OnDemand Server does support failover. If processing fails on one server, it is automatically re-submitted to another server in the cluster. Distributionofstoreddata Documents can be stored centrally in a repository (database server), on a remote file server or locally (on the client machine).

Sparsity (only for persistent models) Sparsity handling is the responsibility of the data warehouse or MDDB that Brio Enterprise connects to for data. Methods for managing size The decisions about aggregates and indexing are the responsibility of the data warehouse. Data returned to the client (DataCache) and documents are stored in a compressed format, typically between one-third and one-tenth of the original size. The typical size of a DataCache is around 100,000 rows of data. In-memory caching options The DataCache automatically manages in-memory caching. Informing the user when stored data was last uploaded The results data of a query is time-stamped to show when the data was last refreshed. Additionally, when querying data, end users can view metadata that shows the last time the data warehouse was updated.

Management of users Multiple users of models with write facilities Brio Enterprise does not support direct multi-user write-back facilities to the database. User security profiles Defining user and group-level security is maintained through a graphical tree-like interface. Interaction levels and group assignments can be easily administered via point-and-click. Query governance Query governors are available to set controls on the time a query takes to perform and the number of (unique) rows returned to the client. Restricting queries to specified times There is no support for restricting end-user queries to particular times of the day, but a query-sizing feature is provided to query the database and to show how many records a query will retrieve – this is useful for testing a questionable query and postponing processing of large results sets during peak network periods.

Management of metadata Controlling visibility of the ‘roadmap’ Brio Enterprise relies on model and user security assignments to control the visibility of metadata.

Adaptability

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Brio data models are quite adaptable to change. New dimensions and measures can easily be added to data models. Adaptability is greatly enhanced by the OMI feature, which dynamically maps existing metadata from a range of back-end data sources. This ensures that the data source, data models and metadata are kept synchronised. Data models stored in the central repository are automatically updated to reflect changes in the source database. However, support for impact analysis and metadata audit trails is not provided.

Change in business requirements Adding new dimensions to a model New dimensions can easily be added to a data model at any time via drag- and-drop. If a model is updated in the repository, documents based upon it are automatically updated. However, users are not provided with details of the changes. Re-use of dimension definition Dimension definitions are contained in the data model and can be re-used. Adding new measures to a model New measures can easily be added to data models. Re-use of calculated measure definition Measure definitions are contained in the data model and can be re-used. Changing the architecture to reflect business needs There is no direct support for changing the architecture to a full MOLAP or ROLAP mode. However, Brio Enterprise can implicitly link into both these environments. Mobile architectures are well supported.

Changes to data sources Keeping the data source and model schema synchronised A database synchronisation feature is available to keep data models stored in the repository, in line with changes to the source database. An itemised list of the changes made is provided. Automatic updating of members in a dimension Dimension members can automatically be updated when there is a change in the data source. Changes to master data models are automatically reflected in dependent reports.

Metadata Synchronising model and model metadata The OMI feature ensures that changes in upstream metadata remain synchronised with data models. However, descriptive metadata input when loading a data model remains unaffected and must be manually updated. Impact analysis There is no support to inform the administrator of the effect on documents and reports when there is a change in the structure of the data warehouse. Metadata audit trail (technical and end users) An audit trail showing changes to the history of the metadata is not available. Generally, the auditing capabilities provided relate to usage of data models. Access to upstream metadata Brio’s Open Metadata Interpreter (OMI) reads and interprets existing metadata from most of the leading data warehousing tools. The OMI link enables model designers to view extraction and transformation metadata, as well as descriptive information and naming conventions about tables and columns in the data warehouse to help them build data models. Metadata is propagated so that Brio reports that run against old metadata can update themselves (although some user intervention is usually required). Integration is provided with a variety of data warehousing tools, including Informatica’s PowerMart Suite, HP Intelligent Warehouse, IBM Visual Warehouse, Ardent DataStage, Broadbase, Logic Works Universal Directory, Carleton Passport, Pine Cone and Prism Warehouse Manager. Snap-in and customisable metadata templates are also provided for several leading metadata vendors. Performance tunability

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As Brio Enterprise effectively operates as an MDDB cache to a relational database, it can be difficult to understand how to optimise such a system, since both the underlying database access and the access into the cache can be optimised. For example, dimensionality can be handled in the database schema or an alternative dimensionality can be layered in the desktop multidimensional cache. For the analysis of small datasets’ performance, this is not an issue. However, Brio Enterprise’s desktop architecture does impose limitations on the size and complexity of models it can analyse on the client. The web configuration supports a more scalable three-tier architecture – the OnDemand Server supports SMP and can easily be scaled across a cluster of multiprocessor servers for simple load balancing and failover support. Administrators should give particular consideration to how best to use Broadcast Server alongside OnDemand Server to load balance queries and improve overall performance of the system.

ROLAP Multipass SQL Multipass SQL is not supported. Options for SQL processing Processing can be carried out on the database server or the Brio server, depending on where the calculations are defined. For example, measures added to the query section will be processed by the RDBMS and measures added to the results section will be performed by the Brio server. Brio Enterprise also supports native DB2, Teradata and Red Brick functions for more sophisticated processing on database servers. Speeding up end-user data access The DataCache can be stored in a document and subsequently retrieved for optimal query performance. The DataCache is time-stamped to show the currency of the data. Aggregate navigator Brio Enterprise relies on aggregate awareness implemented in the target database. There is no native aggregate navigation provided by the Brio tools.

MOLAP Brio Enterprise is not a MOLAP tool.

Support for multiple users Typically, OnDemand Server supports 200–300 users. Performance can be significantly enhanced if dedicated hardware is used for the OnDemand Server and the repository resides on a separate database server.

Processing Use of native SQL to speed up data extraction Native access is supported for Oracle, Sybase (Adaptive Server), Red Brick and Informix (Dynamic Server and MetaCube). Brio Enterprise uses ODBC to access other relational data sources. Distribution of processing A cluster of OnDemand Servers can be configured and multiple queries can be routed across the servers for simple load balancing. Each cluster is comprised of a manager and one or more nodes – the distribution is ‘round robin’ across all the active nodes in the cluster. Load balancing can also be achieved for Broadcast Servers, although this requires significant programming. SMP support The OnDemand Server is multi-threaded and supports SMP parallelism.

Customisation

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Brio Enterprise is primarily an out-of-the-box solution, but graphical development tools are also provided to simplify the construction of simple analytic applications. The development environment uses a combination of drag-and-drop and scripting (JavaScript). Third-party development tools can be used via OLE automation. Development is object-based, rather than object-oriented and geared towards the construction of analytic EIS-style interfaces, rather than complex OLAP applications that require specialised functionality.

Customisation Option of a restricted interface The Brio clients are essentially the same program, but with different features disabled, based on their intended target audience. The Adaptive Reports feature offers users five levels of interactivity, depending on the user’s profile and the document’s profile. Ease of producing EIS-style reports The EIS tab section, included in BrioQuery Designer, supports a development environment for building graphical front-ends and electronic dashboards using a combination of drag-and-drop user interface controls and JavaScript. Layout tools are provided for embedding objects such as bar charts, hot spots, graphics or ‘top seller’ lists on a screen to be viewed by high-level users. Data on the screen is live and updated regularly.

Applications Simple web applications Simple reporting applications can be developed for the web clients by using the development and JavaScript scripting facilities provided. Development environment The EIS tab section provides graphical and scripting tools for development. Report components and user application objects can easily be assembled on screen using drag-and-drop. A number of standard user interface controls (radio buttons, check boxes, list boxes and so on) are provided. A scripting language (JavaScript) is available to manipulate Brio application objects and build functionality into the application. The scripting editor supports basic debugging and testing facilities. The development environment is object-based rather than object-oriented – which lends itself to simplicity and ease of maintenance.

Use of third-party development tools The Brio object model is accessible via OLE automation allowing the BrioQuery application to be controlled by third-party development tools such as Visual Basic, C++, Delphi and PowerBuilder.

Other customisation features Proprietary scripting A proprietary scripting language can also be used to script Brio client applications from external applications; special add-ons are provided for Microsoft Excel and Macintosh HyperCard applications. Generally, scripting from other applications is simply a matter of establishing communication (via DDE or AppleEvent) between the client application and the Brio client, which acts as a ‘server’. Scripts can also be sent directly to the Brio client running in a Unix Motif environment, using a command line parameter. Localisation Brio Enterprise is available in five different language versions and includes support for double-byte character sets (DBCS).

Deployment

Platforms Client BrioQuery clients (Designer, Navigator and Explorer) run on Windows 3.1, 95, 98 and NT workstation, Apple Macintosh and Unix Motif. Brio.Insight and Brio.Quickview run on Microsoft and Netscape web browsers using their plug-in APIs. Server The Brio Enterprise servers (Broadcast Server and OnDemand Server) run on Windows NT and Unix (HP-UX, Solaris and AIX). OnDemand Server works with a variety of web servers (including Apache web servers on Unix) via ISAPI, NSAPI and CGI. Data access Brio Enterprise provides native access to Oracle, Sybase, Red Brick and Informix database servers. ODBC access is provided for other relational databases, including IBM DB2, Teradata, Microsoft SQL Server, QueryObjects (CrossZ) and White Cross. It can also connect to third-party OLAP servers. Native access is provided for Hyperion Essbase and IBM DB2 OLAP Server (both accessed via the GridAPI), Informix MetaCube and SAP BW. OLE DB for OLAP access is also provided to connect to Microsoft SQL Server 7.0, OLAP Services, NCR TeraCubes, SAS, WhiteLight and Applix TM1. Additionally, Brio Enterprise can also be accessed from knowledge repositories, such as Sqribe (ReportMart) and VIT (MetaWarehouse). Access to SAP R/3 is provided by Acta Technology’s RapidMarts for SAP. Integration with other ERP systems is planned. Standards Brio Enterprise supports Microsoft’s OLE DB for OLAP as a consumer. Published benchmarks Brio Enterprise has conducted its own internal benchmarking tests for OnDemand Server – the results will be published by the end of 1999. Price structure Pricing for Brio Enterprise Server is $32,495 for Windows NT and $44,995 for Unix systems. Both editions include OnDemand Server and Broadcast Server, Brio Enterprise Administrator and ten named-user licences for the Brio.Quickview web client. All server and client components are also separately available; OnDemand Server costs $19,995 for Windows NT and $29,995 for Unix; Broadcast Server costs $14,995 for Windows NT and $19,995 for Unix. Pricing for individual Brio client tools ranges from $50 for Brio.Quickview, up to $3,995 for BrioQuery Designer.

Summary

At a glance ...... 2 Terminology of the vendor ...... 3 Ovum’s verdict...... 4 Product overview ...... 5 Future enhancements ...... 11

Commercial background

Company background ...... 12 Distribution ...... 13

Product evaluation

End-user functionality ...... 14 Building the business model...... 15 Advanced analytical power ...... 17 Web support ...... 18 Management ...... 20 Adaptability ...... 22 Performance tunability...... 23 Customisation ...... 24

Deployment

Platforms ...... 26 Data access ...... 26 Standards ...... 26 Published benchmarks ...... 26 Price structure ...... 26 Evaluation: Business Objects – BusinessObjects Ovum Evaluates: OLAP

At a glance

Developer Business Objects, twin headquarters in Paris, France and San Jose, USA Versions evaluated BusinessObjects version 4.0, comprising of the BusinessObjects user module, BusinessObjects Designer, BusinessObjects Supervisor, Document Agent Server, BusinessQuery and BusinessMiner; and WebIntelligence II version 2.0 Key facts • A client-based tool that provides OLAP query, analysis and reporting • Runs as a Windows 3.1. Windows 95, Windows NT, Unix Motif or Java client; servers run on Windows NT and Unix • Business Objects has re-architected its product lines to support a 32-bit component environment, and has made significant enhancements for web access Strengths • An easy-to-use ‘out-of-the-box’ tool • Flexible OLAP query, analysis and reporting available via the Web • Graphical set of IS tools for creating the mapping layer and deploying OLAP across the enterprise Points to watch • The mid-tier Document Agent Sever component lacks the capabilities of a full OLAP engine • Potential scalability issues for large result sets and complex OLAP calculations • Little support for custom application development Ratings 12345678910

End user functionality

Building the business model

Advanced analytical power

Web support

Management

Adaptability

Performance tunability

Customisation

Terminology of the vendor Business Objects – BusinessObjects Note the difference between the company name and the product name. Business Objects denotes the company, whereas BusinessObjects is the name of its product. Business objects Equivalent to dimensions and measures in OLAP terms. A combination of business objects is used to create a multidimensional business model. Document A container for a multidimensional model that can be stored persistently on a server for scheduling and distribution. Microcube A local multidimensional data structure that allows users to perform OLAP analysis. A microcube is created ‘on the fly’ from a query and is viewed in a report. It corresponds to Ovum’s definition of a multidimensional business model. Semantic layer A mapping layer that provides a representation of the database using familiar business terms (such as ‘customer’ or ‘sales’). Report Provides a graphical interface that can be used directly as a starting point for multidimensional analysis because it is connected to the microcube that holds the data. Universe Hosts ‘classes’ of business objects. Each universe corresponds to the needs of a particular group of users, an application or a department.

Ovum’s verdict

What we think Building on its established strength of providing flexible access to corporate data, the most distinguishing feature of BusinessObjects is its ease of use. The tool provides ‘out-of-the-box’ functionality for the masses of general business users, rather than analysts seeking high analytical functionality, or customers seeking to develop specialised OLAP applications. The architecture supports a well designed mapping layer that shields end users from the complexities of the underlying data sources. This provides easy and flexible access to corporate data using familiar business terms. The ‘dynamic’ nature of BusinessObjects’ multidimensional models means that users can easily direct queries to additional data during analysis in an ad hoc way. The BusinessObjects graphical designer and administration tools have also been implemented with an emphasis on usability, and specifically address the problems of rolling out BusinessObjects to large numbers of users. Business Objects has made significant strides in the area of web enablement. Its WebIntelligence II product provides one of the strongest web interfaces in the OLAP market, and is closely integrated with the BusinessObjects tools in terms of end-user functionality and infrastructure. The main challenge for Business Objects is to build on its early success in the low-end business intelligence market to capture share in an ‘enterprise’ space that demands high performance and scalability. The BusinessObjects’ mid-tier component provides a range of report processing services for scheduling and distribution. But it does not constitute a full mid-tier OLAP server. Customers with queries that return large data sets, and require complex OLAP calculations must therefore integrate with third-party OLAP engines, or license specialist technology to support this capability.

When to use BusinessObjects is suitable if you: • want ‘out-of-the-box’ functionality • want to empower hundreds of general business users with query, analysis, reporting and data mining via an integrated interface • want to support ad hoc queries to a range of data sources via a web browser • require quick and easy deployment across the enterprise. It is less suitable if you: • want to provide advanced analysis and forecasting functions, without having to integrate with third-party technology • want to build highly customised OLAP applications • intend to build large, dimensionally complex business models that require extensive OLAP calculations.

Product overview

Components BusinessObjects is comprised of the following end-user tools and IS tools: • BusinessObjects user module version 4.0 • Designer version 4.0 • Supervisor version 4.0 • Document Agent Server version 2.0 • WebIntelligence II version 2.0 • BusinessQuery version 4.0 • BusinessMiner version 4.0. Figure 1 shows the primary functions of the components and whether they run on the client or the server. BusinessObjects is a client-based tool that provides ad hoc OLAP query, analysis and reporting capabilities from a PC. A web configuration pushes most of the processing to a mid-tier application server. BusinessObjects works directly against relational data warehouses or datamarts, and data providers enable access to non-SQL sources such as ERP applications and third-party multidimensional databases. BusinessObjects provides a central repository for metadata definitions, models and reports. The tool also supports a Visual Basic-like scripting language called ReportScript for customisation. BusinessObjects user module The user module consists of the Reporter and Explorer components. It provides a graphical drag-and-drop user interface to access corporate data via an integrated querying, reporting and OLAP environment. The BusinessObjects Reporter component allows users to dynamically query the database and define their own reports. Queries are expressed via a query panel, and use a mapping layer to represent the data stored in the database. End users simply combine business objects to create native SQL or ODBC calls to the database server to retrieve data.

Figure 1 Component functions

OLAP OLAP Web access Data mining Distribution Management design analysis & security

Client Designer BusinessObjects WebIntelligence II BusinessMiner Supervisor user modules

BusinessQuery

Server WebIntelligence II Document Agent Server

When data is downloaded into a report, it is stored locally on the client as a multidimensional data file, called a ‘microcube’. The BusinessObjects Ex- plorer components provide multidimensional analysis capabilities directly from the report. If a further level of detail is requested for drill-down to data (for example, for it to be displayed along another dimension), the microcube can be expanded or moved dynamically. Designer A graphical DBA tool for defining classes of business objects (equivalent to dimensions of a business model) that map to the source database; Business Objects refers to this mapping as its ‘semantic layer’ (for which it holds a patent). The semantic layer allows end users to view data using familiar business terms. Every BusinessObjects client holds a local copy of the mapping layer. How- ever, to simplify distribution and administration it is also possible to store it centrally in a repository within the database, from where it is available to all end users. Supervisor A graphical administration tool for managing end users and system resources. An object-based security model lets administrators assign and modify the rights granted to groups of end users. User profiles include access privileges to the mapping layer, reports and individual menu functions. Additionally, the size of the result of a query, or the query execution time, can be limited for end users. Document Agent Server A mid-tier server component that provides scheduling and batch processing facilities for distributing microcubes and report definitions as stored ‘documents’. When an end user submits a document for processing, it is stored as a pending job in the BusinessObjects repository. Document Agent Server checks the pending jobs, either periodically or conditionally, and retrieves them for processing as scheduled. When it has finished it places them back in the repository for retrieval via the BusinessObjects user module or a web browser. Document Agent Server can also perform tasks such as sending documents via e-mail or to a web server in HTML format. The server supports its own administrator interface for modifying the scheduling, priority settings and distribution of processed documents. WebIntelligence II Enables query, OLAP analysis and reporting functions from a Java-enabled web browser. WebIntelligence II includes an object request broker for the server and a Java applet for the browser. A web query panel is used to create new reports on-the-fly; this is downloaded to the web browser as a Java applet, and includes a copy of the mapping layer. Web users are provided with a range of report formatting options and can also add their own simple calculations to report data. The interface is based on a personal user homepage, from which end users can access personal and shared documents, or targeted reports can be sent to an ‘inbox’ via the Document Agent Server.

BusinessQuery (optional) A Microsoft Excel spreadsheet add-in that lets end users pull data directly into Excel spreadsheets using the terminology from the BusinessObjects semantic layer. BusinessMiner (optional) An end-user tool that provides data mining facilities. It uses decision-tree algorithms to graphically depict hierarchical relationships in data.

Architectural options Full mid-tier architecture WebIntelligence II is a thin client implementation of BusinessObjects that extends the architecture to incorporate a more powerful application server that comes closer to the functions expected of a ‘full’ mid-tier OLAP server. Significantly, this configuration moves most of the BusinessObjects code from the client to the WebIntelligence II application server. WebIntelligence II is based on the use of Java applets for ad hoc queries. By downloading a web panel and a local copy of the mapping layer onto the web client, users are provided with a similar level of functionality to the BusinessObjects desktop client. WebIntelligence II runs as a set of centrally managed software components. It has a distributed component architecture (DCA) that supports multiple copies of the server components across different web servers. DCA is implemented using Corba technology licensed from Visigenic Software. Light mid-tier architecture This architecture includes the addition of a mid-tier report server component called Document Agent Server. The configuration allows users to run queries in real-time, or schedule the batch processing of documents on the Document Agent Server; the server can run on either a separate application server or on the database server. Document Agent Server is a ‘light’ mid-tier server that provides report processing services. It does not support a static MDDB, nor does it offer the powerful processing functions expected of a full OLAP server engine. End users do not communicate directly with the Document Agent Server, but the BusinessObjects repository, which stores all the processed documents. There is no caching or storage of model data on the Document Agent Server itself. A web server can be added to provide web publishing capabilities. The web server uses a CGI interface to communicate with the Document Agent Server to distribute standard BusinessObjects reports published in HTML format. Selected reports can be downloaded with an associated microcube, which makes further analysis on the desktop possible if the BusinessObjects client is also installed locally on the recipients’ client. Desktop architecture The ‘natural’ BusinessObjects client-server architecture is a two-tier desktop architecture, based on a full BusinessObjects client that interfaces with one or more relational databases. BusinessObjects supports a number of differ-

ent schemata, including normalised tables, star, snowflake, multistar and data warehouses with aggregates. The desktop architecture uses a local copy of the mapping layer stored on the client. Using this mapping layer, an end-user formulates a query and sends it to the desktop query generator, which automatically generates SQL (joins, group byte, multipass SQL statements and other SQL clauses) and sends it to the database server. After processing the request, the database server returns the results data back to the client where it is cached as a ‘dynamic’ microcube. Microcubes are local multidimensional data files created ‘on-the-fly’, and contain a slice of data needed for a particular query. If the user drills down into the microcube to obtain more detailed levels of data, a new query is sent back to the database server. BusinessObjects can pull data from several sources into a single microcube. Multiple microcubes can be linked to allow queries to span across more than one microcube. Mobile architecture The BusinessObjects desktop architecture ‘naturally’ lends itself to a mobile architecture. While disconnected, users are restricted to the data stored in the downloaded microcube.

Using BusinessObjects Main concepts It is easier to understand the BusinessObjects approach to OLAP if three principal concepts – semantic layer, business objects and universes – are clarified first. The idea of providing users with a way to refer to corporate data in business terms lies at the heart of BusinessObjects. It is achieved by using the semantic layer, a centrally defined and controlled model of the underlying database. This provides a mapping layer that allows end users to view data using familiar business terms called business objects (approximately equivalent to dimensions or measures in OLAP terms). There are no restrictions on the way users combine business objects to create their queries. Business objects are ‘semantically dynamic’, which means that they retain their meaning in whatever combination they are used. For example, the object ‘sales revenue’ will report the correct amount whether used in conjunction with customer or product. The SQL statements needed to retrieve the data are automatically generated by BusinessObjects, with no awareness required on the part of the user. Business objects are organised in classes; for example, the class ‘customer’ might consist of different business objects, such as age, group or sex. Uni- verses consist of different classes and objects. Typically, different users use different universes for different purposes. Multiple universes, such as sales, personnel or inventory, can be created to meet the needs of different groups of users.

Figure 2 Designer graphical interface

BusinessObjects Designer

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Designing the mapping layer The mapping layer is typically created by DBA or IS staff using the BusinessObjects Designer tool. As illustrated in Figure 2, Designer employs a highly graphical interface to create a schema which defines how users see the database. As well as draw- ing and printing the database schema, the tool also includes routines for automatic design checking, including loop detection and resolution. A Quick Design Wizard is also provided for the rapid creation and maintenance of new schemata. Flexible modelling and analysis Users either work with predefined models, or build their own ‘on-the-fly’ by directly querying the database. Users start by selecting an appropriate universe that contains dimensions and measures specific to their business problem. Users simply drag across appropriate dimensions and measures in a query panel. This is shown in Figure 3. When a user issues a query, data is delivered to the client in the form of a ‘dynamic’ microcube (a model, in Ovum’s terms) that can be analysed using standard OLAP functions such as drill-down and slice-and-dice.

Figure 3 Query panel

Query Panel

Requested information

Available Information

Conditions

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The model is dynamic and is typically stored as part of a document. Models can be expanded dynamically when a user requests to view data along another dimension or wants to drill-down. By navigating through the model in this way, users can view all the data to which they have access. The whole process of creating, moving or changing a microcube is completely transparent to the user, who only works with the business representation of corporate data. Users can enhance the model with additional calculations, and create reports on the data contained within it. Users can also link and incorporate related data from other data sources into the model.

Future enhancements The next major release of BusinessObjects is due in early 1999. The new version will provide a number of enhancements, including: • a toolkit for writing custom data providers • a set of Active X controls for custom application development • integration with a wider range of metadata sources, including the Microsoft repository.

Commercial background

Company background History and commercial Business Objects was founded in 1990 by two former Oracle employees in Paris, France. The company was modelled on the venture capital funded technology start-ups in the US. It attracted investments from venture capitalists in Silicon Valley and Europe, including the founding shareholders of National Semiconductor and Oracle. BusinessObjects was first released in 1991, and has since sold more than 870,000 licences worldwide. In 1994, 25% of the company was floated on the Nasdaq. The flotation raised more than $30 million in capital. In 1996, Business Objects decided to re-architect its product to support a 32-bit component-based architecture. However, delays in the introduction of a stable version 4.0 of the product caused a financial loss in the third quarter of 1996. Business Objects has now recovered from this hiccup and, with a major product transition now behind it, has returned to financial growth and stability. Business Objects’ revenues for fiscal 1997 grew 34% to $114.3 million. Business Objects has joint headquarters in Paris, France and San Jose, California, US. The company employs 800 people and has additional offices in North America, Europe and Asia-Pacific. Character and direction Business Objects has been a player in the reporting and analysis tools market for some years and has achieved great success in this market. Its OLAP products are positioned at the client tool end of the OLAP market, and are targeted at general business intelligence requirements. The idea of providing large user communities with a way to query, analyse and report on corporate data in familiar business terms lies at the heart of Business Objects’ philosophy. BusinessObjects is sold directly and through VARs and OEMs. It is positioned as a ‘neutral’ front-end component, providing an open back-end to access a range of data sources including third-party OLAP server engines (Microsoft SQL Server OLAP Services, Hyperion Solutions Essbase, Oracle Express and Informix Metacube). Partnerships are key to its OLAP strategy. The company has close relationships with all the major RDBMS vendors, data warehousing vendors and ERP application software vendors. For example, IBM bundles BusinessObjects as part of its Business Intelligence initiative. Business Objects also resells Informatica’s PowerMart Suite.

Customer support Support Business Objects offers a multi-tiered help-desk support system at a corporate and a field level. Support is provided via telephone hot-line and the Web.

Training Education services are available in several languages both in-house and on- site. Public courses are run frequently for end users, designers, administrators and supervisors. Computer-based training is also available. Consultancy services Business Objects’ consultants are mainly relational database specialists and are available to provide advice and development support on all aspects of product implementation. However, no significant portion of revenues is attributed to consulting, except in the UK where it accounts for 18% of revenue. Consulting projects include requirements for data access and the analysis of the relational database schema. Business Objects also has numerous consulting and referral partners.

Distribution US Business Objects 2870 Zanker Road San Jose CA 95134 USA Tel: +(1) 408 953 6000 Fax: +(1) 408 973 1057

Europe Business Objects 1, Square Chaptal 92300 Levallois-Perret France Tel: +(33) 1 41 25 21 21 Fax: +(33) 1 41 25 31 00

Asia-Pacific Business Objects Australia Suite 210 283 Alfred Street North North Sydney NSW 2060 Australia Tel: +(61) 2 9922 3049 Fax: +(61) 2 9922 3069

http://www.businessobjects.com E-mail: [email protected]

Product evaluation

End-user functionality Summary

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BusinessObjects’ main strength is its ease-of-use; it provides excellent ‘out-of- the-box’ functionality with little or no need for prior adaptation. It shields users from the complexities of SQL, providing access to data through an easy-to-use mapping layer. All OLAP functions are available through point- and-click, regardless of whether a user wants to analyse models or format a report. Reports can be shared and distributed by using the Document Agent Server, or via integration with e-mail systems. Finding and understanding the model Finding and loading a multidimensional model Generally, models are dynamic, and created on-the-fly. However, users can search for predefined documents stored in the BusinessObjects repository. Text string and keyword searches are supported for finding files. Metadata for end users In the context of BusinessObjects, most metadata is structural information about the database, and therefore relevant only for developers. However, developers have the option of including universe and object descriptions that can be displayed in reports. Annotation by the end user The end user cannot annotate the microcube directly, but can add textual annotations as part of a report. These annotations are accessible to all users of the report. Using the model Basic OLAP functionality BusinessObjects excels in its simple and intuitive graphical interface that integrates query, analysis and reporting from a consistent interface. All OLAP functions are easily achieved using either point-and-click or drag-and- drop. Changing the position of members in a dimension level The position of members in a report can be changed using point-and-click. Visualising the drill-down hierarchies An ‘explore’ option allows end users to navigate through the dimensional hierarchy. Drilling down to detailed data End users can drill-down to detail data held in relational databases directly from the BusinessObjects client. The detail data is freshly retrieved and viewed directly from a BusinessObjects report.

Range of front-end user tools The BusinessObjects query panel is embedded in specialist third-party tools such as Geoconcept, StatSoft and SPSS. This allows them to query and pull in data using the BusinessObjects mapping layer. Business Query is an add-in for Microsoft Excel that enables users to extract information from databases and load it into Excel cells for further analysis. Visualising the results Reports present data in tabular (standard and master/detail tables), graphical and cross-tab (matrix) formats. BusinessObjects supports a range of business graphs and charts. Users can also drill-down directly from graphs. Multiple charts and tabular data can co-exist on the same screen. Integra- tion with the Geoconcept tool allows GIS-type mapping to be applied to data. Saving and sharing results Designing a report Free-form reports can be designed from scratch by end users. A Report Wizard is provided. Alternatively, users can choose a predefined report template. Full production-style report formatting capabilities are provided. Reports can also contain data from different sources, including spreadsheets and OLE objects from external applications. Publishing a report Reports can be published and sent to the Document Agent Server for scheduled delivery. Targeted distribution via e-mail Reports can be sent via MAPI-compliant e-mail systems directly from the BusinessObjects interface. Reports can be e-mailed on a scheduled basis to groups of users by using a BusinessObjects script. But dynamically generated address lists are not supported. Subscribing to reports The Document Agent Server supports the conditional delivery of reports based on user-defined preferences.

Building the business model Summary

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The process of building a business model is split between DBAs (who create the mapping layer) and end users (who create reports by querying the database using the mapping layer). Easy-to-use graphical tools are provided for both types of user. The design tools provide extensive wizard support, and DBAs can readily exploit existing database schemas. Multi-designer environments are also well supported by concurrency and versioning controls.

Basic design Design interface The BusinessObjects Designer module provides a graphical interface for designing the mapping layer (the meta-model) and creating universes (end- user perspectives on the meta-model). The interface has a standard Microsoft Office 97 look-and-feel. A Quick Design Wizard is provided to guide developers through each step in the process, and includes facilities for design checking. Models are built using queries, and viewed and analysed in a report. Users simply drag-and-dropping important business objects in a familiar Microsoft Office-style interface to retrieve data. The query functions are also supported by wizards. Visualising the data source A graphical view of the database schema is provided. Sample data for a particular table or column can also be viewed on-screen. Universally available mapping layer BusinessObjects supports a mapping layer, which it calls the ‘semantic layer’. The universe defines a particular type of mapping for groups of end users. Prompts for metadata Developers and end users are not automatically prompted to provide additional metadata when creating the mapping layer or building models. All metadata inputs are optional. Building the dimensions Selecting columns for the dimensions Columns can be included and excluded selectively via point-and-click using the Table Browser facility. Selecting the members shown in a dimension level Dimension members can be selected using point-and-click or via SQL. Defining a dimension hierarchy Dimension hierarchies can be built using point-and-click. Hierarchies can be built using the existing database schema or by custom definitions. Time dimension Time aggregations need to be explicitly defined. Custom time dimensions can be built. But there is no direct support for dynamic period-to-date dimensions. Annotating the dimensions Designers can annotate dimensions with long name descriptions. These descriptions can be displayed in report and chart views. Default level of a dimension hierarchy It is possible to default a report to view specific dimensions and levels upon opening.

Defining the measures Calculated measures Calculations can be added to a model using a calculator-type interface. BusinessObjects provides a range of standard numeric and arithmetic functions and operators. Support for multiple measures with a set of dimensions It is possible to include multiple measures within a dimension. Multiple designers Multiple designers The Designer tool supports a centralised metadata repository with check- out/check-in facilities and concurrency controls; it provides locks on the mapping layer so that only one designer can modify a universe at a time. Support for versioning The metadata repository uses delta versioning.

Advanced analytical power Summary

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BusinessObjects is limited in its support for complex analytical functions. Generally, the range of native functions provided is geared towards general business analysis. Advanced functions such as statistical modelling and forecasting rely entirely on integration with third-party tools. A bonus is the integrated and easy-to-use data mining facilities provided by the BusinessMiner tool. Third-party tool integration Tight integration is provided with specialist third-party tools. Vendors have licensed the BusinessObjects Query Panel to access BusinessObjects data. These include SPSS, StatSoft and Geoconcept. A Microsoft Excel spreadsheet add-in is also provided. Defining specialised models Ranking and sorting Ranking and sorting functions are supported. Mathematical methods BusinessObjects supports standard arithmetic, trigonometric and logarithmic functions. Financial functions BusinessObjects does not support any financial functions.

Statistical models There are no statistical modelling facilities directly provided by BusinessObjects; this is supported via integration with SPSS. Trend analysis BusinessObjects supports simple period-on-period analysis. Apart from this there is no direct support for advanced trend analysis based on exponential smoothing or curve-fitting techniques. Simple regression BusinessObjects does not support any regression forecasting functions. Time series analysis forecasting BusinessObjects does not support advanced time series forecasting algorithms. User-definable extensions A variable and formula editor is available for creating simple user-defined analytical functions. Scripting is also available. Write back for ‘what-if’ analysis BusinessObjects does not support write back to the database. Incorporating non-numerical data Reports can contain textual data. But there is no direct support for the analysis of textual data sources. Data mining BusinessMiner is a data mining tool that can be fully integrated into BusinessObjects; it uses the same semantic and security layer as BusinessObjects and is accessed as an option on the BusinessObjects menu. BusinessMiner is client-based and uses decision-tree technology developed by Isoft to graphically depict relationships in data. BusinessMiner is suitable for general business users; the emphasis of the tool is clearly on ease-of-use, rather than on advanced data mining algorithms.

Web support Summary

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WebIntelligence II provides strong support for web access. It supports most of the OLAP query, analysis and reporting functions as its desktop counterpart, including the ability to create new models and define format report formats. WebIntelligence II also benefits from the distribution of processing across multiple servers for increased scalability and load balancing. The web tools share the same metadata layer and security as the client-server tools, allowing for integrated management.

End-user functionality via the Web Functionality of web access to explore models WebIntelligence II provides strong facilities to explore and modify models from a web browser. It provides the same level of OLAP functionality (including drill-down, slice-and-dice and pivot) as the desktop client through the web panel. The Web Panel is implemented as a Java applet that combines the functions of BusinessObjects’ query panel and slice-and-dice panel. WebIntelligence II supports standard functions (sum, count, average, min, max and percentage) that enable users to define and add their own calculations in web reports. Charting and report formatting is also available via the Web. Supports both registered and unregistered web access WebIntelligence II supports both registered and unregistered (guest) access. Range of users supported by the web interface The HTML and Java-based web interfaces support both casual users that need browse access to static HTML reports, and more advanced users that require a more interactive experience. Creating models via the Web Editing the mapping layer It is not possible to edit the mapping layer using the web tools. Building and editing models WebIntelligence II users can access the mapping layer to create new models and reports. The web panel is a Java applet that provides similar query and model building functions as the desktop tool’s query panel. Web users can therefore easily add additional data not stored in the microcube by initiating another ad hoc query via the Web. Distributing via the Internet and the Web Generate HTML and Java Reports can be saved in HTML format using point-and-click. Corporately organised distribution via the Internet Reports can be e-mailed over the Internet to corporate/workgroup folders or personal in-boxes. These reports can be accessed by registered WebIntelligence II via their personal homepage. Document Agent Server can also schedule delivery of reports via the Internet. Include URLs in a report Reports can include multiple URLs. Distribution of web server processing WebIntelligence II supports a distributed component architecture that uses CORBA-compliant technology from Visigenic Software. This allows for the distribution of processing across multiple web servers for load balancing.

Other web support features Multimedia objects can be embedded in web reports. Hyperdrill allows report cells to be hyperlinks that can drill out of a report into any Internet- based data sources.

Management Summary

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The BusinessObjects Supervisor provides excellent graphical management tools that ease the administration of reports, metadata and end users. The tool provides strong query governance mechanisms and supports a sophisticated security model to restrict user access to models and other application objects. But query monitoring is only available with WebIntelligence II and there is no support provided for tuning the BusinessObjects client cache. Management of models Separate management interface The management of reports and users is via the Supervisor interface. The mapping layer is managed using the Designer module. Both tools share a similar graphical interface, and most tasks are achieved using point-and- click. Security of models Administrators can set multi-level security controls for reports. Read-edit access can be specified on individual reports. Query monitoring The BusinessObjects client-server tools do not provide any graphical facilities to monitor queries; though this can be set up using event-driven scripts. WebIntelligence II, however, does provide an audit trail facility for tracking queries. Management of data How persistent data is stored (not scored) Data is stored in the document domain of the repository as a document file. The file contains the report definition and one or more microcubes (models). Data can also be stored locally on the client or a separate file server. Scheduling of loads/updates With Document Agent Server, users can schedule time-specific updates on an hourly, daily, monthly or custom interval basis. Event-driven scheduling Event-driven scheduling in Document Agent Server can be achieved through the use of scripts that check a specified environment variable before executing a schedule.

Failed loads/updates Failed loads automatically produce an error message and log file. Scripts can be set up to e-mail error messages to DBAs. Document Agent Server can reprocess failed updates, and administrators can specify the number of re- submission attempts. Distribution of stored data Data can be stored on the client, in the repository or on a file server. Sparsity (only for persistent models) Because only the lowest level of detail needed is stored in a microcube, there is no requirement for sparsity handling in BusinessObjects. Methods for managing size No limits are imposed on the size of the target model. The size of the microcube is restricted only by the time taken to download it to the client. In memory caching options Facilities are provided for tuning the cache. Informing the user when stored data was last uploaded All reports specify when the model was last refreshed. Management of users Multiple users of models with write facilities BusinessObjects does not support a write-back capability. User security profiles An object-based security model is supported for users. Administrators can grant access rights to individuals or groups of users (including nested groups). Several user profiles are provided, and custom profiles can also be set up. Complex security hierarchies can be set up and displayed graphically; users ‘inherit’ security attributes and access rights from ascendant groups. Query governance Controls can be set that restrict the size of the result of a query; for example, the maximum number of rows returned by a query, and limit the maximum ‘fetch’ time for a query. Additional controls can be set on: • elements of SQL syntax generated by queries (such as nested ‘select’ commands and operator functions) • the use of certain business objects in a query • access to specific rows in a database table. Restricting queries to specified times Users and certain types of queries can be restricted to certain times of the day.

Management of metadata Controlling visibility of the ‘road map’ BusinessObjects’ ‘universe domains’ are used to restrict the visibility of the metadata model for specific users or groups of users. These domains provide users with a controlled view of the mapping layer and data operations they can access.

Adaptability Summary

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In BusinessObjects, adaptability is generally a case of incorporating new members automatically and being able to modify the mapping layer to meet changing business requirements. All of this is well supported. Accessing upstream metadata from data warehousing tools can be used to synchronise the mapping layer with data sources. However, there are no facilities to inform end users of the updates or impact analysis for existing models. Change in business requirements Adding new dimensions to a model New dimensions can easily be added to the mapping layer and subsequently be incorporated in reports. This is easily achieved using point-and-click and re-running the query. All changes and additions are automatically time- stamped. Re-use a dimension definition New dimensions added to the universe can be re-used across reports. Adding new measures to a model New measures can easily be added to the mapping layer and subsequently used in new or existing reports. Reuse of calculated measure definition New measures added to the mapping layer can be re-used across reports. Changing the architecture to reflect business needs There is no direct support for changing the architecture to a full MOLAP or ROLAP mode. But BusinessObjects can implicitly link into both these environments. Changes to data sources Keeping the data source and model schema synchronised The Design Wizard can read database schema or accessing metadata about the data source from external metadata tools. This enables the mapping layer to respond to changes in the database and synchronise any reports built using it. But users are not automatically informed of any changes to source data.

Automatic updating of members in a dimension Changes to the structure of the database, such as the inclusion of a new dimension member, will require updates to the mapping layer; these changes are automatically picked up. Metadata Synchronising model and model metadata There are facilities to keep reports and the mapping layer synchronised. Impact analysis There is no support for impact analysis. Metadata audit trail (technical and end users) WebIntelligence II provides audit trail facilities for administrators. But these facilities are not yet supported by the client-server tools. Access to upstream metadata BusinessObjects can access metadata created by data warehousing tools such as Informatica, Prism Solutions and Carleton Apertus. This metadata can be mapped to a universe schema.

Performance tunability Summary

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BusinessObjects’ desktop architecture has potential restrictions on performance and scalability. To overcome the size and performance issues DBAs can tune BusinessObjects to exploit native SQL access, multipass SQL and aggregation tables. Special third-party technology can also be licensed to handle large and complex data sets. However, performance would be enhanced by a more scalable, server-based OLAP engine. ROLAP Multipass SQL BusinessObjects automatically generates multipass SQL. Options for SQL processing SQL processing is done in the database server. Speeding up end-user data access Microcubes are cached on the server in an optimised format for queries. These microcubes can be directly accessed to speed up end-user access times. Aggregate navigator BusinessObjects can use aggregate tables in the database. If DBAs aggregate data in the target database at multiple levels (day, week and month), BusinessObjects automatically selects the highest level of aggregation that satisfies the query.

MOLAP Trading off load time/size and performance BusinessObjects does not provide its own persistent MDDB store. Microcubes are created ‘on-the-fly’ as small multidimensional data structures and do store pre-calculated aggregate data. However, BusinessObjects can integrate with third-party MDDBs. Support for multiple users BusinessObjects supports large-scale deployment through its ease of set-up and extensive user administration features. Processing Use of native SQL to speed up data extraction The BusinessObjects query engine supports native SQL and ODBC access to databases. Distribution of processing Depending on the architecture implemented, processing can be done on the client (desktop architecture) or the server (Document Agent Server or WebIntelligence II). SMP support If the target database supports SMP, Windows 95 and Windows NT, BusinessObjects clients can take advantage of this. The WebIntelligence II application server supports shared processing. Other performance tunability features AnswerSets can be used to enhance the handling of large data sets. AnswerSets is a graphical set-based data segmentation and sampling tool that integrates tightly with BusinessObjects and other desktop OLAP tools. It can be used to parameterise large queries by selecting, grouping and banding data using ‘set-theory’ in advance of running a query. Complex queries can thus be broken down into a series of small, simple queries using optimised multipass SQL. AnswerSets is developed by Next Action Technology (www.answersets.com) and is licensed as a separate product. Business Objects exclusively resells AnswerSets in the UK.

Customisation Summary

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BusinessObjects is positioned as a ‘ready-to-use’ tool for end users – the client modules do not require any customisation or adaptation. Support for specialised application development is limited to a procedural Visual Basic-like scripting language, for customising aspects of the tool’s interface and behaviour, or links to Windows development tools via OLE automation. Customisation Option of using a restricted interface Certain aspects of the BusinessObjects user interface can be modified to restrict functionality. Ease of producing EIS-style reports A scripting language (called ReportScript) can be used to create custom EIS reporting systems. Scripting is a two-part process: creating a visual interface; and defining the actions to be taken from the interface. Applications Simple web applications There are no tools provided to develop web applications. Development environment BusinessObjects does not support a graphical development environment. An internal scripting language, called ReportScript, does allow developers to design screen layouts and define program logic to launch custom BusinessObjects reports and queries, as well as other desktop applications. ReportScript is based on Visual Basic, and includes standard editor, compiler and debugging facilities. Use of third-party development tools BusinessObjects supports OLE automation (client and server). This allows BusinessObjects functions to be called from Windows development tools, such as Microsoft Visual Basic and Visual C++. Other customisation features BusinessObjects is available in English, French, German, Spanish, Hebrew and Japanese language versions.

Deployment

Platforms Client BusinessObjects clients run on Windows 3.1, Windows 95, Windows NT and Unix Motif. WebIntelligence II supports web browser which supports Java. Server Document Agent Server runs on Windows 95, Windows NT and Unix. WebIntelligence II application server runs on Windows NT.

Data access BusinessObjects has native drivers for all the major relational databases. ODBC access is also provided. It also provides pre-packaged data providers for a number of non-SQL sources, including spreadsheet data, multidimensional databases (Microsoft SQL Server OLAP Services, Hyperion Solutions’ Essbase, Oracle Express and Informix MetaCube) and external applications; rapid deployment templates are provided for SAP, Oracle, PeopleSoft and Baan applications.

Standards BusinessObjects provides a published API. It also supports Microsoft’s OLE DB for OLAP (as a consumer) Oracle Express’ SNAPI and Hyperion Solu- tions’ Essbase API.

Published benchmarks BusinessObjects does not have any published benchmarks.

Price structure The standard BusinessObjects query and reporting modules are priced at $595 each. BusinessObjects Explorer and Analyzer modules cost $695 each. The BusinessObjects Supervisor and Designer tools each cost $1,995. WebIntelligence II is priced at $595 per user; the WebIntelligence II Ex- plorer modules cost an additional $395 per user. Document Agent Server is priced at $7,995 for the Windows NT version and $15,995 for the Unix version. BuisnessMiner is priced at $495 per user.

Summary

At a glance ...... 2 Terminology of the vendor ...... 3 Ovum’s verdict...... 4 Product overview ...... 5 Future enhancements ...... 11

Commercial background

Company background ...... 12 Distribution ...... 13

Product evaluation

End-user functionality ...... 15 Building the business model...... 16 Advanced analytical power ...... 18 Web support ...... 20 Management ...... 21 Adaptability ...... 23 Performance tunability...... 25 Customisation ...... 26

Deployment

Platforms ...... 28 Data access ...... 28 Standards ...... 28 Published benchmarks ...... 28 Price structure ...... 28 At a glance

Developer Cognos, Ottawa, Canada Versions evaluated PowerPlay and PowerPlay Web version 6 Key facts • A desktop tool for multidimensional analysis • Runs on Windows 95 and NT. Optionally, processing can be carried out on a Unix server (AIX, HP-UX or Sun Solaris) • Cognos also produces Aristotle, a client tool specifically designed for access to Microsoft SQL Server 7 OLAP Strengths • An easy-to-use ‘out-of-the-box’ tool with no programming requirements • Analytical and data mining functionality can be added via other Cognos tools • Good performance tuning for a desktop tool Points to watch • Accessing data from relational databases to build the model requires the use of Impromptu, Cognos’s report writing tool • Little support for specialised analytical analysis • No web access to detailed data Ratings

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End user functionality

Building the business model

Advanced analytical power

Web support

Management

Adaptability

Performance tunability

Customisation Terminology of the vendor Categories The Cognos term for members of a dimension. Impromptu Impromptu is Cognos’s report and query tool. Although it is possible to import data from RDBMSs without the use of Impromptu, in practice it is usually used for this. Impromptu is also used to display the detailed data. Impromptu catalogue The business view of the data that is produced in Impromptu Administrator. This is available to developers via the Impromptu icon within Transformer. Impromptu queries PowerPlay can access relational data sources, but the definition of the data to be retrieved is done in Impromptu and saved as a ‘query’. A query definition is thus the SQL definition and metadata to enable Transformer to execute the query. Model The model is the specification from which PowerCubes are generated. The model is usually designed by IT staff. The end user works with PowerCubes (data stored in a multidimensional format) rather than the model (a design specification). Portfolio This organises PowerPlay reports into briefing books for EIS use. PowerCube Cognos’s storage format for multidimensional data. PowerCubes can be stored as files or in a relational database if extra management and security features are required. Ovum’s verdict

What we think PowerPlay used to be described as ‘an easy-to-use desktop tool’, but this is now only one of its several aspects. When used in this mode, it is differentiated from its competitors primarily by its integration with other modules from Cognos – thereby potentially offering an impressive range of data mining and forecasting features. While it is most frequently used in a client-centric configuration, it also provides a mid-tier engine. PowerPlay now offers a compromise between an end-user tool and a MDDB. It offers better datastore facilities than most desktop OLAP products, and better end-user facilities than most OLAP servers. It is an ideal tool if you do not know how large or specialised your requirements will become, because it allows you to introduce a specialised OLAP server if one is needed. The increasing functionality provided by PowerCubes – particularly the partitioning options – offers some of the performance benefits of a MDDB. Cognos will have to decide whether to continue to enhance the datastore features and compete directly with the OLAP servers offered by Arbor, Oracle and Microsoft, or whether to concentrate on strengthening the end- user functionality and assume that the tool will be used to access a third- party OLAP database.

When to use Cognos’s PowerPlay should be on your shortlist if you: • want something that runs out-of-the-box • want to separate the roles of model builder and model user • already use Impromptu for reports • want integration with desktop data mining and forecasting tools • want to access ERP (particularly SAP) data. It is less suitable if you wish: • to build models with more than 500,000 unique members • to develop customised applications • to avoid buying two tools, because Impromptu is essential to efficiently- built models based on relational data • to use complex analytics. Product overview

Components The main components from Cognos that support OLAP are: • PowerPlay version 6.0 • Impromptu version 5.0 • PowerPlay Server Web Edition version 6.0 • Scenario version 2.0 • 4Thought version 4.0. The main focus in this evaluation is on PowerPlay and PowerPlay Server Web Edition. Figure 1 shows whether the component runs on a client or a mid-tier server, the stage at which it is typically used and its primary function. PowerPlay PowerPlay provides support for the OLAP aspect of the business intelligence spectrum. It is used to build multidimensional models and uses these and third-party OLAP models built using Arbor’s Essbase, Oracle Express, DB2 OLAP and OLE DB for OLAP providers. This component is well integrated with Impromptu and, in practice, even if the main requirement is for OLAP, both tools would be used. There are two reasons for this: when drilling down to the detail, Impromptu is required and, if data in relational databases is required in the PowerCube, it is imported via an Impromptu query. Other components that are installed as part of PowerPlay, but have separate icons on the desktop are listed below. Transformer Transformer mode is used to build multidimensional models from Im- promptu queries (this is the means of accessing relational databases as described above), ASCII files, Excel, Lotus, dBase, Paradox and FoxPro.

Figure 1 Component details

Main purpose

Extract data Build and Web access Desktop data Forecasting from RDBMS use OLAP mining

Client Impromptu PowerPlay Scenario 4Thought

Mid-tier PowerPlay Server server Web Edition Portfolio This organises PowerPlay reports into briefing books for EIS use. Scheduler For scheduling the update of the reports defined in Impromptu or multidimensional models defined in PowerPlay, and automatically publishing reports on the Web. Authenticator This is used to set up user privileges and access controls on the multidimensional models. Impromptu Impromptu is primarily a report writing tool, but reports specified in it can also be used as data sources in PowerPlay. This indirect method is used to incorporate data from relational databases and ERP sources such as SAP, Baan, PeopleSoft and Oracle Financials into multidimensional models defined in PowerPlay. Impromptu comes in two editions: • the Administration edition for creating a catalogue – giving a business view of the data sources (that is, metadata) • the Enterprise User edition for creating reports. PowerPlay Server Web Edition This enables web access to pre-built PowerCubes, or third-party OLAP databases, but does not allow the user to build new models. Users identify themselves when logging on and only have access to certain PowerCubes. Users can drill across and down within the PowerCube and can drill-down to detailed data using Impromptu Web Query or Impromptu Web Reports if they have access to these tools. Scenario (not included in this evaluation) Scenario is a data mining tool designed to help non-experts understand the contribution of factors in determining an outcome. The user chooses a measure to investigate and the columns that may be influential. The contribution of each of these factors is displayed with the sample size. The user can drill- down and use the Impromptu component to display detailed data. It is entirely driven by point-and-click. PowerCubes and Impromptu reports are just two of the input sources. 4Thought (not included in this evaluation) 4Thought was acquired in 1997 from Right Information Systems. It is a business modelling and forecasting tool with time series or profile analysis; it uses neural network technology. PowerCubes and Impromptu reports are just two of the input sources.

Architectural options PowerPlay is usually described as a desktop OLAP tool, but it can also be configured so that the processing to build and manipulate the multidimensional model is carried out on a mid-tier Unix server (HP/UX, IBM/AIX or Sun Solaris). Full mid-tier architecture Full mid-tier architecture, in which the OLAP engine, an MDDB and metadata are held on a mid-tier server, is not supported by PowerPlay. Light mid-tier PowerPlay supports two forms of light mid-tier architecture (in which the processing is done on a mid-tier server, but there is no MDDB): • using a Unix server for the generation and scheduling of PowerCubes • using the PowerPlay Server Web Edition. When a Unix mid-tier server is used, the design of the model is done on the desktop using the Transformer component of PowerPlay and then passed to the mid-tier server for building. The model can then be accessed in the usual way using PowerPlay on the client. PowerPlay Server Web Edition is used to access, but not build, models. As with most OLAP tools, it uses a four-tier architecture in which the PowerPlay Web Server uses CGI to communicate with the web server and thus enables the generation of HTML pages. Desktop architecture This is the ‘natural’ architectural configuration for PowerPlay. The multidimensional model is designed and built on the desktop PC. The PowerCube can be stored locally or centrally and can, optionally, be stored in an RDBMS if database management features are required. Mobile architecture PowerPlay supports a mobile architecture, in which OLAP processing can continue when the links to external data and processing sources are severed. It is simply achieved by copying the PowerCube onto a PC with PowerPlay installed.

Using PowerPlay Need for Impromptu to access data to build the models One feature that is unusual in PowerPlay is the way in which some data for the model is accessed. To use data from an RDBMS or a specialised data source (such as SAP or Oracle Financials) in a model, a ‘query definition’ has to be defined in Impromptu to extract the data. This query definition then appears as a data source in the PowerPlay interface. Thus, Impromptu must be installed and used when the multidimensional model is built. The data is then stored within the PowerCube, so it is not necessary to have Impromptu when the model is being accessed. Multiple opportunities to add calculated measures More opportunities bring the benefit of greater flexibility. The use of Im- promptu to define queries to extract data means that there are three points in the OLAP process at which the designer or end user can add calculated measures: • when defining the query in Impromptu • when building the multidimensional model in PowerPlay • when running a report. The Expression Editor in Impromptu is shown in Figure 2. Impromptu is also able to incorporate functions provided by the database being accessed. Figure 3 shows the Expression Editor in Transformer that is used when the model is being built. Out-of-the-box ease-of-use PowerPlay is an easy-to-use, out-of-the-box tool. Version 6 makes use of multiple frames, as shown in Figure 4. This shows how the navigation frame on the left enables the user to get an overall picture of the model.

Figure 2 Impromptu Expression Editor

Figure 3 Transformer Expression Editor

Figure 4 PowerPlay frames

Division of responsibility The tool lends itself to a division of responsibility between designer and user. The multidimensional business model is designed, usually by an IT person, with the Transformer interface in PowerPlay. This is accessed by PowerPlay in Explorer or Reporter mode. Support for multidimensional databases Models built in multidimensional databases (such as Arbor Essbase, Oracle Express, DB2 OLAP and OLE DB for OLAP providers) are directly accessed using PowerPlay Connect, an easy-to-use interface in which the path to a model is specified and given an alias with an ‘.mdc’ extension. It then behaves like a PowerCube. Partitioning options In early versions of PowerPlay only the detailed data was held in the PowerCube and all aggregates were calculated on-the-fly. Versions 5 and 6 offer partitioning (manual or automatic). Partitioning is a process in which a potentially large model is divided into a number of partitions, or nested sub- cubes. The partitions contain pre-calculated aggregates on some dimensions. The effect of partitioning is to increase the size of the cube but potentially to improve performance. Load time is traded off against end-user access time by the design decisions made when setting up partitions. PowerCubes can be designed so that the user can navigate easily from one to another if they share dimensions. Future enhancements Version 6.5 of PowerPlay is scheduled for release in the first half of 1999. Cognos intends to include the following enhancements: • the provision of a multi-server back end. This is expected to support load balancing, write-back from 4Thought to PowerCubes and greater support for PowerPlay Web Reports • support for the remote installation of the client version of PowerPlay • support for WAN deployment of the client version of PowerPlay • the addition of extra features to the PowerPlay Server Web Edition to support Java clients, drill through to Impromptu Web Reports and a common log-in for all web products. Commercial background

Company background History and commercial Cognos was established in 1969 and is based in Ottawa, Canada. It was originally a consulting company and developed into a single product company selling PowerHouse, its 4GL for mid-range systems. In the late 1980s, it broadened its portfolio from straight application development tools into data analysis and reporting, launching PowerPlay in 1990 and Impromptu a year later. In the early 1990s, the company lost momentum when the switch towards client-server systems reduced demand for PowerHouse and the emphasis within the company moved from its 4GL product to desktop business intelligence tools. It has extended the range of its business intelligence desktop tools through acquisitions. In 1995, it licensed data mining software from Angoss Software in Toronto, which emerged as Cognos’s data mining product, Scenario, in 1997. Also in 1997, the company acquired 4Thought, a forecasting tool using neural networks, from Right Information Systems, a UK-based company, for $8 million. Another 1997 acquisition, Interweave Software, provided the basis for the web versions of Impromptu. In early 1998, Cognos licensed an end-user tool, code named Aristotle, from Panorama, the Israeli company that originally developed Microsoft SQL Server 7 OLAP Services. Microsoft claims that this does not give Cognos any advantage over other ISVs because the Aristotle team at Panorama post-dated the OLAP Services developments. Cognos has reported good revenue growth and profitability for several years. Revenue for the financial year ending February 1998 was $244 million, with a profit of $33 million (or $50 million excluding the cost of acquisitions). In the previous year revenue was $198 million and profit was $36 million. Character and direction While Cognos’s 4GL product, PowerHouse, still brings in revenue, the energy and focus of the company in the business intelligence market. It is positioned at the end-user tool end of the market, although several of the improvements in version 6 relate to the multidimensional storage capabilities of the PowerPlay cube. Through the acquisition of Scenario and 4Thought, Cognos now has a comprehensive range of business intelligence end-user tools. In response to the challenge of the low-entry cost associated with Microsoft SQL Server 7 OLAP Services, the company acquired Aristotle, described by Cognos as a ‘little brother’ to PowerPlay. Cognos has also responded to the growing awareness of the need to harness ERP data with its ‘Accelerator for SAP’ product, which is an extraction and reporting tool for SAP data. The Cognos products are generally targeted at generic business intelligence requirements. Cognos seeks to provide an integrated set of decision support tools and believes that this enables customers to choose the tools they require. The philosophy behind PowerPlay is to provide a range of support for a range of users, without any need for programming. The company has licensed NCR to include its products in data warehouse products for financial institutions. Other significant partnerships include an agreement with IBM to bundle Cognos Impromptu and PowerPlay with Visual Warehouse. IBM also supports the use of Cognos with IBM’s DB2 OLAP server. Cognos uses direct and indirect channels to sell its business intelligence products. The majority of revenue is generated by its direct sales force, which is organised around vertical sectors including food, drink, retail, telecommunications, finance, transportation and manufacturing. Indirect sales, in which partners either distribute the products or incorporate them in their own solutions, tend to be for lower volumes. It is likely that sales of Aristotle will be through partners. Cognos intends to continue to sell its products through a combination of direct sales and partnerships.

Customer support Support Hotline support is available, typically at a cost of 20–25% of the licence fee. Training Training is offered at Cognos sites worldwide as well as on-site. Consultancy services Cognos offers consultancy directly and via its partners. The company runs the Cognos Certified Professional Programme for product specialists.

Distribution Headquarters Cognos 3755 Riverside Drive PO Box 9707, Station T Ottawa, ON Canada K1G 4K9 Tel: +1 613 738 1440 Fax: +1 613 738 0002 US One Burlington Business Centre 67 South Bedford St Suite 200W Burlington, MA USA Tel: +1 781 229 6600 Fax: +1 781 229 9844 Europe Cognos Westerly Point Market Street Bracknell Berkshire RG12 1QB UK Tel: +44 1344 486668 Fax: +44 1344 485124 Asia-Pacific Cognos 110 Pacific Highway Third Floor St Leonards NSW 2065 Australia Tel: +61 2 9437 6655 Fax: +61 2 9438 1641 http://www.cognos.com Product evaluation

End-user functionality Summary

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PowerPlay is an extremely easy-to-use tool with a very intuitive interface. The metadata support to aid end-user understanding of the models includes a description of dimensions and measures. However, there is no support from within the tool to enable end users to schedule the distribution of reports to colleagues, or to subscribe to such reports. Finding and understanding the model Finding and loading a multidimensional model In PowerPlay, business models are grouped into folders. Alternatively, Impromptu and PowerPlay reports can be grouped in an EIS-style interactive briefing book, called a ‘portfolio’. Metadata for end users Metadata, in the form of descriptive text, can be entered for measures, dimensions and members. It is available to the end user via an ‘explain’ dialogue box. The currency of the data in the PowerCube can be built into the title and, if the ‘preferences’ options are set appropriately, will always appear. Annotation by the end user The end user cannot annotate the PowerCube, but can add text when viewing it in Explorer and save this as a report. It is thus accessible to users of the report. Using the model Basic OLAP functionality All the usual OLAP functions, including exception reporting, are accessed using point-and-click. A more restricted (easier to use) environment can be created in an EIS-style portfolio. Changing the position of members in a dimension level Values in a dimension can be repositioned using point-and-click. Visualising the drill-down hierarchies The levels of a dimension can be shown in a navigation window. Drilling down to detailed data If an Impromptu query is used to build the model, the user can drill-down to detailed data if Impromptu is available. The detailed data is freshly retrieved. Range of front-end user tools From PowerPlay, the report can be saved in Excel format, comma delimited, ASCII or as a sub-cube. Visualising the results There is a range of charts and graphs, but no maps. Detailed data and charts can be seen on the same page. Saving and sharing results Designing a report Reports prepared in Explorer can incorporate bitmaps and sounds. Publishing a report There is a ‘publish as HTML’ menu option to create a series of web pages with navigation buttons showing a series of pages, each containing a ‘slice’ of information. That is, the pages are not interactive, but contain snapshots of the data. There is no direct point-and-click support for scheduling the publication of a report to an individual or group, although it could be executed dynamically or scheduled as a one-off or recurring task using the macro scripting language. Users can also publish a PowerPlay report as a page within a portfolio briefing book for EIS use. Targeted distribution via e-mail There is a menu option to mail a link to a PowerCube. A static view of a report can be mailed by copying it and then pasting it into the e-mail, ob- tainable via the ‘send’ menu option. Subscribing to reports There is no direct support for users to see and subscribe to available reports.

Building the business model Summary

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The strength of the tool lies in its ease-of-use in defining the structure of a multidimensional model and populating it with data. There is automatic support for defining the time dimension and quickly producing a prototype if the data source is appropriately structured. Using Impromptu to access SQL data prevents a sample of data being available to the designer. Basic design Design interface The design interface is easy-to-use and exclusively point-and-click. Visualising the data source It is not possible to display a sample of data from the data source/s to be used in the model. Universally available mapping layer There is no direct support for a universally available mapping layer. Some support is provided by the use of re-usable Impromptu queries. Prompts for metadata The developer is not automatically prompted but can, optionally, provide a description for dimensions, measures and members in the model. This is accessible to end users. Access to upstream metadata There is no integration to access metadata generated by extraction or database tools in the preparation of the data. Building the dimensions Selecting columns for the dimensions Columns for the levels in the dimensions can be selected using point-and- click. Selecting the members shown in a dimension level Members can be hidden using point-and-click. Defining a dimension hierarchy A dimension hierarchy can be built automatically from a data source using the Autodesign feature or point-and-click. The tool supports unbalanced dimensions, the specification of alternative drill-down paths and the inser- tion of user-defined levels. Time dimension PowerPlay has a set of date dimension dialogue boxes to assist with the creation of a hierarchy in this dimension. New date units can be defined in this, and automatically allocates the members, including calculations such as year-to-date change or growth. Annotating the dimensions Dimensions can be annotated using the properties sheet in which there is a description tab. Information entered here can be viewed by the end user by right clicking on the dimension and choosing ‘explain’ from the menu. It is also possible to define both long and short names for the members. Default level of a dimension hierarchy The default levels in a dimension are the top ones. However, having drilled down and changed dimensions, the user can save this view which then becomes the starting point when the view is re-opened. Defining the measures Calculated measures Calculated measures are defined in a calculator-type interface in Trans- former. It allows ‘if, then, else’ constructions and a range of functions. Support for multiple measures with a set of dimensions A set of dimensions can have multiple measures attached to it. Multiple designers Multiple designers Support for multiple designers is only an issue if a mid-tier server is used, in which case only one client is allowed to access models on the server. This ensures that there are no lost updates. Support for versioning There is no direct support for versioning. Other ‘building the business model’ features Using a ‘count’ button on the toolbar, the developer can see the number of members in each dimension. Prototype models can be quickly built using the Autodesign feature from one or multiple data sources if the data is appropriately structured. Using a currency table query, the user can format the measures in the appropriate currency.

Advanced analytical power Summary

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PowerPlay provides multidimensional support, but for more specialised analytics the user would have to make an additional purchase of Scenario (although this is included if the business intelligence suite is purchased) for data mining, and 4Thought for business modelling and forecasting. While PowerPlay is promoted by Cognos as an easy-to-use tool and has few in-built functions supporting specialised analytics, the tool includes a scripting language that enables users to extend the capabilities of the product. The score reflects the facilities within PowerPlay, but the text indicates how the other two products could significantly enhance this. Third-party tool integration From Explorer, reports can be saved in Excel format and then loaded into Excel to use its analytical functions. The amount of integration with Cognos’s other products, which offer more sophisticated analytical support, is continually increasing. From Scenario, the desktop mining tool, users can now drill-down to display detailed data in Impromptu. Scenario and 4Thought (for business modelling and forecasting) can both read PowerCubes. Scenario can generate a PowerCube for further exploration. Defining specialised models Ranking and sorting This is provided in Explorer mode in PowerPlay. Mathematical methods Not provided by PowerPlay. Financial functions Not provided by PowerPlay. Statistical models In PowerPlay, the user can add standard deviation and regression lines to graphs, but more support is available in 4Thought and Scenario. Trend analysis There is no direct support for trend functions, such as moving averages and smoothing functions. Simple regression There is no support for regression algorithms. Time series forecasting Dynamic time series, such as ‘the last 12 months’, can be defined in PowerPlay. Forecasting is the main focus of 4Thought. User-definable extensions There are facilities for users to extend the analytical capabilities by writing their own functions using CognosScript, a scripting language similar to Visual Basic. PowerPlay provides a development environment for this, which includes a debugger. The language exposes the dimensions, measures and members, either by name or using an index. Write back for ‘what-if’ analysis There is no provision for write back, either in PowerPlay or 4Thought. Incorporating non-numerical data There is no support within PowerPlay, but 4Thought and Scenario can process categorical data. Data mining This is not provided by PowerPlay, but is the main focus of Scenario. Other analytical functionality Although the two additional products, Scenario and 4Thought, are acquired products, the amount of integration between them is steadily increasing. From Scenario, users can now drill-down to detail displayed in Impromptu. Cognos users have a site on CompuServe (that is, not a Cognos-supported website) from which macros for some of these functions (for example, Holt- Winters and Pareto) can be downloaded. Web support Summary

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Web support is provided by PowerPlay Server Web Edition, which is purchased separately from PowerPlay. It allows browser access to PowerCubes. The functionality provided by browser access to PowerCubes is getting closer to that offered via the desktop. However, it does not yet offer facilities to edit or create models via the browser. While there are facilities for individuals to initiate publication and distribution of models and reports, the product would be enhanced by additional features to support centrally organised distribution. End-user functionality via the Web Functionality of web access to explore models Web access offers most of the functionality offered by desktop access, although the interaction is via drop-down charts rather than drag-and-drop, resulting from the use of Java Script rather than Java applets. The access is comparable to using the desktop version in Explorer mode, but does not allow the greater flexibility that users get with Reporter mode on the desktop, in which they can add information at different levels and dimensions as well as perform calculations. Only minimal detail data is available on the same screen as the graphical display. Pivoting is supported via a toolbar button and a range of charts is available. There is no support to drill-down to detailed data using web access. Users can only access data held in the PowerCube. Supports both registered and unregistered web access Technically, PowerPlay supports registered and unregistered users. However, the standard licence agreement with Cognos does not allow access to unregistered users. Internet access licensing is available by negotiation. Range of users supported by the web interface The web browser interface is primarily for interactive exploration of business models. It is possible to save the result so that it can be imported into Excel, but there are no facilities to build a report using the information retrieved. Creating models via Internet and the Web Editing the mapping layer There is no intervening mapping layer. Building and editing models There is no support for creating models via the web interface. They have to be created in the desktop version. Distributing via the Web Generate HTML and Java HTML text can be generated when running PowerPlay in Reporter mode. There is no option to generate Java code. Corporately organised distribution via the Internet There is no support for the centrally organised distribution of models or reports, although as described in the section on End-user functionality there is support for individually organised distribution. Include URLs in a report URLs cannot be included in web reports. Distribution of web server processing Using information provided by PowerPlay Web Administrator (as described in Query monitoring), the administrator can manually balance web processing between multiple servers.

Management Summary

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There are two main editions of PowerPlay, (client- and server-based). In both editions, user and PowerCube securities can be defined. The management features of the server edition are designed to support a large number of users. The score here reflects the good support given to the management of users. In version 6, the features for performance tunability of the data have been substantially enhanced, but to fully exploit these the administrator has to manually control the partitioning features. Management of models Separate management interface The administrator uses the Transformer interface of PowerPlay running on the client to manage the design and build process. Security of models Security is controlled through the Authenticator document, which is usually stored on the mid-tier server. The location of this is specified within each PowerCube. It enables the administrator to define privileges for users and PowerCubes. Query monitoring Query monitoring is not supported in the client version. However, PowerPlay Web Administrator provides status and performance information that can be used to balance web processing over multiple servers. Information given includes the number of requests received in the last minute and the average time to process requests. Management of data How persistent data is stored (not scored) Persistent data is stored in PowerCubes. PowerCubes can be stored locally, on a mid-tier server or in an RDBMS (for additional security and management features). In early versions of PowerPlay, only the detailed data was held in the PowerCube, and all aggregates were calculated on-the-fly. Versions 5 and 6 offer partitioning (manual or automatic), which adds partitions to the cube that generates pre-calculated aggregates on some dimensions. The effect of partitioning is to increase the size of the cube but improve performance. The size of cubes is reduced by compression. PowerCubes can be designed so that the user can navigate easily from one to another if they share dimensions. Scheduling of loads/updates Scheduling can be controlled either from the desktop or from the mid-tier server. If this is done from the desktop, a separate Scheduler module is available to schedule refreshing of the PowerCube. With this module, a schedule can be defined using point-and-click. If scheduling is centralised on the server, it uses the Unix scheduling utilities of cron and crontab. Using the scripting language, multiple cubes can share a refresh schedule. Event-driven scheduling Event-driven scheduling can be defined using the scripting language from within the scheduler in the client version. Failed loads/updates When a PowerCube is built, information is created in a log file, including the time taken, number of records processed and whether the processing was successful or not. If a load is partially successful, a checkpoint is created so the load job can start from this point rather than the beginning. If a job fails, the user is informed next time Transformer is opened. Distribution of stored data PowerCubes can be stored wherever the user wishes. Sparsity (only for persistent models) Transformer, by default, assumes that the combinations of dimensions in a model are sparse. Sparsity only becomes an issue when pre-calculated consolidated aggregates are also stored and is not a concern unless partitioning is used to do this (that is, the basic PowerCube does not include consolidations). The handling of sparsity is therefore dependent upon the design of the partitions. Methods for managing size A basic PowerCube contains only detailed data and no aggregates. In PowerPlay, size is a function of the amount and design of partitioning. Without partitioning, records are stored in the PowerCube and rolled up at runtime by the PowerPlay client to provide summary values. Potentially, millions of records could be involved in this. Partitioning reduces the summarisation required at runtime by writing consolidated records to a partition, and leaving lower level detail records in a separate partition. In general, the effect of partitioning is to trade-off end-user access time against increased build time. Cognos claims that partitioning may increase size by 50–100%, but speeds up retrieval time between ten and 100 times. In memory caching options The cache size used in the client when building and executing models can be set through the ‘Cognos.ini’ file. It is a manual process, requiring adjustment if there appears to be heavy disk activity or the building of PowerCubes takes longer than expected. Informing the user when stored data was last uploaded As described in the End-user criteria, reports can (optionally) include the refresh date in their title. PowerCubes viewed in Explorer do inform the user when the stored data was last uploaded. Management of users Multiple users of models with write facilities Not applicable. User security profiles There are a number of ways of controlling security, including password access to PowerCubes, definition of ‘user classes’ (that is, group), security profiles, and the use of the RDBMS (if the PowerCubes are saved in a database) and network security. The file containing the user class definition information is encrypted. Query governance There are no query governance features, apart from the time restriction. Restricting queries to specified times Users can be restricted to particular times for accessing PowerCubes. Management of metadata Controlling visibility of the ‘road map’ Users are not aware of PowerCubes to which they have no access rights. Different users can have different views on the same PowerCube.

Adaptability Summary

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In a client-based configuration, with minimum metadata, adaptability is generally a case of incorporating new members automatically and being able to modify the model to meet changing business requirements. All of this is well supported. Adaptability is more of an issue in a large-scale environment, where it is likely that a server-centric model would be used. Adaptability could be extended to support some of the features described below by the use of MDL, a fourth generation language, and C shell scripts. There is thus power and flexibility, but a requirement for additional skills. Change in business requirements Adding new dimensions to a model A new dimension can be added to a model using point-and-click and a new PowerCube generated using a button on the toolbar. In a client-server configuration there are straightforward mechanisms to ensure that server and client models are synchronised. There are no change management facilities. Re-use of dimension definition There is no support for the re-use of a dimension definition. Adding new measures to a model As with dimensions, a new measure can be added to a model and a new PowerCube generated. Re-use of calculated measure definition Measure definitions cannot be saved and re-used in other models. Changing the architecture to reflect business needs The architecture of the system can be changed from being client-based to server-based if required. Previously defined PowerCubes can still be used. Some of the benefits of HOLAP architecture are achieved by storing summary data in PowerCubes and using Impromptu query definitions to retrieve the detail data. Impromptu query definitions always retrieve data as required, rather than accessing a pre-stored source. Changes to data sources Keeping the data source and model schema synchronised The data and model are both held in the PowerCube, so they are always synchronised. Automatic updating of members in a dimension When a PowerCube is refreshed, new members are automatically included. There is support for level locking so that new categories in that level are not added. This is indicated to users by an icon of a lock on the level. Metadata Synchronising model and model metadata There is no mechanism to ensure that when changes are made to the dimensions and measures that the descriptive metadata is synchronised. Impact analysis There is no support for impact analysis. Metadata audit trail (technical and end users) There is little metadata to audit. Access to upstream metadata There is no support for access to upstream metadata.

Performance tunability Summary

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The original design of the PowerCube gave little scope for performance tunability, but this has changed in recent versions. The combination of breaking the model into separate cubes that are linked so users can drill through from one to another, and partitioning, give significant tuning options. Parti- tioning gives the administrator the option of trading off build time against execution time. The incremental update enables new data to be appended to the PowerCube rather than recreating the whole model. In the commonly used client-centric configuration, the PowerCube is loaded down to the desktop; the speed of the local processing is a function of the desktop hardware and the settings for the in-memory cache. ROLAP Not applicable, because the data for the model is retrieved from a pre-built multidimensional PowerCube. MOLAP Trading off load time/size and performance This is achieved using partitioning. Without partitioning, records are stored in the PowerCube and rolled up at runtime by the PowerPlay client to provide summary values. Potentially, millions of records might be involved in this, leading to reduced end-user performance. Partitioning reduces the summarisation required at runtime by writing consolidated records to a partition, and leaving lower level detail records in a separate partition. In general, the effect of partitioning is to trade off end-user access time against increased build time. Partitioning may increases the size by 50– 100%, but speeds up retrieval time between ten and 100 times. There is no wizard support to help in deciding what to consolidate; this is a manual design decision. Support for multiple users Users do not generally download a copy of the appropriate PowerCube to their desktop, but they can do if necessary. They can also define a sub-cube and download that. If the PowerCubes are shared, the number of simultaneous users that can be supported can be increased by partitioning (as this speeds up access time). The number of simultaneous users is limited by the network traffic that can be supported. Processing Use of native SQL to speed up data extraction Data for the PowerCubes is retrieved using Impromptu query definitions. Impromptu uses native SQL for all the major databases including Oracle, Informix, Microsoft SQL Server, Sybase, CA-Ingres and MDI DB2 gateway. Distribution of processing There is no support for the distribution of processing. SMP support The application uses multi-threading and can thus take advantage of SMP.

Customisation Summary

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PowerPlay is designed as an ‘out-of-the-box’ toolkit and thus has few features for building standalone executable applications with multidimensional features. PowerPlay supports OLE automation and thus applications that make use of PowerPlay components can be developed in third-party languages. The usual division of responsibilities is that IT-based staff create and maintain the multidimensional models from which PowerCubes are generated, and business operators use these to produce customised interactive reports. These reports can be considered as applications, but they do not disguise their PowerPlay genesis. Customisation Option of using a restricted interface There is no option for the user to access the model via a restricted interface. Ease of producing EIS-style reports This is provided by the portfolio, a module provided with both Impromptu and PowerPlay, which provides an EIS environment to give the simplicity of a ‘button-driven’ application. Applications Simple web applications There is no direct support to develop applications specifically for the Web. The web browser is used to access PowerCubes developed via the desktop or server. Development environment There is no OLAP-specific development environment. Use of third party development tools PowerPlay offers OLE automation and thus reports, briefing books and other PowerPlay objects can be embedded in applications written using an OLE- compliant language. Deployment

Platforms If used in a client-server configuration with PowerCube generation and scheduling carried out on the server, the available server platforms are HP- UX 9.04 and 10.x, AIX version 4.1, and Sun Solaris 2.4 (SunOS 5.4).

Data access The sources that can be used to build a model include Impromptu files (which gives access to RDBMS and ERP data), comma delimited files, and personal data sources such as dBase, Excel and Foxpro. PowerPlay can directly access Microsoft Access databases. Impromptu uses native SQL for all the major databases, including Oracle, Informix, Microsoft SQL Server, Sybase, CA-Ingres and the MDI DB2 gateway.

Standards PowerPlay has an OLE DB for OLAP consumer interface.

Price structure If purchased separately, Impromptu (End User Edition), PowerPlay Client and Scenario cost $700 per user. When purchased as a bundle, the cost is $1,300 per user. Impromptu Web Query and PowerPlay Server Web Edition cost $500 per user (when purchased for 100 users) and $255 per user (when purchased for 1,000 users). Internet access licensing is also available. Contact Cognos for details. PowerPlay Administrator costs $2,000 (only one is required). Impromptu Administrators Edition costs $900 per user.

Published Benchmarks There are no published benchmarks for PowerPlay. Gentia Millenium Applications Platform

Summary

At a glance ...... 2 Terminology of the vendor ...... 3 Ovum’s verdict ...... 4 Product overview ...... 5 Future enhancements ...... 14

Commercial background

Company background ...... 15 Distribution ...... 17

Product evaluation

End-user functionality ...... 18 Building the business model ...... 20 Advanced analytical power ...... 21 Web support ...... 23 Management ...... 24 Adaptability ...... 26 Performance tunability ...... 28 Customisation ...... 29

Deployment

Platforms ...... 31 Data access ...... 31 Standards ...... 31 Published benchmarks ...... 31 Price structure ...... 31 Evaluation: Gentia Software – Gentia Millenium Applications Platform Ovum Evaluates: OLAP

At a glance

Developer Gentia Software, London (UK)

Versions evaluated Gentia Millennium Applications Platform (G-MAP), version 5.0.2

Key facts • A development environment for analytical OLAP applications with an MDDB • Server runs on Windows NT and leading Unix flavours; clients run on Windows 95, 98, NT, Macintosh (PPC) and Sun Solaris. Web support is also provided • Gentia’s main analytical application is the Renaissance Balanced Scorecard for implementing and tracking organisational strategies

Strengths • Powerful development environment with specialist OLAP objects and functions • Supports a wide range of distributed architectures • Gentia also provides packaged analytical applications for enterprise performance management

Points to watch • Limited ‘out-of-the-box’ OLAP functionality • Developers may find learning and using the core visual development environment difficult initially • The Gentia MDDB cannot be accessed by third-party front-end tools

Ratings

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End user functionality

Building the business model

Advanced analytical power

Web support

Management

Adaptability

Performance tunability

Customisation

Terminology of the vendor

Base models These are physical multidimensional cubes that contain dimensions and measures. Book A Gentia application is a number of pages organised into chapters and placed in a book. A page is effectively a screen of information that is likely to contain a set of visual objects for screen building, database access and analytical functionality, and a multidimensional business model, which can be interactively explored. GDL Gentia Development Language. A scripting language with procedural control, used to define management tasks and to extend the flexibility of the application development environment. Item The term used for measures. Multicube architecture GentiaDB uses a multicube architecture to minimise the size explosion caused by combining sparse dimensions. In effect, the logical multidimensional business model is built using a number of separate cubes, each of which has dimensions that are dense with regard to each other. Scenario A virtual multidimensional cube that can be created from SQL tables and other data stores. A scenario can be stored locally for personal offline use or on a server for shared workgroup access. Smart Agent Manages the automation and delegation of common and complex processing tasks within a Gentia application. Warehouse An organisational element used to group application details together and set security restrictions on them. Each warehouse can contain one or more books. Also known as an object store.

Ovum’s verdict

What we think Gentia’s core competency is providing a complete development platform for building and deploying analytical applications in a heterogeneous and distributed environment. The Gentia Visual Development Environment (VDE) is well equipped to cope with a range of application development needs for medium-to-large sized organisations. The new Application Framework extends application development to end users, but complex development will require significant programming skills and IT involvement. Gentia G-MAP is, however, a less out-of-the-box OLAP application than those offered by other MDDB vendors – principally because of the lack of front-end tools to access the database. While, in theory, the GentiaDB multidimensional database could be used directly by end users, the original decision of the company to base the API on the OLAP Council’s first specification means that the MDDB cannot be accessed by third-party OLAP tools. Gentia is well aware of this limitation and has already adopted the OLE DB for OLAP standard as a consumer provider support is planned for version 6.0. The core application development product is sound, but users that require greater sophistication and complexity in their applications may find learning and using the VDE difficult initially. A sophisticated development environment, such as Gentia will require significant training. We are also concerned about the long-term viability of a small company offering a product that needs a significant amount of customisation, when the entry of Microsoft will hasten the move towards a commodity market. To the company’s credit, it recognises the threat and has repositioned itself as a solutions-oriented company, with the delivery of applications such as the Renaissance Balanced Scorecard and the Impact range, designed to implement organisational strategies and track performance. When to use The Gentia suite of products should be considered if you: • have a distributed and heterogeneous IT environment • require strategic enterprise management applications, such as balanced scorecard, performance measurement and activity-based costing • want to build highly specialised OLAP applications and have the necessary development skills in-house to create them. It is unsuitable if you: • simply require an out-of-the-box OLAP business intelligence application with minimal customisation • want to use a range of third-party OLAP clients, as well as front-end tools developed in-house • intend to build large models with more than one million unique members and flexible time dimensionality built-in.

Product overview

Components The main components of the Gentia Millennium Applications Platform (G-MAP), version 5.0.2 are: • Visual Development Environment (VDE) • Application Framework • Open Network Architecture •GentiaDB •Gentia WebSuite • Gentia Excel Add-in. Figure 1 shows whether the component runs on a client or a mid-tier server, the stage at which it is typically used and its primary function.

Visual Development Environment The Visual Development Environment (VDE) component of the Gentia platform can access a wide range of data sources (including MDDBs, ROLAP systems and relational databases) and does not have to be used with the GentiaDB, but they are almost always used together. There is a server and client component to the VDE. The Gentia VDE is an object-oriented development environment for client- and server-based applications. Although termed ‘client-server’, the division of responsibilities is more flexible than the term suggests, because the ‘client’ has the same engine as the server, with the addition of interface services. Much of the development is done using drag-and-drop. Procedural control is through the Gentia Development Language (GDL), which is a semi- interpreted 4GL-type language. All applications developed in the VDE can allow access from both web or client-server environments, without specific programming.

Figure 1 Component details

OLAP analysis Application Support a distributed Web access development architecture

Client Gentia Excel Visual Development Add-in Environment Open Network Architecture Server GentiaDB Gentia Web Application Suite Framework

Application Designer Application Designer is a ‘personalised’ extension to the core Gentia VDE, which allows business end users to build their own OLAP applications. It provides a template-driven approach for building applications and multidimensional models and designing reports using Excel. Users have access to a number of specialised business templates or can choose to build them from scratch. The templates contain dimensions with business rules and users can select and use predefined templates via Application Designer’s drag-and-drop environment. Model structures can be imported from simple external file structures and updates scheduled.

Application Framework The Application Framework is a layer on top of the core Gentia VDE and Application Designer components, which provides a further set of re-usable components to speed up the development of applications. The components include templates, pages, menus, toolbars, status information, administration, navigation, online help and user preferences. The Application Framework also includes an Object Library that contains illustrative samples of code. A Gentia User View facility is also provided, to allow all the components to be customised.

Open Network Architecture This is a proprietary Object Request Broker (ORB), based closely on the CORBA standard that lets users access information from any data source, regardless of the platform they are working on. This support for a distributed, heterogeneous environment is one of Gentia’s distinguishing features.

GentiaDB The GentiaDB component of the Gentia Platform for Analytical Applications is a multidimensional database; it uses a multicube approach to cope with the sparsity issue. Each cube is made up of dimensions that are densely related to each other. If there is a sparse relationship (for example, sale price is not related to customer or location), then it is put in a separate cube. Thus, the sparse, large model is a view and joins are performed on-the-fly to produce slices of the view as required. To minimise the time taken to load fresh data into the database, the process of consolidation is carried out with minimal re-calculations. When a new value is added, rather than re-calculate all its derivatives, using the metadata, the system calculates which values will change as a result of this and re-calculates this minimal set. The GentiaDB also provides a central metadata store for dimension and measure definitions. Base models (the equivalent of physical OLAP cubes) are created using these definitions. In addition, virtual cubes can be created dynamically by joining base models (called join models) or can be created from SQL tables and other data stores (called scenarios).

Gentia WebSuite This is a web extension to the Gentia environment that provides full application functionality over the Internet. Using the Gentia WebSuite, messages from the browser are relayed to an Internet server (for example, Microsoft’s IIS), which then passes them to the WebSuite, which in turn acts as an interface to the Gentia application server and interprets the message. WebSuite offers three interfaces: • View, for carrying out ad hoc query and OLAP-type operations, such as drill-down, and then submits them to the web server to generate new pages • Report, which provides report formatting and distribution facilities • Data Entry, for writing back to the database for budgeting applications.

Gentia Excel Add-in The Microsoft Excel Add-in enables spreadsheet users to access Gentia business models. It supports drill-down and pivoting, and reports created in Excel can be saved or e-mailed to other users, using a predefined mailing list. Creating the reports in ‘free format’ mode enables cells to be moved to any part of the spreadsheet and thus gives the user more options than the usual one of presenting the data in standard rows and columns. Reports, including tables and charts, can also be published in HTML format for distribution over the Internet. A data entry mode provides realtime update and consolidate capabilities to the GentiaDB. Architectural options The Gentia toolset is unique among the tools we have evaluated in that it can support all four architectural options. The Object Request Broker allows for location transparency and, when designing the application, the user allocates processing to the server or the client.

Full mid-tier architecture This is the ‘natural’ architectural configuration for Gentia. The full mid-tier architecture – in which the OLAP engine, data for the models and metadata are held on the mid-tier server – is supported through the use of the Gentia server, which gives access to the services provided by GentiaDB. Access to third-party MDDB servers is also supported via OLE DB for OLAP. The client interface can be an application written using the Gentia Platform for Analytical Applications, the Excel Add-in or a web browser.

Light mid-tier architecture In Gentia, the processing of OLAP queries can be performed by the client or the server; both have an engine, but only the client has interface services. The data being accessed by the application engine can be stored in an MDDB, a RDBMS or several other formats. Access to the RDBMS does not give ROLAP support, because the SQL queries are not produced dynamically as a result of the user interacting with the model: they are predefined by the

application designer. These queries can be parameterised to support drill- down to detailed data. The flexibility is further enhanced as applications can transparently (using the ORB) access data and services stored on a variety of platforms. A light mid-tier architecture (where the processing is done on the mid-tier, but there is no MDDB store) can therefore be supported. There is the same range of client interfaces as with the full mid-tier architecture.

Desktop architecture As described for the light mid-tier architecture, there is flexibility about where the processing takes place. By configuring the system so that processing is carried out on the PC, a desktop architecture is supported, although restrictions will apply on the size of the dataset that can be analysed.

Mobile architecture The small footprint of the client (approximately 8Mb) means that it is possible to run an application on a laptop computer. Mobile users can download predefined or ad hoc sections of data, by selecting pages from books, and then analyse them offline. Changes made to the data are automatically published to other workgroup members upon reconnection to the network server. Using G-MAP

Gentia Platform is for building analytical applications Gentia is not primarily designed to be used out-of-the-box by end users, although there is a definite move in more recent versions of the product to provide more immediate ad hoc OLAP functionality, by extending application development to business end users. Development and deployment of analytical applications is at the heart of the Gentia approach to business intelligence. An application consists of a number of pages; a page will usually contain multiple views of a multidimensional business model that have been created separately, using GentiaDB or a third- party OLAP source. The principal method of building Gentia applications is through the Visual Development Environment (VDE), which uses a book-chapter-page metaphor. The structure of a Gentia application is based on pages, which are collected into chapters and then become books. Development uses base-level objects together with higher-level application objects that come as part of the Application Framework. There are three categories of base-level objects: application objects (pages, text and SQL scripts), page objects (tables, charts, hot spots and widgets) and connector objects (MDDB data source, calculator and sorter). Once an object is defined, it is held in the Object Store, where it can be viewed using the Object Inspector. The application developer works in ‘author’ mode with a Builder Palette, from which objects are dragged onto the page, as shown in Figure 2. Widgets from the palette are used to create tables, charts, list bars, slider bars, buttons, bitmaps, text areas, split views, help text and so on. A default set of

attributes is inherited from a base builder object, but these may be modified through the Object Inspector. As in a Visual Basic development environment, the graphical interface is designed and then code is attached to it. Objects are linked to data using the Connections Mapper, which defines the locations of data sources and associates the objects with a business model in the GentiaDB. In most cases, the base-level and Application Framework objects provided will satisfy most application needs. However, Gentia also provides the Gentia Development Language (GDL) to provide greater sophistication in applications. GDL supports additional features, such as event handling, dynamic SQL and an interface to the Object Store.

Extending application development to end users Gentia’s ‘user’ mode was previously restricted to viewing information using an application built by a developer in ‘author’ mode. The introduction of the Application Designer component has blurred this distinction by allowing end users to ‘toggle’ between the two environments in order to build their own applications quickly and with minimal IT involvement. The Application Designer is fully integrated into Gentia’s Application Framework environment and provides a friendly drag-and-drop environment for the easy construction and deployment of applications. Predefined application and reporting templates are provided by Gentia and application partners. End users can customise these templates according to their own specific application needs or they can choose to build new ones from scratch. A template designer is provided to select dimensions, measures and hierarchies that will be used in the OLAP application. Users can publish the template as another re-usable object in the centralised Object Store.

Figure 2 Builder Palette

Building the multidimensional model – two choices of interface Creating the structure for large, complex multidimensional business models is generally a task for the administrator or application designer, although end users can use the Application Designer interface to create their own simple structures. As with most tools, there is a point-and-click interface offering ease of use. There is also a data definition language (DDL), which tends to be used more in production environments. Some of Gentia’s customers have over 100 business models to maintain and prefer more of a script-based interface. This approach requires some technical competence. For instance, the first stage is to manually create some initialised files to hold the structure and the data. This requires making adjustments to the configuration file so the paths to these files are known to the application. These changes can be generated from the Services Manager via a GUI. Parts of the procedure of building the business model in ‘author’ mode have a dated feel about them, which is not evident in the more graphical Application Designer modelling interface. The business model is a logical structure and acts as a mapping layer. Once this has been closed and committed, ‘base models’ defined using the business model are created and data is loaded into them. These base models can be combined to form ‘join models’, which are effectively views. For each base model, security levels have to be defined (carried out easily using a point- and-click interface) and a consolidation strategy selected. Models can also be created from SQL tables and other data stores (called scenario models). Scenario models can be stored locally for personal offline use or on a server for shared workgroup access.

Support for data cleansing and transformation Gentia offers more support than most OLAP tools for data cleansing and transformation. This can occur at two stages in the process: • when loading data into GentiaDB • when loading data into the application. When loading data into GentiaDB As with many extraction tools, GentiaDB uses a visual display of the data flow showing the data splits, aggregations and transformations. ‘Tasks’ are constructed to define and maintain dimensions and to load data into the database. A task consists of a number of transformation objects called ‘transformers’. As well as a number of provided transformers, developers can write their own in GDL and include them in the flow. The main transformers provided by the system are: • ‘converter’ – to perform currency conversions on data being imported • ‘expander’ – to add a text string to a field, either before or after the incoming value • ‘fielder’ – to add fields to the incoming data records • ‘logger’ – to view the output from another transformer. They can be placed at any point in the task • ‘qualifier’ – to validate details from an incoming data record against an existing dimension.

When loading data into an application Each page in an application requires at least one connector to link data to widgets and to manipulate the format of the data prior to display. The specification for where the data is to come from and how it is to be manipulated is defined by dragging connectors from the mapper keypad, shown in Figure 3, and connecting them to form a visualisation of the data flow, as shown in Figure 4. Connector types include source (to define an MDDB source), SQL (to define a SQL source), selector (to define the dimensions), sorter (to order the data in rows or columns), filter and calculator (to generate new values using the Gentia development language). A hierarchy connector is used to define the drill-down hierarchy. It will automatically identify the levels in the dimension from the model, but allows these to be edited, added to and deleted.

Making Smart Agents do the work Gentia supports Smart Agents, which are programs that automatically carry out certain tasks and can be scheduled or event-driven. They have data and methods that act on that data, but also have ‘beliefs, commitments and goals’. That is, a degree of reasoning is applied to the data available to guide the gathering of extra information, in order to achieve the goals that have been set. While Agents carry out the tasks assigned to them, this is supported by the Gentia Agency System, a network-wide infrastructure that co-ordinates the work of separate Agents. The Agents and Agency are machine- and platform-independent.

Figure 3 The Mapper keyboard

Figure 4 Data flows

Agents are defined using a graphical interface, as shown in Figure 5. Other types of Agent include: • Extractor, which automates text searches • Phone, which, as its name suggests, will initiate a dial-up connection • Director, which can be used to monitor changes in the source data files • Process, which can be used to trigger a GDL routine that might, for instance, read the source files and import and consolidate the analytical model.

Figure 5 Agency definition in the GUI

Future enhancements

Version 6.0.1 of G-MAP is scheduled for general release in the fourth quarter of 1999. The major planned enhancements include: • OLE DB for OLAP provider support − any OLAP end-user tools complying with this standard will be able to access the GentiaDB. Gentia is partnering Simba Technologies to embed the SimbaProvider OLE DB for OLAP products within Gentia • extended thin-client options − users will be able to access data via any Microsoft and Java web browser and a range of Citrix-based devices • new data administration components − for controlling and monitoring back-end data loading and processing tasks in the Gentia environment • consumer and provider support for OLE and ActiveX objects − allowing developers to embed external objects (for example, mapping and ERP transaction objects) within the Application Framework and to embed Gentia objects into third-party applications • a page-build API − allowing end users to customise application pages on- the-fly. Links will also be provided to the Application Designer for access to a range of predefined templates • support for VBScript and JavaScript − this will be added to complement the GDL scripting environment • enhanced capabilities for the GentiaDB − including advanced calculations, alternative and multiple time dimensions, selective disablement of consolidation of hierarchies and loading of data at different levels • a revised SQL architecture − Gentia will use Merant’s DataDirect and SequeLink drivers and manager to provide enhanced access to relational databases, using ODBC, OLE DB, JDBC and native SQL drivers. The drivers will be extended to include non-SQL sources such as Lotus Notes databases. Gentia is currently evaluating plans to optionally embed the K.wiz datamining (acquired from Compression Sciences) components into G-MAP and existing analytical applications. K.wiz will also provide the focus for the development of new applications for the e-commerce sector, specifically basket analysis and fraud detection. Gentia Software is also expanding its suite of packaged analytical applications. Two additional applications will be delivered in the second half of 1999: • Budget Impact, for budgeting and forecasting analysis. Gentia is currently seeking a domain partner for development • Traffic Optimizer, a network traffic analysis application for telcos, which is being developed in partnership with Bell Atlantic and Hewlett-Packard. Additional reseller partnerships for the RBSC application are also expected.

Commercial background

Company background

History and commercial Gentia Software, originally a UK company called Planning Sciences, was founded in 1983. The company’s first product was EIS-EPIC, a LAN-based EIS system. This was followed by Gentium, a Windows-based visual development tool that was first launched in 1993. The original product line focused on the areas of financial modelling and the production of EIS applications. It included support for all the major features of object orientation from its inception. Agent technology was introduced in 1994 and the multidimensional database, GentiaDB, was released in 1996. In 1997, the company changed its name to Gentia and launched new web-based and Excel Add-in products. The various components are now integrated and marketed under the brand Gentia Millennium Applications Platform (G- MAP). In March 1998, the company signalled a change of direction with the release of the Renaissance Balanced Scorecard (RBSC), a packaged application for implementing organisational strategies. It provides software support for the Balanced Scorecard management technique developed by Harvard Business School professor, Robert Kaplan, and Renaissance Worldwide Solutions president, David Norton; Renaissance remains a major partner and reseller. In May 1998, Gentia acquired a consultancy firm, Technical and Computer Management Services (TCMS), and the acquisition of Compression Sciences was completed in November 1998 for $3.1 million. Compression Sciences develops K.wiz, a Java-based datamining tool. Gentia is one of the smaller vendors in the OLAP market and went public in 1996. The company employs approximately 200 staff worldwide and has joint headquarters in London (UK) and Boston (US) and a network of international distributors. Core R&D is based in Ipswich (UK), although application development is also conducted in the US, Switzerland and Israel. Since the early 1990s, the company experienced steady growth, which has slowed down in the last two years. Revenues for fiscal 1998 grew less than 10% to $29.5 million. More worryingly, however, was the net loss of $15.6 million in 1998 (compared to a loss of $4 million in 1997). Around 60% of revenue comes from Europe.

Character and direction Gentia is one of the earlier OLAP vendors selling ‘big-ticket’ software. Until early 1998, Gentia Software was clearly defined as a company providing development tools for the production of enterprise-wide decision support systems. With the announcement in January 1998 of its strategic partnership with Renaissance Worldwide to develop the Renaissance Balanced Scorecard (RBSC) application, and the acquisition of TCMS later in the same year, Gentia has firmly indicated a repositioning from a technology- led approach to a solutions-oriented company. While Gentia intends to continue with its development tools, the main thrust is now to ‘sell high’ and ‘sell strategic’. The company is assembling a comprehensive suite of Enterprise Performance Management (EPM) applications aimed at medium to large-sized organisations. The RBSC

application, released in March 1998, uses a broad range of leading and lagging indicators – customer perspective, internal/business processes, learning, growth and financials – to evaluate whether a business is moving towards its strategic goals. Gentia also provides a range of Impact-branded applications for performance measurement, which are designed to run underneath RBSC, including: • Profit Impact, an activity-based management application for profitability analysis, developed in conjunction with Arthur Andersen. This is primarily a profitability analysis application, based on activity-based costing analysis. The analysis provides users with a view of profit, cost and revenue by product, customer and channel • Performance Impact, a tactical application used to track key performance indicators (KPIs) across an organisation. It integrates with RBSC to provide a more detailed measure analysis • Revenue Impact, for sales analysis, which has been developed in partnership with Decision Systems, an Israeli consultancy. All the applications have been developed jointly with partners that have application domain knowledge and expertise. Product endorsements from partners are key to successful market uptake and Gentia has established a Select Partner Program for delivering EPM applications. The adoption of these EPM applications is crucial to reviving Gentia’s flagging fortunes and returning the company to profitability. Gentia anticipates that once a customer has bought a packaged application, they may seek to buy the Gentia platform to integrate and enhance the packaged solution or to complement it with additional packaged applications. Gentia has more than 500 customers worldwide, including JP Morgan, Volvo, McDonald’s Restaurants and Sun Microsystems. There are currently around 45 RBSC customers, although this figure is expected to grow substantially in 1999. While Gentia’s OLAP business continues steadily, analytical applications represent the greatest opportunity to accelerate sales growth over the long term. In particular, Gentia is aggressively pursuing RBSC, by supporting it on all the major RDBMS platforms and by widening its strategic sales partnerships. Gentia also founded the Balanced Scorecard Technology Council (which has 3,800 members), which promotes market awareness and acceptance. Customer support

Support Global 24×7 telephone hotline support is available; this is based in Atlanta, Georgia, US with second line support provided by Ipswich, UK. Support is also available via the Web and online product enhancement and fault reporting facilities are provided. Support and maintenance is priced at 20% of the annual licence fee.

Training Gentia runs a variety of public or on-site courses to support customers, including a four-day introductory course, an advanced four-day course and shorter courses on specialised topics such as Gentia Agents.

Consultancy services Almost all purchasers of Gentia products buy consultancy of some kind. Most of it is provided by accredited global consultancies (Arthur Andersen, CAP Gemini and PricewaterhouseCoopers) and Technical and Computer Management Services (TCMS), which Gentia acquired in May 1998. Services accounted for 45% of Gentia’s revenues in 1998. Distribution Gentia has headquarters located in London (UK) and Boston (US). Europe Gentia Software Tuition House St George’s Road Wimbledon London, SW19 4EU UK Tel:+44 181 971 4000 Fax:+44 181 944 1604

US Gentia Software 201 Edgewater Drive, Suite 241 Boston, MA 01880 USA Tel:+1 781 224 0750 Fax:+1 781 224 4340

Asia-Pacific Gentia Software Singapore 89 Science Park Drive #04-06/07 The Rutherford Singapore Science Park Singapore 11826 Tel:+65 778 1678 Fax:+65 778 6884

E-mail: [email protected] http//:www.gentia.com

Product evaluation

End-user functionality

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Summary Gentia is primarily an application development environment, so the end-user functionality largely depends on what the developer builds into the application. However, the Application Designer and Excel Add-in tools provide a greater degree of out-of-the-box functionality for end users. Regardless of the tool used, all the Gentia applications provide the usual OLAP functions of drill-down and pivot. Distribution is supported by publishing reports using a ‘book, chapter, page’ metaphor for users and workgroups. However, the product would be enhanced by a wider range of front-end user tools and better support for subscribing to reports.

Finding and understanding the model Finding and loading a multidimensional model Gentia uses a ‘book, chapter, page’ metaphor to present applications to users. Books, or sections of books, are accessible depending on the user’s access rights. If the user has the correct permission they will see it, otherwise they will not. Search facilities and hypertext navigation allow rapid navigation through the system. Metadata for end users End users can access descriptive metadata about dimensions and measures by right-clicking when over the element. The metadata is non-structured descriptive text that is entered when the business model is built. There is no end-user access to metadata generated upstream by, for instance, extraction tools. Annotation by the end user There is no direct support, but it could be built into the application using the Gentia Development Language (GDL). The Object Library (part of the Application Framework) contains some sample code to illustrate this.

Using the model Basic OLAP functionality Drill-down/up and anywhere, slice-and-dice and pivot are supported by point- and-click. Within the application, it is straightforward to define alerts and colour-coded exceptions and include nested dimensions. The end user can locally define a new calculated measure. Changing the position of members in a dimension level Members can be repositioned using drag-and-drop functions.

Visualising the drill-down hierarchies It is possible to see the drill-down hierarchies using a pop-up window with a collapsible menu hierarchy. These windows can be customised for user presentation. Drillingdowntodetaileddata This could be achieved by combining a summary table and a detailed table in a page in the application. It is likely that the summary table will draw data from the GentiaDB and the detailed table from another source, such as a relational database. Using Agents, a parameterised query could be generated to populate the detailed table appropriately. An alternative method of achieving this is for the application to read off the dimension members from where the user clicked and to use these as selection clauses in the SQL. This is then sent to the SQL database by the client or passed to the Gentia Server for execution against the RDBMS. Range of front-end user tools Gentia applications can be accessed by the Gentia client, a web browser (if WebSuite is installed) and Microsoft’s Excel, but none of the well known end- user tools provided by third-party vendors, such as Brio, Business Objects and Cognos. There is a published API, which is compliant with the OLAP Council API, version 0.5, but it has not been widely supported. Visualising the results A number of display functions are supported, including tables, chart formats, 3-D graphics, value filtering, exception flagging and hot spots. It is possible to see multiple charts on the same page as tables from multiple sources.

Saving and sharing results Designing a report A Report Builder allows for the easy creation of reports, which include SQL query facilities. Bitmaps can be incorporated into a report, but there is no support for embedding OLE objects. The Application Designer interface relies entirely on the functions available in Excel for report layout and design. Publishing a report Users can publish reports to other users through the use of Gentia’s ‘book, chapter, page’ approach. Reports can be scheduled for publication using an Agent. It is possible to dynamically define groups using GDL. In the Excel Add-in interface, users can publish Excel templates via Gentia’s object store. Additionally, reports can be dynamically run and published to a web server in HTML. Targeted distribution via e-mail A MAPI interface is supported for report distribution via e-mail. Reports can be e-mailed directly from Gentia applications. Subscribing to reports There is no direct support to enable users to see available reports and elect to subscribe to them; the Gentia model for sharing information is for the user to create a report and allow other colleagues in their group to access it.

Building the business model

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Summary In the Gentia toolkit, the multidimensional business model is built in GentiaDB, but, because of the lack of end-user tools to directly access it, it will almost always be embedded in a Gentia application. The multidimensional business model in the MDDB can be accessed through its API, but the company claims this is not often requested or required. Although much of the specification is done using point-and-click, it is less easy to use than other MDDBs we have seen. The Application Designer provides a simpler interface for end users to define and populate models, although they are typically less complex. Areas that could be strengthened include support for more specialised calculated measures and flexible time dimensions, and the ability to capture and use metadata during the design process.

Basic design Design interface The design interface for dimension, member and measure definitions is largely via point-and-click. Visualising the data source The developer can see both the schema and a sample of data. Universally available mapping layer There is some support for this within GentiaDB, through the use of shared metadata. Prompts for metadata There are no prompts to add metadata.

Building the dimensions Selecting columns for the dimensions Columns are selected using point-and-click. Selecting the members shown in a dimension level Members in a dimension level are selected using point-and-click. Defining a dimension hierarchy Hierarchies can be created and edited using drag-and-drop; multi-level hierarchies are supported as well as the ability to include different hierarchies within a single dimension. Developers can also define unbalanced dimensions, and Gentia will automatically implement appropriate drill-down paths for them.

Time dimension There is support for defining time periods and spans, which can be defined dynamically (for example, year-to-date). There is no support for alternative time dimensions and limited support for defining multiple time dimensions in a single model. Annotating the dimensions The use of ‘display sets’ allows designers to add descriptive comments to dimensions. Default level of a dimension hierarchy This cannot be specified within GentiaDB; it has to be specified using dynamically populated filters for each user or group.

Defining the measures Calculated measures Calculated measures are created by typing in an expression made up of arithmetic operators and functions. Some mathematical, statistical and time functions are provided. More complex calculated measures could be added when the multidimensional model is used within an application. Support for multiple measures with a set of dimensions This is supported.

Multiple designers Multiple designers Once the model has been designed, it is ‘committed’, meaning that the data is loaded. GentiaDB supports ‘all or nothing’ consolidation to ensure that updates are not lost. Support for versioning GentiaDB provides version control. Advanced analytical power

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Summary Analytical functions can be built into the multidimensional business model when it is defined in the GentiaDB (or third-party OLAP source), and others added when the model is used within an application created in the Gentia VDE. A few analytical functions are provided within the MDDB and the application development environment. The company’s philosophy behind the product is that if complex analytics are required by users, they could be created as re-usable components using the GDL. The toolkit would be enhanced by a greater range of ready-to-use analytical functions, particularly for time-based calculations, and access to external functions.

Third-party tool integration An add-in enables Gentia business models to make use of the analytical functions in Microsoft Excel. There is no integration with third-party statistical tools.

Defining specialised models Ranking and sorting This is supported in GentiaDB and in applications built using the Gentia platform. In the MDDB there is a transformer with this functionality and, when developing an application, it can be incorporated using a connector. Within the connector the developer can specify whether the processing of the data takes place on the client or the server. Mathematical methods Gentia supports a range of standard functions, including integer, absolute value, exponential, logarithms, cosine, tangent and factorial. But there is no in-built support for complex mathematical techniques. Financial functions There is support for currency conversions (including the euro) in GentiaDB. The application development environment provides functions for lagging or leading co-ordinate values (shifting them back or forward one position), obtaining the net present value of an expression at a specified percentage rate and compound interest calculations. Statistical models There are statistical functions to obtain the mean value of a distribution, average deviation and skewness. Other statistical functions include moving average and kurtosis. Trend analysis There are functions for exponential smoothing. Simple regression There is support for single linear regression. Time series forecasting There is some support for defining values in a time span in the GentiaDB, making seasonal comparisons possible.

User-definable extensions Developers can create their own functions using the Gentia Development Language (GDL). The new functions can be stored and re-used.

Write back for ‘what-if?’ analysis Write back to GentiaDB is supported from client-server and web applications and the Gentia Excel Add-in client.

Incorporating non-numerical data Gentia supports a text object that allows text and narrative data to be stored, updated and queried. This functionality is prebuilt into the RBSC and Impact range of applications, but can also be incorporated into any Gentia application.

Gentia also offers a text management option called Text Infobase, which can be used for analysis of unstructured, text-based information. Agents can be used to feed information from this back into the system.

Data mining One of the sample applications provided with the product is an example of datamining. Further datamining applications could be developed using the GDL. Gentia acquired datamining technology (K.wiz) from Compression Sciences in May 1998. Gentia is currently investigating plans to integrate some K.wiz components into the G-MAP platform or future Impact applications. (see Future enhancements). Web support

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Summary Web access in Gentia is supported by Gentia WebSuite, which uses a CGI gateway between the usual web server and the Gentia server. It thus enables a web browser to access Gentia applications and base models. Version 5.0.2 has removed the need for developers to specifically design applications for web access, making WebSuite a more integral part of the product. Web applications offer excellent functionality; there is even a web version of Gentia’s flagship application, the Renaissance Balanced Scorecard. However, when accessing base models via the Web, the user interface lacks the sophistication and some of the functionality provided by desktop client access. One useful feature is that write-back via the browser interface is supported. But there could be better exploitation of the Internet as a distribution mechanism.

End user functionality via the Web Functionality of web access to models Tables and charts are displayed; tables using HTML support tables and charts using Java applets. Neither of them is an active object, as in the Gentia client, and drilling is carried out via hypertext links. Supports both registered and unregistered web access Unregistered users are not supported. Range of users supported by the web interface There is no special support for the production of EIS-style reports, but with appropriate design, a wide range of users can be supported. A feature of particular use to power users is the ability to write back to the MDDB via the web interface.

Creating models via the Web Editing the mapping layer Not applicable. Building and editing models While it is technically possible to create models via the Web, there are no prebuilt objects to make this a simple or practical process.

Distributing via the Internet and Web Generate HTML and Java The Report Manager module within the Gentia suite is primarily designed to support paper-based reporting, but can be used to generate static HTML pages for web publishing. There is no support for generating Java. Corporately organised distribution via the Internet There is no support for centrally organised targeted distribution via the Internet or the Web. However, ‘report packs’ can be scheduled for distribution over the Web using the Gentia Excel Add-in. Include URLs in a report URLs and custom Java applets can be included in an application.

Distribution of web server processing Some distribution of processing can be achieved using Agents. Management

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Summary There are two locations in which data and users have to be managed: the GentiaDB and applications developed in the Visual Development Environment (VDE). But singular administration is available for client- server and web environments. The Application Designer provides a single console to administer both application and data-level security. There is a good range of management facilities for controlling access rights, and security wizards provide access to cell level, along with the selective use of pages and objects. The management of large systems can be largely automated through the use of Agents. However, the GentiaDB would benefit from tools that allow systems administrators to easily control and monitor processing tasks and data loads, and view the GentiaDB log. The MDDB would benefit greatly from a more flexible method for consolidating hierarchies and tools that help define which measures should be precalculated or calculated on-the-fly.

Management of models Separate management interface Management of applications and their associated objects is carried out in author mode, through the Warehouse Manager or through the Application Designer interface. Security of models In the application development environment, Gentia has full security support to define access for authoring and using applications. Full data-level security, down to cell level, is also provided through security wizards. Query monitoring Facilities are available to track which users have accessed what application pages and models and when this occurred. There is a workable sample application provided in the Object Library to analyse this information.

Management of data How persistent data is stored (not scored) Multidimensional data is stored in the MDDB. Data retrieved by SQL calls to relational databases is always freshly retrieved. Scheduling of loads/updates Updates and schedules are organised by Agents, which can be scheduled using point-and-click. Event-driven scheduling This is well supported using Agents, and is specified mainly through the use of point-and-click. Failed loads/updates In GentiaDB there is a ‘logger’ transformer that can be used to collect details. The developer can specify how failed updates are handled, using GDL script within an Agent. Rollback is provided, and by dividing large updates into smaller tasks the consequences of failed loads can then be minimised. Distribution of stored data One of the great strengths of Gentia is that the data can be partitioned and distributed anywhere on the network. The ORB enables the application to access data with location transparency. Sparsity (only for persistent models) The GentiaDB solution to sparsity is a multicube architecture, in which each cube is made up of dense dimensions. Methods for managing size Within GentiaDB, all intermediate summary levels are precalculated at consolidation time (which may be defined as different from load time). Calculated measures can be either precalculated or calculated on-the-fly. This is a manual task and there is no wizard support. In memory caching options The cache can be configured to optimise performance of the MDDB through the cache parameter in the config file.

Informing the user when stored data was last uploaded There is no direct support for this, although it could be achieved with Agents or by writing each update to a text object, which can be presented to end users in an application.

Management of users Multiple users of models with write facilities GentiaDB uses the concept of ‘shadow’ pages for writing and consolidation. Only when write and consolidate are complete is the new data made available to others. Users reading the data always see a consistent dataset. User security profiles User security profiles can be set at an individual or workgroup level and are defined in the Application Designer or Warehouse Manager. Both user and author access are defined on a level between one and seven, allowing a fine level of control. Users are grouped into workgroups to enable sharing of information within a workgroup. Users can be in several workgroups. User security can be applied across client-server and web communities and can be dependent on access mechanisms. For example, administrators may restrict user or group access to an application either via a locally connected client or a web browser. Query governance This is not necessary for MDDB data and not available for SQL queries. Gentia relies on the underlying RDBMS for SQL monitoring and governance. While Gentia cannot stop an SQL query while it is running within an RDBMS, there is an option for limiting the number of records returned to the client. Restricting queries to specified times There is no direct support for limiting queries to certain times of the day or week, although they can be scheduled to run at specific times with Agents.

Management of metadata Controlling visibility of the ‘road map’ The visibility of applications is controlled by the Warehouse Manager, so that users can only see those to which they have access. Adaptability

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Summary Gentia does not have ‘out-of-the-box’ adaptability but, while requiring some programming, Agents can provide a powerful means of developing mechanisms that support adaptability of models and applications.

It is easy to adapt the application in the light of changing business requirements. It is possible, using Agents, to provide very sophisticated support to ensure that the data sources, models and applications remain synchronised at all times. However, this does require significant development effort. The tool does not provide support for impact analysis and change management.

Change in business requirements Adding new dimensions to a model Adding a new dimension to the model is a two-stage process. Firstly, it needs to be incorporated into the GentiaDB multidimensional database and then the application needs to be edited to include it in the appropriate page(s). Alternatively, dynamically generated pages can be set up to use the current number of dimensions available, although this is dependent on the underlying application functionality and process that is being modelled. The tracking of changes made to underlying models is supported by version control in the GentiaDB. With regard to applications, if a developer changes the definition of an object, that change will immediately be reflected in all pages and applications that use those objects. The book manager tracks who created what and when, but there is no explicit version control. Re-use of dimension definition Since the business model is a mapping layer, the dimension definitions can be named, saved and re-used. Adding new measures to a model New measures can be added directly to the application or, as described above, built into the base model in the MDDB and they will then, by default, be visible to any application using that model. Re-use of calculated measure definition Since the business model is a mapping layer, the calculated measure definitions can be named, saved and re-used. Changing the architecture to reflect business needs Gentia applications are primarily based on data stored in the MDDB, although they can incorporate data retrieved using SQL queries. However, these are not dynamically generated SQL queries and there is limited support for using these for multidimensional analysis. They can be processed into ‘scenarios’, which are essentially mini personal data cubes that can be viewed and manipulated like any other MDDB source. The Gentia platform does not support a ROLAP option, but can implicitly link into third-party ROLAP systems.

Changes to data sources Keeping the data source and model schema synchronised There is no direct support to ensure that the schema of the data source(s) is synchronised with the application schema. However, Agents could be used to check this before the application is run.

Automatic updating of members in a dimension Agents are extensively used to manage this. There are a number of options open to the application designer, including ‘add’, ‘ignore’, ‘add but warn’ and put into a special ‘unknown’ bucket.

Metadata Synchronising model and model metadata There is no end-user metadata to synchronise. Impact analysis There is no support for impact analysis to show the consequences of changing a model. Metadata audit trail (technical and end users) This is not applicable, as there is no real metadata to audit. Access to upstream metadata There is no direct integration for enabling the designer to access metadata generated by the extraction tools. Performance tunability

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Summary The distributed client-server architecture of the Visual Development Environment offers excellent support for allocating processing flexibly at the server level, on the client or through a combination of the two. Processing can also be configured down to the application page level; for example, an application page could be constructed that automatically switches to server- based processing for web applications, improving access speed and reducing network traffic. Within GentiaDB, performance tunability is largely dependent upon good design decisions, such as storing (caching) large dimension business structures locally and using the Gentia server to update those models. The tool would be enhanced by some wizard support for this process.

ROLAP A ROLAP option is not directly supported. Although data from SQL sources can be incorporated into any Gentia application, it will be displayed in tabular, rather than cross-tabular, form. Gentia can create ‘scenarios’ (virtual multidimensional cubes) dynamically from SQL data sources, but these are limited to what can be held in memory.

MOLAP Trading off load time/size and performance In GentiaDB, all intermediate summary levels are precalculated at consolidation time (which may be defined differently from load time). Calculated measures can be precalculated or calculated on-the-fly to speed

up performance and load times. GentiaDB also supports local caching of large dimension structures on the client, which can be updated by the Gentia server whenever the cached data is affected by fresh data loads. Keeping business model files in separate partitions or, better still, on separate disks can reduce load time.

Support for multiple users The flexible architecture in which processing can be carried out on the client or the server, and the support that Agents can give to the management infrastructure, enable thousands of users to be supported. Part of the application specification is whether (when it is run) the application is totally, partially or not at all cached on the local machine. If caching is selected, the models in the application are dynamically updated when re-used.

Processing Use of native SQL to speed up data extraction GentiaDB and the Gentia Visual Development Environment (VDE) use ODBC to access relational databases. The company ships Merant’s DataDirect and SequeLink drivers and manager. Native access to RDBMSs is provided by Merant’s SequeLink server software, embedded in G-MAP. Distribution of processing Processing can be carried out on the client or server, and is specified when the application is designed. A decision is made at page level. The underlying Object Request Broker means that the processing can make use of objects and services regardless of their location. SMP support GentiaDB (but not the Gentia Visual Development Environment) is based on a multi-threaded architecture that can take advantage of SMP. Customisation

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Gentia is designed from the outset as a platform for analytical application development, and offers a comprehensive range of services to support this. The Visual Development Environment (VDE) enables specialised OLAP applications to be developed quickly in a GUI environment, and supports a high degree of re-usability. Gentia also provides a friendlier, but simpler, environment for allowing business end users to develop their own OLAP applications using a template-driven interface. As a result of its distributed architecture, the applications can run on both Unix and Windows, using data stored on a variety of platforms. It is possible for the developer to produce sophisticated applications for the Web and for conventional desktop access (RBSC and Performance Impact are excellent examples of where equal functionality has been enabled for both environments).

Customisation Option of using a restricted interface As the end-user tool is nearly always an application developed within the VDE, a restricted interface to enable ease of use for occasional users may be required, which can be built into the application. Ease of producing EIS-style reports The VDE can be used to create both complex applications and simple EIS- type programs. Additionally, end users can create simple reporting applications from the predefined reporting templates provided by the Application Designer.

Applications Simple web applications VDE’s drag-and-drop and publish-and-subscribe development environment can be used to build a simple EIS application to be run in a web browser. The Gentia approach is to provide web access to all applications. Development environment As the development environment is specialised, there is a rich collection of features, such as tables with drill-down features and charts to speed up the process of building applications for multidimensional analysis. The development environment is supported by the Application Framework layer, which supports an extensive library of objects. The GDL can also be used to add greater functionality to applications. The development environment is OLE-compliant and there is extensive documentation, which is provided on a CD rather than as hard copy. Use of third-party development tools Gentia has a published API and it is possible to develop the applications in C++, Visual Basic or Java. In practice, this is seldom used, as the VDE offers specialised OLAP functionality, enabling faster development.

Other customisation features Localisation Gentia has excellent support for internationalisation and supports more than 30 languages in development mode; its multi-language capabilities are a consequence of its support for the Unicode UTF-8 translation standard. The display of different character sets, both single- and double-byte fonts, is supported regardless of platform. The localisation of Gentia is addressed through the use of message tables and developers can produce their own application message tables.

Deployment

Platforms Client The Gentia Visual Development Environment (VDE) client runs on Windows 95, 98, NT, Macintosh (PPC) and Sun Solaris. The Gentia WebSuite is supported on all current Gentia server platforms and supports the following browsers: Netscape Navigator, Microsoft Internet Explorer and Hot Java Browser. The Gentia Add-In for Excel runs on Windows 95, 98 or NT. It is supported on all Gentia server platforms, excluding Netware NLM. Server The GentiaDB and Gentia VDE Servers run on Windows NT and Unix (HP- UX, Sun Solaris, Unixware, AIX, Generic SVR4, Pyramid and NeXT). Additionally, Gentia VDE supports Netware NLM. Data access GentiaDB can access and load data from any ODBC-compliant relational database, including Oracle, DB2, Informix, Ingres, SQL Server, Sybase, Dbase, Paradox, Interbase, FoxPro and Btrieve. It can also access data held in ASCII flat files and Excel spreadsheets. Gentia is an OLE DB for OLAP consumer and can access third-party MDDBs that support this standard. Standards Gentia VDE supports the OLAP Council’s MDAPI version 0.5 specification. It has no plans to support version 2 of the OLAP Council specification. Gentia supports Microsoft’s OLE DB for OLAP API as a consumer, allowing it to access third-party MDDBs; Gentia plans to provide support as a data provider with version 6.0.1 due in the fourth quarter of 1999. Published benchmarks Gentia does not have any published benchmarks. Price structure Pricing is based on the number of named users with a built-in volume discount; there are six pricing bands, each with a ‘per user’ price. A 50-user licence costs approximately $150,000. Pricing for RBSC and Impact applications is also based on named users. In addition, a restricted user licence (RUL) is available to provide customers with the ability to build or customise applications using G-MAP component modules. Gentia will incorporate training in pricing models for future releases.

Summary

At a glance ...... 3 Terminology of the vendor ...... 4 Ovum’s verdict...... 5 Product overview ...... 7 Future enhancements ...... 13

Commercial background

Company background ...... 14 Distribution ...... 16

Product evaluation

End-user functionality ...... 17 Building the business model...... 18 Advanced analytical power ...... 20 Web support ...... 22 Management ...... 23 Adaptability ...... 25 Performance tunability...... 26 Customisation ...... 27

Deployment

Platforms ...... 29 Data access ...... 29 Standards ...... 29 Published benchmarks ...... 29 Price structure ...... 29 Evaluation: Hummingbird – BI/Suite Ovum Evaluates: OLAP

At a glance Developer Hummingbird Communications, North York, Ontario, Canada Version evaluated BI/Suite version 5.1, comprising BI/Query version 5.0.2, BI/Analyze version 5.1, BI/Web version 2.0 and BI/Broker version 2.0. Key points • An integrated desktop query, OLAP and reporting tool • The server runs on Windows NT and Unix; clients run on Windows 95/98/ NT and Java 1.1-based web browsers • BI/Suite is based on OLAP and query tools developed by Andyne Computing, which Hummingbird bought in January 1998 Strengths • A tightly-integrated suite of tools that is easy to use and manage across client-server and web environments • Supports a distributed architecture with load balancing across multiple servers • Client tools connect to a wide range of third-party relational and multidimensional databases Points to watch • Accessing data from relational databases to build the model needs BI/ Query, Hummingbird’s query and reporting tool • No support for complex analytical analysis • Limited range of front-end tools – HyperCubes can only be analysed using the BI/Suite client tools Ratings

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End user functionality

Building the business model

Advanced analytical power

Web support

Management

Adaptability

Performance tunability

Customisation

Terminology of the vendor App Handlers Server-based components that handle client connections to underlying data sources for query, OLAP and report processing. Crosstab Similar to a dynamic spreadsheet, this tool is used to display and analyse data in a multidimensional model. Data model A graphical representation of the contents of a relational database in familiar business terms. The data model acts as a semantic layer that allows end users to query and access data using point-and-click. A data model only exists in BI/Query and should not be mistaken for a multidimensional data model. HyperCubes These are small, multidimensional cubes that are stored in specially-formatted files and downloaded on to the BI/Analyze client for subsequent OLAP analysis on the desktop. HyperCubes are processed on the BI/Broker when accessed by BI/Web. HyperCubes are built with a query result set using the BI/Query tool or a flat file. Levels Different levels of aggregation in a dimensional hierarchy. Members Individual components or sub-categories in non-numeric dimensions; for example, ‘the US’, ‘Canada’, ‘Ohio’ and ‘Ontario’ are all members of a ‘region’ dimension. Members can be shared within a dimension. Metric Corresponds to Ovum’s definition of a measure. A metric can represent a single column of numeric data or can be derived from a calculation. Presentation A report that contains crosstabs, charts and OLE objects. A presentation can include ‘live’ data in the form of a HyperCube or any supported multidimensional source accessed natively or via OLE DB for OLAP.

Ovum’s verdict

What we think BI/Suite provides an easy-to-use set of tools; its greatest strength is its simplicity. The client-server and web-based end-user tools provide immediate access to data – even to inexperienced users with minimal training. Users also benefit from the ability to schedule reports, refresh HyperCubes and share them through BI/Suite’s mid-tier broker server. Support for a single-server architecture gives users the benefits of a shared report and metadata repository, and load balancing. The payoff for administrators comes with centralised management and administration. Both thin and fat clients use the same content, security and user profiles from a single server, thereby eliminating the hassle of managing multi-server set-ups. While BI/Suite firmly establishes Hummingbird in the enterprise query and reporting space further development is needed for its to qualify as an enterprise OLAP solution in its own right. The client-server (BI/Analyze) and web-based (BI/Web) OLAP clients satisfactorily cope with general business intelligence needs; however, they are not suited to highly complex and specialised analysis. Users that need this level of functionality will have to export data to third-party tools such as Excel. Native and OLE DB for OLAP access is provided for a range of third-party MDDB servers; however, the product’s client-centric architecture restricts the size of the HyperCubes (generated from a direct query to the relational database) that can be effectively downloaded and analysed on the BI/Analyze desktop environment. Hummingbird is rapidly establishing a presence in the business intelligence market, largely through acquisitions. BI/Suite is Hummingbird’s first product in this market. The company’s future success will depend on: • how well it can integrate BI/Suite and its recently-acquired data transformation, financial software and knowledge management technology • whether it can market this portfolio in a coherent way.

When to use BI/Suite is most suitable if you: • want a simple out-of-the-box solution that can be easily rolled-out across the enterprise • want to provide integrated query, OLAP analysis and reporting capabilities to general business users, with minimal training requirements • want to perform OLAP on small datasets sourced directly from a variety of relational databases or flat files • have to distribute small business models across the enterprise.

BI/Suite is less suitable if you: • want to build complex models with large numbers of unique members • require the analysis of data using advanced or specialised analytical functions • do not want to use two tools to access and analyse RDBMS data – BI/ Query is needed to efficiently build models based on relational sources • want to build custom analytical applications.

Product overview

Components The important components of BI/Suite version 5.1 are: • BI/Query version 5.0.2 • BI/Analyze version 5.1 • BI/Broker version 2.0 • BI/Web version 2.0. Figure 1 shows the primary functions of the components and how they relate to client-server systems. Although Ovum Evaluates: OLAP covers the entire suite of BI/Suite tools, the main focus of this evaluation is the OLAP functionality provided by BI/ Analyze and BI/Web. BI/Query This is primarily an end-user tool for building ad hoc queries. It provides a graphical interface for querying relational databases and generating reports. BI/Query simplifies the process of data access by creating a semantic layer (data model) that provides a graphical representation of the structure of a relational database in familiar business terms. Results sets can be used to: • create standard reports (using the integrated report writer) • act as data sources for creating multidimensional models (HyperCubes). This indirect method is used to incorporate data from relational databases and transaction sources into multidimensional data structures defined in BI/ Analyze. BI/Query is based on an enhanced version of Andyne’s GQL (Graphical Query Language).

Figure 1 Component functions

Ad hoc query OLAP analysis Web support

Client BI/Query BI/Analyze BI/Web

Server BI/Broker BI/Broker BI/Broker

BI/Analyze This is an end-user tool for desktop OLAP analysis. It includes a CubeCreator facility for building HyperCubes from BI/Query results sets, as well as other flat-file data sources. Native access is provided for Hyperion Essbase and Informix MetaCube. BI/Analyze can also connect to other third- party MDDBs that support the OLE DB for OLAP interface as a data provider. BI/Analyze is offered as a standalone desktop OLAP client or as a component of BI/Suite. BI/Analyze is an enhanced version of Andyne’s Pablo analysis tool. BI/Broker This is an application server that provides shared services, security and administration functions. It includes a central repository that stores all data models, queries, results sets, reports and their associated metadata. Admin- istrators can publish information for multidimensional data sources to the repository (although data sources are not stored there). BI/Analyze clients access data sources via the repository. BI/Web uses the App Handlers within the server to handle connections to data sources. BI/Broker is built on a CORBA architecture. Visigenic’s ORB technology is used to deploy application services as distributed objects over the network. BI/Broker provides three main interfaces for managing the query and OLAP environment: • Administrator – primarily addresses server variables such as repository directories and mapping the HTML links for BI/Web • User and Group Manager – for end-user management and security setting • Scheduler – to schedule queries and refresh reports based on time- or event-driven criteria. Other components of BI/Broker include a load-balancing utility and a Session Manager tool for managing user connections. BI/Web A thin client interface that provides query, OLAP analysis and reporting functions via a web browser. BI/Web uses three main Java applets to gain access to BI/Broker services. XML is used to render all content – reports, data, models and query results – to web users. The BI/Web interface is based on Java-based OLAP technology licensed from Internetivity.

Architectural options Full mid-tier architecture BI/Suite does not support a full mid-tier MDDB architecture; however, BI/ Analyze can implicitly link to these environments as a front end.

Light mid-tier architecture A thin-client variant of a light mid-tier architecture is supported by the inclusion of BI/Web and BI/Broker. In this configuration, BI/Broker acts as a mid-tier application server and offloads processing from the client to the server. It also provides a central repository for storing models, reports and metadata. In a client-server environment, BI/Broker provides mid-tier architecture for security access, publishing, scheduling, notification and distribution. Its role is not to handle processing, but to manage throughput and the flow of data to clients. Desktop architecture This is the natural architecture for BI/Analyze; it creates multidimensional cubes for desktop analysis. All OLAP processing is performed on the client. Mobile architecture BI/Analyze has a standalone OLAP engine to support a mobile architecture. Mobile users can choose to work offline by packaging a report and ‘slicing off’ data from a HyperCube or a third-party MDDB server. Data can be refreshed when the user is reconnected to the data source.

Using BI/Suite Query first, then analyse To analyse data from a relational database or a transactional data source in a multidimensional cube, a query definition has to be defined in BI/Query to extract the data. The results set then appears as a data source in BI/ Analyze. The query and the cube-creation environments (CubeCreator) are tightly integrated; a multidimensional structure is generated via point-and- click. BI/Query provides an easy-to-use graphical interface to define queries. As shown in Figure 2, it uses a ‘data model’ to provide a graphical representation of the database. The data model acts as a mapping layer through which end users can query the database and return a subset of data. Icons in the data model can: • relate to database tables • be used as virtual tables that include joins from multiple tables or calculated attributes. BI/Query provides a number of design windows for creating data models. These windows are used to specify data objects (which represent tables in the database) and the relationships that tie them together. The data objects are the starting point for building queries that retrieve information from a database. Queries are built by selecting the attributes from tables. Users can use more than one object in the data model to build a query. BI/Query also provides a facility for incorporating prompts into queries that make users enter a value into a qualification.

Figure 2 BI/Query data model

It is not possible to use more than one query or data source to create a HyperCube in BI/Analyze; in BI/Query, however, it is possible to combine multiple queries into a single query. This query consolidates data from multiple databases into a single source file before it is imported into BI/ Analyze. Using BI/Query results sets as data sources for BI/Analyze is the most efficient way of accessing relational data. Users can also create their own data sources directly from other processes that return query results in a comma- or tab-delimited flat-file format; saved BI/Query results sets are also delimited text files. All the HyperCubes built this way are ‘local’ HyperCubes – that is, stored on the hard drive or on a network server – and can be distributed to end users. Designing and building HyperCubes using CubeCreator Having created a results set, end users can use the CubeCreator to design and build a HyperCube for OLAP analysis. The first stage is to create a logical design for a HyperCube. CubeCreator provides two graphical interfaces for designing a multidimensional cube – the Designer and the Editor. The Designer The Designer is used to create HyperCubes from source data. Users can modify the design by rearranging and renaming the columns and consolidating detail data into summarised information. Designer’s AutoDesign feature reads the source data and designs the HyperCube based on the relationships that it finds. It scans a specified number of rows in the results set and – based on the correlation it finds – groups the related columns into levels within dimensions. It also identifies most date columns, breaking them down to yearly, quarterly and monthly

levels within a date dimension. It also understands that numeric columns are metrics that belong in a metrics dimension. An ‘arrange columns’ dialogue box is provided to rearrange or rename the columns specified by the AutoDesign function. Designer’s ‘learn as you go’ feature automatically applies these changes the next time a similar HyperCube is built. Developers can also specify templates (created using the Editor tool) to generate a design automatically. After designing a HyperCube, it can be physically built. Developers can refresh the HyperCube with data whenever there is a change in the structure or underlying data source; they can also modify the design, adding greater complexity into the model. The Editor The Designer is useful for generating a ‘starter’ set of dimensions. As shown in Figure 3, developers can fine-tune the multidimensional model, adding greater complexity to its structure using the Editor interface. The Editor allows developers to design more advanced HyperCubes by working with the metadata. The metadata includes information about the underlying data (such as its location or the query that generated it), including attributes, prompts, variables and qualifications. The Editor can: • create calculated metrics • group metrics into hierarchies • create and delete dimensions, levels and members • create shared members • build custom asymmetric hierarchies. Developers can also set up the HyperCubes to refresh incrementally and design ‘template’ HyperCubes without data. Templates contain metadata about how columns should be organised into dimensions, levels and metrics. Using multidimensional data sources BI/Analyze can be used to access predefined multidimensional data: natively from Hyperion Essbase and Informix MetaCube or from third-party MDDB servers that support OLE DB for OLAP as data providers. HyperCubes based on larger multidimensional data sources are called ‘sliced’ HyperCubes. A sliced HyperCube is similar to a local HyperCube. Developers can: • specify the dimensions and members that they want to include • modify the structure using the Designer/Editor interface • send it directly to BI/Analyze to create reports. It is possible to build sliced HyperCubes from other HyperCubes as well as from views created in BI/Analyze. Sliced HyperCubes are suitable for mobile users that need fast, easy access to specific data in a large data source. They are not suitable, however, if the data being accessed is volatile and users need up-to-date data for analysis.

Web access to data using BI/Web BI/Web provides query, OLAP and reporting capabilities via a Java-capable web browser. The main entry-point for web users is the Personal Portfolio, as shown in Figure 4, which uses an Explorer-like interface for organising data models, reports and HyperCubes into a folder-like system.

Figure 3 The Editor interface

Figure 4 Personal Portfolio

Future enhancements A number of minor maintenance releases are planned for 1999. Humming- bird will port BI/Broker to Solaris, HP-UX and AIX platforms in mid-1999. The next major release is due in mid-1999. Major enhancements include: • the ability to drill through to detail-level data • additional OLE DB features, such as support for dimension member properties, multiple hierarchies and writeback • enhancements to security • tighter integration with Excel • the ability to automatically load metadata (such as business terms and short descriptions) into BI/Query data models from the Informatica metadata repository • increased analytical functions in the client tools • enhancing the upper limits on the amount of data that can be stored in a HyperCube and moving more processing tasks (such as filtering and ranking) to the server. In the longer term, support for accessing Oracle Express multidimensional models is also planned. Integration will also be provided with third-party metadata repositories for streamlined development. A wizard facility will be provided, which will guide users in building BI/Query data models based on the existing metadata. The metadata will also be accessible by BI/Analyze when building HyperCubes. Hummingbird intends to integrate its newly-bought extract, transform and load (ETL) technology with its other products in order to provide an end-to- end datamart solution. Detailed product announcements will be made in late 1999. The intention to buy PC Docs will spur the development of an ‘enterprise knowledge portal’ strategy; this strategy will integrate BI/Suite with PC Docs’ document and knowledge management framework and Financials and Case Management systems.

Commercial background

Company background History and commercial Hummingbird Communications is a Canadian software company. The company was founded in 1984 as a consulting firm; it changed direction shortly afterwards to become a developer of PC-to-host connectivity and terminal emulation software. It has three main products in this area: • Exceed – a PC X server that connects Windows PCs to legacy X and Unix applications • NFS Maestro – designed to connect PC networks and host computer systems • HostExplorer – terminal emulation and connectivity software that links PCs to IBM mainframes, and the AS/400 and Unix systems. Hummingbird achieved rapid growth in the core network connectivity market – it holds just under a 70% share of the PC X server market. Connec- tivity software remains a profitable business for the company. As the market began to plateau, Hummingbird reviewed its business direction and markets. In January 1998, the company diversified into the business intelligence market by buying Andyne Computing for $60 million. Andyne developed two products: • GQL (Graphical Query Language) – a query and reporting tool • Pablo – a desktop OLAP tool. These two products were rebranded as BI/Query and BI/Analyze. They were integrated into the BI/Suite, which was first released in July 1998. In March 1999, Hummingbird announced that it intended to buy three other companies: • PC Docs Group International, a US company that develops document and knowledge management software. If PC Docs is bought by Hummingbird, it will be the first evidence of knowledge management and business intelligence technology coming together • Context, a New York-based company specialising in software and consultancy services for the financial industry. Context’s main product is Financial Frameworks, a packaged software solution aimed at the financial services sector • Leonard’s Logic, developers of Genio, a data extract, transform and load (ETL) tool Hummingbird completed its first IPO in 1993 in Canada; it later issued two more public offerings in the US. The company is quoted on the Nasdaq and Toronto stock exchanges. Hummingbird employs more than 800 people; its headquarters are in North York (Toronto), Canada, with offices and distributors worldwide. R&D is based in Montreal, Quebec and Toronto and Kingston (the original headquarters of Andyne), Ontario. Revenues for the 1998 fiscal year (excluding

revenue from bought companies) grew by 10% to $130 million; net income was $26.6 million. Around one-quarter of the revenues come from business intelligence tools. The company’s biggest market is North America, which accounts for approximately 60% of sales. Character and direction Although Hummingbird’s network connectivity business still provides substantial revenues, the company is targeting the business intelligence market to drive future growth. Hummingbird’s newly-formed Business Intelligence Division can be regarded as a start-up within the company. Hummingbird’s presence in this market is based largely on the query and analysis tools that it bought from Andyne. Andyne’s business intelligence products had not previously been marketed and promoted particularly well – they were not widely recognised in the enterprise OLAP market. Hum- mingbird has introduced a three-tier architecture and implemented a more focused sales and marketing strategy to rectify this. The acquisition of Leonard’s Logic and its ETL products, enables Humming- bird to offer a turnkey solution for deploying datamarts integrated with a range of business intelligence capabilities – reflecting the general business intelligence trend towards users seeking end-to-end solutions. Other planned acquisitions also open up wider markets for Hummingbird to exploit: • Context’s Financial Framework technology will enable Hummingbird to enter the financial sector, providing analytical applications for credit risk management, profitability analysis and sales prospecting • PC Docs will strengthen Hummingbird’s strategy to encompass the enterprise knowledge management market. Planned integration between the two companies’ products will spur the development of integrated business intelligence and knowledge management solutions, as described in Future enhancements. Hummingbird has the potential to become a significant player in all these markets if it can: • avoid the usual problems of mergers • tightly integrate its bought technology • devise a coherent marketing strategy. Hummingbird claims that more than 3,500 user sites worldwide have installed its business intelligence tools. Major OLAP (BI/Analyze) customers include the UK’s Inland Revenue (45,000 users), the Nationwide Building Society, Compaq, Conrail Consolidated Rail, Lockheed Martin, Whirlpool and Teachers Insurance. Hummingbird’s products are sold directly to large Fortune 1,000 companies and indirectly through resellers, OEM partners, VARs, systems integrators and 40 worldwide distributors. Hummingbird has established close relationships with software, systems and applications vendors through its accredited QuickStart programme.

Customer support Support Worldwide technical support is provided by Hummingbird and its distributors and VARs via telephone, e-mail, fax, the Web and an electronic bulletin board system. Support (including upgrades) is priced annually at 20% of the overall licence fee. Training Training courses for all components of BI/Suite are available on-site or at training centres in Canada, the US and Europe. Courses include one-day introductory classes or two- and three-day intensive programmes for users and administrators. Training for resellers and distributors is offered. Consultancy services Hummingbird’s Professional Services Group provides services for implementation, and project and strategic consultancy. In April 1998, Hummingbird bought Datenrevision, a specialist German data warehousing consultancy.

Distribution North America Hummingbird Communications 1 Sparks Avenue North York Ontario M2H 2W1 Canada Tel: +1 416 496 2200 Fax: +1 416 496 2207 Europe Hummingbird Communications 66 rue Escudier 92774 Boulogne Cedex France Tel: +33 1 41 10 0505 Fax: +33 1 41 10 0500 Asia-Pacific Hummingbird Communications Level 19, AGL Centre 111 Pacific Highway Sydney NSW 2060 Australia Tel: +61 2 9929 4999 Fax: +61 2 9956 6442

http://www.hummingbird.com E-mail: [email protected]

Product evaluation

End-user functionality Summary

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BI/Suite’s greatest strength is its easy-to-use graphical interface for ad hoc query, analysis and reporting functions. The extensive use of wizards guides end users through the query, analysis and reporting cycle. BI/Analyze provides a number of sophisticated mechanisms for navigating through a HyperCube, but the range of front-end tools is limited; there is no Excel add-in or scope for using third-party OLAP clients, for example. BI/Broker enables reports to be distributed across the enterprise via e-mail and other push channels. Finding and understanding the model Finding and loading a multidimensional model Users are provided with a profile that shows which data models and HyperCubes they can access in the repository. These can be grouped into public or personal folders for access. There are no search facilities, however. Metadata for end users End users can view metadata about data models and HyperCubes (for example, the topic, author, when it was created and when it was last refreshed). Annotation by end users HyperCubes cannot be annotated directly; however, end users can add textual prompts to reports and change the description of data sources. Using the model Basic OLAP functionality Multidimensional data is presented to end users in the form of a crosstab presentation. Support for standard OLAP (such as drill-up/drill-down and slice-and-dice functions) is provided using point-and-click. Users can also drill-down on individual data cells to analyse exceptions. Colour-coded traffic lighting can also be set on data cells. Changing the position of members in a dimension level The position of members can be changed using drag-and-drop. Visualising the drill-down hierarchies An Explorer-type dialogue box shows the drill-down hierarchies graphically; however, there is no support to show the current position within it. As users move the cursor over the elements of a crosstab, it changes to a plus or minus sign to show the ability to drill-up or drill-down to data.

Drilling-down to detailed data Drilling-down to detailed transactional data is not supported; however, users can easily return to the original query results set for a HyperCube. Range of front-end user tools End users are limited to the end-user tools provided by Hummingbird. An Excel add-in for directly accessing multidimensional data is not available. Visualising the results Data can be presented in tables, crosstabs or a variety of chart formats (including two- or three-dimensional line, bar, area and pie charts; scatter histograms and spectral maps). An optional link to MapInfo, for the spatial representation of data on a map, is also available. Saving and sharing results Designing a report Guided by presentation wizards, users can easily create reports that contain tables, crosstabs, nested dimensions, charts, graphics and OLE objects. Multiple charts and crosstabs can be viewed on the same screen. Drill-down is supported on charts; a report can contain data from different sources. Local calculations can also be defined in reports to create additional rows or columns in a table. Standard arithmetical operator, string, aggregate, date/ time and logical operator functions are provided. Publishing a report Reports and queries can be published to specific users or groups of users. Distribution channels include e-mail, FTP, the Web and third-party ‘push’ channels (for example, Pointcast or Microsoft’s ActiveDesktop (CDF) channel formats). Push notifiers send messages to users when information from a push channel becomes available. Targeted distribution via e-mail HyperCubes and reports can be distributed via e-mail to other users. The SMTP, VIM and MAPI mail standards are supported. MAPI allows users to e-mail HyperCubes directly from CubeCreator’s Editor interface. Subscribing to reports Subscription services are not supported.

Building the business model Summary

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The strength of the tool lies in its ease-of-use – notably, the ability to define the structure of a model quickly. CubeCreator provides a user-friendly graphical interface that makes extensive use of wizards and design templates. An AutoDesign feature eliminates most of the initial work in designing HyperCubes by creating a ‘starter’ set of dimensions, measures and hierarchies that can be further refined.

However, the tools are geared towards general business modelling rather than creating large, specialised models with complex calculations. Developers can only access a single data source to build a model. Using BI/Query to access SQL data prevents a sample of source data being available to the model designer (although OLAP users can bring up a view of the query results set). Basic design Design interface CubeCreator provides an easy-to-use graphical interface for model design and incorporates some sophisticated design features. The AutoDesign facility automatically builds HyperCubes from a BI/Query results set. CubeCreator automatically parses the results set and builds a ‘starter’ set of dimensions that can be further refined using wizards, design templates and a drag-and- drop Editor interface. Standard design templates can be built and re-used for future design. Visualising the data source It is possible to view a sample of the query data on screen; however, the underlying database tables cannot be viewed. Universally available mapping layer The data model acts as a mapping layer for query only. Prompts for metadata When building HyperCubes using flat files or BI/Query results sets, CubeCreator generates most of the metadata (including its location, attributes, prompts, variables and qualifications). It also describes the structure of the data in the HyperCubes and includes meaningful descriptions for members, levels and dimensions. Developers can include additional metadata when creating a HyperCube (such as the author, details and a description), but they are not explicitly prompted to do so. Building the dimensions Selecting columns for the dimensions CubeCreator’s AutoDesign feature automatically arranges the columns of a BI/Query results set or a flat file into dimensions. Alternatively, columns can be selected or excluded manually using point-and-click. Selecting the members shown in a dimension level Designers can add dimension members by checking off the dimensions using point-and-click. Defining a dimension hierarchy Designers can selectively edit data branches to create asymmetrical hierarchies that include different levels of detail in the same dimension or leave only summary data. Dimension members can also be placed in multiple hierarchies; Cube Creator automatically adjusts the roll-up totals to avoid double-counting.

Alternative drill-down hierarchies are not supported. Time dimension CubeCreator’s AutoDesign feature automatically identifies date columns, building time dimension based on calendar or fiscal year time periods. Custom time periods can also be defined, but only if they are based on the corresponding date levels that exist in the source data. There is no support for dynamically-defined time dimensions such as ‘year to date’. Annotating the dimensions Model designers can rename the descriptions of the columns that become dimensions, levels and metrics in a HyperCube for increased readability. These naming conventions can be filtered through to reports. Default levels of a dimension hierarchy These can be specified when saving a report. A ‘home’ key can return a user to a default level in the hierarchy. Defining the measures Calculated measures Calculated measures (called calculated metrics) can be created in CubeCreator’s Editor interface using point-and-click. The range of functions is limited to standard arithmetical operators, constants and conditional expressions. Support for multiple measures with a set of dimensions Multiple measures can be associated with a dimension. Multiple designers Multiple designers Security governs reading and editing access to HyperCubes. HyperCubes are locked when they are being edited by a developer, but there are no check-in/ check-out facilities. Support for versioning There is no support for versioning.

Advanced analytical power Summary

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The calculation functions supported by BI/Analyze are limited to ranking and sorting functions, and simple arithmetical and statistical functions. Users will need to export data to third-party tools (such as Excel) for advanced calculations and forecasting applications – direct integration with such tools is not provided.

Third-party tool integration There is no direct integration with Excel or specialised third-party analysis tools. Data can be exported to these tools in a variety of formats, however. Defining specialised models Ranking and sorting Standard ranking (top or bottom by per cent) and sorting (ascending or descending) functions are supported. More complex sorts on nested dimensions are also supported. BI/Analyze supports server-side ranking functionality provided by third-party MDDBs that can be connected to. Mathematical methods Standard arithmetical calculation functions are supported. The Calc-O- Matic tool provides predefined calculations (such as sum, difference, percentage difference, count, minimum and maximum) that are easy to apply to data. Financial functions There is no support for special financial functions. Statistical models Only simple statistical functions (such as average, minimum and maximum) are supported. Trend analysis There is no support for trend analysis. Simple regression Regression-based forecasting algorithms are not supported. Time series forecasting There is no support for time-based forecasting algorithms. User-definable extensions A non-procedural language is not provided for defining complex functions. Custom calculations can be built using nested rules. Writeback for ‘what if?’ analysis Writeback is not supported. Incorporating non-numerical data Analysis is restricted to numerical data only. Data mining There is no direct support for data mining. Hummingbird has a partnership with Angoss for integrating data mining capabilities.

Web support Summary

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BI/Web provides the same intuitive interface as BI/Query, with the addition of a Java-based navigation bar. The web interface provides the same drag- and-drop OLAP functionality as the fat client, with some exceptions: the HTML export option is basic, limited to BI/Query-generated reports and graphs only; and charts created in BI/Analyze cannot be exported in HTML format. End user functionality via the Web Functionality of web access to explore models BI/Web provides full ad hoc query and OLAP analysis capabilities via the web browser. Similar functionality to the fat-client tool is provided through a drag-and-drop graphical interface. The ability to create local calculations and full charting capabilities is also supported. The only major difference is the inability to set exceptions in reports. Each web user has a personal workspace (called a Personal Portfolio) that can be customised in a Java applet file or folder. Supports both registered and unregistered web access All web users must be registered and named users. Range of users supported by the web interface BI/Web provides support for advanced users that want to access interactive reports for ad hoc query and analysis of data, and casual users that simply want to view ‘canned’ reports. Creating models via the Web It is not possible to create new multidimensional models via the Web. Distributing via the Internet and Web Generate HTML and Java Reports can be saved in HTML or Java for publishing over the Internet. The HTML export option is basic and applies only to reports and graphs created with BI/Query. BI/Analyze cannot export graphics in HTML; instead, it converts chart formats into generic text tables. Text formatting is not pre- served. Corporately organised distribution via the Internet Distribution allows you to distribute queries, results sets and HyperCubes via the Internet using web pages, e-mail, FTP and third-party ‘push’ channels. Include URLs in a report It is not possible to include URLs in reports.

Distribution of web server processing BI/Broker uses Visigenic’s object request broker to load-balance processing over multiple servers.

Management Summary

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The simplicity of the toolset lends itself to easy management. BI/Suite’s single-server architecture provides a central point of control for web and client-server environments, and eliminates the burden of managing multi- server set-ups. The scheduling tools and security scheme are flexible, and help to manage large user environments. There are limited facilities for governing queries and monitoring performance, however. Management of models Separate management interface BI/Broker provides several graphical administration interfaces for managing data, schedules, push channels and end users. Security of models Security can be set on database table attributes and rows, and elements of a HyperCube. Query monitoring Simple query statistics can be traced, logged and analysed using the client tools. There is a facility to log performance metrics based on queries. Management of data How persistent data is stored (not scored) Data can be stored on the server – BI/Broker or a remote file server – or locally, on the client. In a client-server configuration, HyperCubes are always downloaded on to the client machine for processing. In a thin-client architecture, processing occurs on the server. Scheduling of loads/updates BI/Analyze can launch BI/Query to load or update data. HyperCubes can be scheduled in this way to refresh at specified time periods (hourly, daily, weekly, monthly, annually or custom). BI/broker notifies users of refreshes by sending an e-mail or a broadcast message, or distributing it via FTP. Event-driven scheduling HyperCubes can be scheduled to load or update data based on external events; for example, an update to the data source.

Failed loads or updates BI/Broker monitors all scheduled jobs using tracing and logging utilities. A notification facility alerts administrators (via e-mail) about particular events; for example, the completion of a scheduled job, or errors. There are no automatic rollback or retry facilities. Distribution of stored data Data can be stored on the client or across multiple servers. Sparsity (only for persistent models) HyperCubes use a hashing technique to handle sparsity; they do not store sparse rows. Methods for managing size HyperCubes are subject to size restrictions; the maximum size of a HyperCube is limited to 64 bits and 20 dimensions. Managing the size and build time for HyperCubes is achieved by: • consolidating data on-the-fly – calculating values when data is requested rather than at build time. CubeCreator supports full and partial consolidations • incremental updates – for loading and updating large datasets on the desktop. It is not possible to incrementally refresh a sliced HyperCube • compression techniques. In-memory caching options In-memory caching options are not supported. Informing the user when stored data was last uploaded End users can see when the HyperCube was last edited and/or updated when opening a report. Management of users Multiple users of models with write facilities Writeback to models is not supported. User security profiles Object-level security is managed via the Access Control Manager. BI/Broker’s User and Group Manager interface provides a drag-and-drop interface for creating and administering security objects. Security profiles can be created to govern access to HyperCubes. Once users and groups have been established, system permissions (which determine what services users can access) and access privileges (which determine what items in the repository users can see and use) can be assigned. Permissions can be set on an individual, group or business role level. The tool supports a flexible system of privileges that can be assigned on a report-by-report basis. Adding single users and groups is a painless task; security permissions are inheritable, allowing for easy application to large user groups, and security information can also be imported from Windows NT. The tool would benefit, however, from the ability to save these security profiles and apply them to other users or domains.

It is also possible to import users and groups from existing Windows NT security schemes. Query governance Query governance is not supported in BI/Analyze; if a drill operation will bring a very large return, however, users will be given a warning message. BI/Query can limit the number of rows that a user can request when constructing a query. Restricting queries to specified times There are no mechanisms that restrict queries to a specified time. Management of metadata Controlling visibility of the ‘roadmap’ The visibility of HyperCubes (dimensions and measures), data sources (tables, rows and values) and associated metadata can be controlled using the Access Control Manager tool. Additionally, the user profile determines what system data and resources end users have access to.

Adaptability Summary

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Adaptability in BI/Suite is generally a matter of being able to add new dimensions and measures to models, although there are no change management facilities provided to support this. Editing existing dimensions and measures, however, is a tedious process; if substantial changes are needed, it will be quicker in many cases to build them from scratch. Overall, the tool would benefit from greater integration between the query (BI/Query) and OLAP (BI/Analyze) environments. Change in business requirements Adding new dimensions to a model Adding new dimensions to a model is a simple point-and-click process. But there are no facilities provided to track the changes. Re-use of dimension definition Dimensions can be re-used. It can be a tedious process, however, to change an existing HyperCube’s dimension definition; it is easier to create a new HyperCube. Adding new measures to a model Adding new measures to a model is a simple process, but there are no facilities provided to track the changes. Re-use of calculated measure definition It is not possible to re-use measure definitions; however, calculated measures can be used in other calculations within the confines of a single model.

Changing the architecture to reflect business needs BI/Suite supports a desktop architecture for OLAP. There is no scope to support MOLAP or ROLAP configurations, although BI/Analyze can link into these environments. Changes to data sources Keeping the data source and model schema synchronised Log files are kept when information goes out of synch, but there is no support to warn users that the data source and model schema are out of synch. Automatic updating of members in a dimension When a HyperCube is refreshed with data, new members are automatically updated; however, members are not automatically removed from a HyperCube. Metadata Synchronising model and model metadata The structural model metadata remains synchronised with the HyperCube at all times. Descriptive metadata about a model needs to be updated manually, however, and on a per-model basis. Impact analysis Impact analysis is not supported. Metadata audit trail (technical and end users) A metadata audit trail facility is not supported. Access to upstream metadata BI/Analyze does not support metadata integration with third-party tools, although it can take the descriptive metadata names from BI/Query and implement them in the HyperCube. Hummingbird provides PowerView, a metadata query and reporting tool that has a prebuilt BI/Query data model – this provides a simplified view of Informatica’s PowerMart repository tables.

Performance tunability Summary

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BI/Broker has a scalable architecture that is enabled through the use of load balancing. Multiple support servers can easily be added for replicating application services; however, BI/Analyze is a desktop tool and performance depends on the size and complexity of HyperCubes loaded on to the client machine. ROLAP BI/Analyze does not support ROLAP operations.

MOLAP Typically, the size of HyperCubes makes performance and size trade-offs a non-issue. CubeCreator can update cubes by adding only new data, so that cubes do not have to be completely rebuilt during a fresh data load. The extent of consolidation at build time can be customised, but there is no support for recalculating only the affected values. Support for multiple users The addition of a mid-tier application server (BI/Broker) increases the scalability of BI/Suite. The server supports concurrent and named server ports to control multi-user access. Processing Use of native SQL to speed up data extraction BI/Query provides native SQL access to most of the leading relational databases; ODBC support is also provided. BI/Analyze does not access relational data directly. Distribution of processing Processing can be distributed across multiple BI/Broker application servers. Load balancing is supported by replicating application services across support BI/Broker servers as needed. SMP support The BI/Broker server does not support SMP parallelism.

Customisation Summary

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BI/Suite is designed to be used out-of-the-box. The end-user tools offer some degree of customisation for creating restricted EIS-like interfaces. Apart from OLE links to third-party applications and tools, there is no support for custom application development. Customisation Option of a restricted interface The BI/Query data model can be configured on a per-user basis to provide restrictive interfaces for subsequent query and OLAP analysis. Ease of producing EIS-style reports There is support for producing EIS-style reporting interfaces. This is done by creating EIS data models using BI/Query, which links buttons to predefined reports.

Applications Simple web applications There is no support for developing web-based analytical applications. Development environment BI/Suite does not support its own development environment. Use of third-party development tools Integration with third-party development tools is achieved via OLE support (as both a client and a server).

Deployment

Platforms Client BI/Query and BI/Analyze run on Windows 95/98/NT workstations. BI/Web runs on any Java 1.1-capable web browser. Server BI/Broker runs on Windows NT and Unix (Sun Solaris, HP-UX and IBM AIX).

Data access BI/Query provides native support to access data from all major relational databases, including Oracle, DB2, Microsoft SQL Server, Sybase, Informix, Ingres, NonStop SQL, RedBrick Warehouse, Teradata and Unidata. It can also use ODBC to access other relational sources. BI/Analyze can connect (natively or via OLE DB for OLAP) to third-party MDDBs and ROLAP servers, including Hyperion Solutions Essbase, IBM DB2 OLAP Server, Applix TM1, Microsoft SQL Server OLAP Services, SAP Business Information Warehouse (SAP BW), WhiteLight, SAS, Informix MetaCube and NCR TeraCubes. It can also access data held in flat files. Access to ERP applications is via third-party tools (Acta for SAP and Noetix Views for Oracle).

Standards BI/Analyze supports Microsoft’s OLE DB for OLAP as a consumer.

Published benchmarks BI/Suite does not have any published OLAP benchmarks.

Price structure Pricing for the Windows NT version of BI/Suite starts at $20,000 for BI/ Broker with core reporting, publishing, scheduling and BI/Web capabilities. BI/Web’s ad hoc query functionality costs an additional $10,000 and BI/Web OLAP functionality costs $20,000 for each central BI/Broker. Concurrent and named user pricing schemes are available for end users: 20 concurrent users cost $50,000 and named ports are priced at $295 for each user (regardless of use with fat or thin clients). Standalone versions of BI/Query and BI/Analyze cost $695 per user. The BI/Query administration tool costs $1,995. Additional BI/Broker support servers are $4,000 each. Unix pricing is approximately 50% higher for the server components.

Summary

At a glance ...... 2 Terminology of the vendor ...... 3 Ovum’s verdict ...... 4 Product overview ...... 6 Future enhancements ...... 15

Commercial background

Company background ...... 17 Distribution...... 19

Product evaluation

End-user functionality ...... 20 Building the business model ...... 22 Advanced analytical power ...... 24 Web support ...... 25 Management ...... 26 Adaptability ...... 29 Performance tunability ...... 30 Customisation ...... 31

Deployment

Platforms ...... 33 Data access ...... 33 Standards ...... 33 Published benchmarks ...... 33 Price structure ...... 33 Evaluation: Hyperion Solutions – Hyperion Essbase Ovum Evaluates: OLAP

At a glance

Developer Hyperion Solutions, Sunnyvale, California, USA

Versions evaluated Hyperion Essbase Server version 6.0; Wired for OLAP version 4.1; Hyperion Integration Server version 1.1; Hyperion Essbase Web Gateway version 2.1; Hyperion Essbase Objects version 1.1

Key facts • A multidimensional database server that can be accessed from spreadsheets, a web browser or a variety of third-party front-end tools • Server runs on Windows NT, OS/2, Unix and AS/400; clients run on Windows 95, Windows NT and Macintosh or can use a web browser • The Essbase MDDB is embedded in more than 60 vertically-oriented analytical applications developed by Hyperion and its application partners

Strengths • Friendly graphical environment for designing and maintaining models • Provides strong multi-user write-back to the multidimensional database • Can be accessed by a range of front-end tools, including standard spreadsheets

Points to watch • Analysis, presentation and distribution functionality depends entirely on the front-end tool used • Presumes a clean and consistent data source – Essbase does not provide any of its own ETL capabilities • Questions still remain about the growth and stability of the newly formed company

Ratings

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End user functionality

Building the business model

Advanced analytical power

Web support

Management

Adaptability

Performance tunability

Customisation

Terminology of the vendor

Application Any analytical application that runs on Essbase. It consists of a multidimensional database, rule files for loading data and scripts to calculate data. Attributes Detailed descriptive qualities of dimension members; for example, customer demographics and product details. Attributes in Essbase look and act like normal dimensions. Calc script A procedural script that calculates the multidimensional database or subsets of the database. Data block A multidimensional array of cells. It is the primary storage unit within the database and is defined during the initial system build. Data load rules These are used to import data into the database, and also to define the hierarchies and relationships within the dimensions. They are used during the initial build process and for ongoing systems maintenance. Database A physical multidimensional data structure that is stored persistently in the Essbase Server. Database outline Defines the structure of an Essbase multidimensional data model, including the definition of all hierarchies and other relationships, plus many calculations. It corresponds to Ovum’s definition of a business model. Dimensional attributes Essbase provides attribute information in the form of ‘dimensional attributes’ that are attached to dimensions in the database outline. Dimensional attributes behave like dimensions; they have structure, can be cross-tabulated and calculated dynamically in models. Partitioning Divides a database into separate parts that can be loaded and calculated in parallel on multiple servers. UDAs User defined attributes (UDAs) are textual tags attached to dimensions that are used for filtering data.

Ovum’s verdict

What we think Essbase’s main strength is in providing a consistently fast and easy-to-use multidimensional database. Its powerful server-based calculation engine provides a good fit for the logical structure of dimensionally complex business models. Essbase scores consistently across most of our evaluation perspectives. Although many of its features are not unique, users will be hard pressed to find another OLAP product with a similar range of functionality. The latest release (version 6.0) builds on Essbase’s hallmark capabilities – namely performance and modelling simplicity. It excels in its graphical design tools, which provide a rapid and consistent approach to business modelling without requiring advanced IS skills. The GUI-based definition of data load rules simplifies the task of designing highly complex multidimensional models. Hyperion has worked hard to remove the stigma attached to multidimensional databases – notably scalability and database explosion. Version 6.0 extends Essbase’s capabilities to larger dimensional structures and provides greater agility in supporting attribute-rich data. While financial planning will remain a ‘bread and butter’ application, these new features move the product out of its comfortable financial niche to new areas such as customer-centric analysis. But as Essbase continues to be pushed into larger and more scalable applications, Hyperion will need to look and act more like a RDBMS, including the provision of better management facilities for fault tolerance, rollback and up-time. Essbase provides a number of prebuilt functions for ad hoc analysis; its multi-user write-back access is well equipped to support advanced budgeting and forecasting applications. However, users that require advanced analytic functionality will have to build it into the server-based model, or integrate with specialist third-party tools. Essbase supports a choice of third-party front-end tools, including familiar spreadsheets. However, customers should choose their front-end tool carefully, because the level of functionality will vary considerably from tool to tool. While Essbase has the capability to be used standalone, it is primarily designed to be used as part of a best-of-breed data warehousing solution. It therefore presumes a clean data source. Essbase provides rudimentary data transformation against relational databases, but it relies entirely on third- party ETL tools for fully-fledged data extraction and scheduling. Perhaps the greatest concern is the stability of the company following its merger with Arbor Software. Its problems with disappointing earnings and stock price has culminated in a number of executive shake-ups. These issues still need to be fully resolved to convince customers (and shareholders) of long-term growth. One of the primary missions will be to befriend application partners that have been alienated by Hyperion’s strategy of being both a platform for partners’ analytic applications and an application provider itself.

When to use Essbase is suitable if you: • need rapid response times for complex multidimensional queries • have a need for rapid deployment of OLAP to power users across the enterprise • are building complex analytical applications that require concurrent multi-user write access to the database • have existing spreadsheet skills to exploit. It is less suitable if you: • do not have a structured data warehouse or other cleansed data sources • want an end-to-end business intelligence solution • have highly specialised analytical requirements that require custom development • require a flexible approach to OLAP – Essbase is a MOLAP-only solution.

Product overview

Components The main components of a Hyperion Essbase OLAP solution are: • Essbase Server version 6.0 • Essbase Application Manager version 6.0 • Essbase Spreadsheet Client version 6.0 • Hyperion Wired for OLAP version 4.1 • Hyperion Integration Server version 1.1 • Essbase Web Gateway version 2.1 • Essbase Objects version 1.1. Figure 1 shows how these components relate to client-server systems. Hyperion develops a number of packaged analytical applications aimed at the finance sector. However, the focus of this evaluation is on Essbase – a multidimensional server designed specifically for OLAP. It consists of a multidimensional database server and OLAP clients for analysis and reporting. The server takes data out of back-end data sources (usually a relational database and other source systems) and organises it in such a way that it can be easily and quickly queried from a multidimensional perspective. Essbase assumes that it will work with cleansed data (typically done when the data is first loaded into the data warehouse).

Essbase Server This is the central component of Essbase, providing a fast, server-based OLAP engine and a multidimensional database. It also stores all Essbase application components, including rules for loading data and scripts to calculate data.

Figure 1 Essbase component framework

Extract data Build model OLAP analysis Web access Application from RDBMS development

Client Essbase Essbase Spreadsheet Essbase Objects Application Client Essbase Manager Wired for OLAP Web Gateway

Server Essbase Essbase Server Integration Server

The Essbase Server is responsible for all data loading, OLAP query processing, calculations and security. Calculations can be precalculated or done at query time. Essbase databases use patented technology (which Hyperion calls ‘dynamic dimensionality’) to provide optimisation for the handling of sparse and dense dimensions in a database for efficient storage and performance. A major feature of Essbase is support for concurrent access and update by multiple users. Essbase provides transparent locking of data to allow multi- user write access. New attribute handling features also extend its scalability to cope with models that include large dimensions and deep hierarchies.

Essbase Application Manager A graphical DBA-type utility that resides in the Essbase Server. It is used for designing and maintaining models, defining data load rules and administering security.

Essbase Spreadsheet Client An end-user tool that provides access to multidimensional models directly from Excel and Lotus 1-2-3 spreadsheets. The Essbase functions for data retrieval and OLAP display and analysis are accessible from the spreadsheet interface.

Hyperion Wired for OLAP Wired for OLAP is an out-of-the-box tool for constructing queries against multidimensional models. The tool, acquired from AppSource in 1997, was designed specifically for Essbase, but can also integrate with OLEDB for OLAP data providers. It provides a range of OLAP analysis and reporting functions from a graphical interface. It also includes designer tools for building EIS-like briefing books applications. Access is available through either a desktop PC or a Java-enabled web browser.

Hyperion Integration Server Hyperion Integration Server is an optional server component that integrates Essbase more closely with a data warehousing strategy. It provides a suite of graphical tools for creating and deploying Essbase models directly from relational databases. Using a metadata layer, Integration Server provides end users with predefined (and re-usable) dimensions, hierarchies and measures that are directly mapped to relational data sources. The architecture is not quite ROLAP, because the relational data returned is still staged in a MDDB format prior to analysis. Integration Server consists of two main tools: one for the developer and one for the end user: • OLAP Architect is a graphical DBA tool used to map relational tables onto logical dimensional structures. It organises these mappings into an OLAP metadata catalogue. The tool works with a variety of database schemata – from normalised and denormalised OLTP to star and snowflake – and includes rudimentary data transformation and cleansing facilities

• OLAP Builder is a graphical end-user tool that uses the catalogue to assemble the re-usable structures to create ‘meta outlines’ to define what data is to be retrieved and built-in to a model. A third component automatically generates the SQL code, builds the physical structure for the data and loads it into the Essbase Server in a multidimensional model.

Essbase Web Gateway Essbase Web Gateway is a toolkit that provides an HTML interface that users can employ to construct OLAP queries against the Essbase Server from a web browser. The Gateway populates HTML templates held on a web server with Essbase data as users request it. Output is in HTML format, and as the web user interacts with the data and requests new data, additional HTML commands are generated to the web server to reconnect to the Essbase Server and pull out the data.

Essbase Objects Essbase Objects is a development environment for building OLAP applications using ActiveX controls. Essbase Objects consists of a family of controls for data access, visual data display, data navigation, query and report layout. A number of third-party ‘Essbase-aware’ controls are also available.

Essbase application modules (optional) These are add-in tools that extend Essbase’s functionality to include SQL drill through to transactional data, partitioning, SQL access to relational data and currency conversion.

Hyperion analytical applications and tools (not evaluated) Hyperion develops several packaged analytical applications and end user tools that integrate with Essbase: • Hyperion Enterprise – a packaged analytical application for financial consolidation, reporting and analysis

• Hyperion Pillar – a packaged analytical application for budgeting, financial planning and forecasting. Pillar can integrate with Hyperion Enterprise – the AutoPilot module provides common process automation for both products • Hyperion Spider-man – a web application that extends financial reporting to the Internet • Hyperion Reporting – a graphical client-server tool for creating production-quality management and statutory reports from Hyperion’s packaged analytical applications • Hyperion Allocation Manager – for the creation of complex business allocation models that can be used across a variety of analytic applications, including those for budgeting and planning, financial consolidation, and customer and product profitability. It provides a graphical allocation environment and library allocation method templates

• Hyperion Application Link – a suite of back-end application integration services for accessing ERP, transaction-processing and packaged analytical applications. Application Link is based on Oberon’s Prospero EAI technology, which provides a visual design environment with plug- and-play components. A Translation Manager module allows users to define conversion rules that govern the process of mapping source data elements. Architectural options

Full mid-tier architecture Essbase is typically configured in a full mid-tier OLAP architecture, consisting of the Essbase Server and clients – typically the spreadsheet client, Wired for OLAP or other supported front-end tools. In this architecture, data is loaded into the Server and stored in a MDDB as a persistent multidimensional business model. The client tools access the model directly and use the Essbase Server’s OLAP engine to perform all processing and analysis functions on the model. There are two variations of the full mid-tier architecture: • distributed mid-tier, consisting of partitioned multicubes residing on multiple Essbase Servers • web-enabled – a ‘thin-client’ implementation using the Essbase Web Gateway for HTML-based analysis, or Wired for OLAP for a Java-based approach.

Distributed mid-tier With the distributed architecture, Essbase models are spread across multiple Essbase Servers to provide a partitioned multicube architecture. The partitions divide a model into separate logical and physical parts, but end users analyse it as a single multidimensional model. The benefits of partitioning are increased scalability and performance; data loading and OLAP query processing can be split across multiple servers. A partition wizard is provided to create three types of partition: • a linked partition that connects models of varying dimensionality by defining multiple drill paths between model dimensions. This lets users drill outside a model to data in another model • a transparent partition, which integrates multiple model’s cubes into a single logical model; each partition can either be the entire model or a portion of it • a replicated partition, where models can be replicated and refreshed incrementally. Essbase automatically synchronises data across all partitions. However, developers will need to carefully think through and set up additional security mechanisms for a partitioned model.

Web enablement Web access is managed through the introduction of a web server that interfaces with the Essbase Server. Users are able to access the Essbase databases via standard web browsers. There are two options: • the Essbase Web Gateway uses CGI to link the Essbase Server with a web server. It utilises HTML to provide interactive analysis and reporting capabilities • Wired for OLAP also provides a web interface for constructing ad hoc queries against the Essbase Server. It is implemented using Java applets. Wired for OLAP ‘applications’ can be developed via a standard desktop and deployed for web access. The applications are stored on a mid-tier server and accessed via the web browser.

Light mid-tier architecture Essbase also supports a light mid-tier architecture. Through a relationship with IBM, the Essbase Server’s multidimensional datastore is replaced by a relational storage interface for IBM’s DB2 database. This configuration still uses the OLAP engine for processing, but the model is held in a relational database in a star schema. The Essbase Server automatically creates and maintains the schema, and populates it with calculated data. The product, called IBM DB2 OLAP Server, is marketed and sold as a separate product by IBM.

Desktop architecture Essbase is a server-based tool. There is no support for a two-tier desktop mode.

Mobile architecture A Personal Essbase version supports a mobile architecture. It allows users to store a model, or part of a model, on a client PC for offline analysis. Synchronisation of data and structures between the Essbase Server and remote clients is supported upon reconnection. Using Essbase Essbase supports a set of highly graphical tools for designing and using multidimensional models. The modelling tools are aimed at power users, and there is no clear division of responsibilities between model designer and end user. However, model designers are expected to have some level of DBA-type skills and a good understanding of the business to use the tools effectively. The end user can also be the power user/business analyst, or simply an information consumer. The latter group requires no knowledge of the database architecture, just an understanding of the business model. Typically, they access the results of the work done by the model designers.

Figure 2 Outline Editor

Creating a model The Application Manager is the main interface for designing models. Within Application Manager, the four principal model building tasks of model definition, calculation, data loading and reporting are clearly defined as separate functions with appropriate graphical user interfaces for each. Define the database outline The database outline determines the structure of the business model. This outline is created using the Outline Editor, which provides a graphical representation of the dimension hierarchy; each dimension and consolidated level in the model is represented in Figure 2. As dimensions are added to the structure, it is assumed (by default) that they will be aggregated according to the hierarchy, but this can be easily changed via a point-and-click interface. While some dimensional hierarchies are typically created manually, larger ones are invariably loaded directly from

existing systems by importing data and then mapping this to the required model. If the underlying data sources subsequently change, this process can be re-run to ensure that the models are always synchronised with the data source. Add calculated measures Calculations are usually defined once, and built directly into the server-based model. Calculations can easily be applied to any level within a dimension in the database outline by typing them into the outline or via point-and-click using a calculator-style interface called the Formula Editor. Essbase provides a set of mathematical functions and cross-dimensional operators for constructing calculation formulas. Multiple formulas and actions can also be placed in calc scripts for advanced calculations that require a more procedural approach or where multiple iterations through the data are required. A Calc Script Editor provides a text editing panel, customised menus, a syntax checker and function, and macro templates for a point-and-click development environment. Load in the data Specifying data load rules is the easiest and quickest way to load data into the model. The Data Prep Editor provides a graphical way of defining these rules. Data load rules are sets of operations that Essbase performs on data from an external data source file as it is loaded or copied into the Essbase model. They support simple transformations for the mapping of raw data into the multidimensional model.

Using Hyperion Integration Server Hyperion Integration Server provides a graphical suite of tools for creating and deploying Essbase models directly from back-end relational data sources. Significantly, it pushes Essbase closer to a data warehouse strategy and increases the ability to quickly build database outlines in an ad hoc manner. A central metadata ‘catalogue’ of common dimensions, hierarchies, structures and business rules is created and managed by DBA-type users using the graphical OLAP Architect tool. End users are then able to reference this catalogue directly and select the required components using drag-and-drop to build and populate a model ‘ondemand’ using the OLAP Builder.

Support for attribute analysis Scalability in terms of handling large dimensions has always been the ‘Achilles heel’ of multidimensional databases. Version 6.0 of Essbase directly addresses this limitation by providing support for analysing dimension attributes. Previous versions of Essbase had relied on the use of user defined attributes (UDAs) – effectively textual tags that are used to filter dimensions – which were unwieldy and limited in usage. Attributes are now attached to dimensions in the database outline and appear and behave like other dimensions; that is they can have a structure (for example, a stacked ‘age’ attribute), can be cross-tabulated and can be calculated dynamically in models. Support for attribute handling is significant because it opens up new classes of analytical applications that were previously closed to multidimensional tools – largely due to scalability issues. These include the analysis of a large product and customer mix that are commonplace in customer relationship management (CRM) and sales & marketing applications.

Exploring models Users can access the model via the Essbase spreadsheet client or Wired for OLAP client; a variety of other third-party front-end tools can also be used. Navigation around the model is governed by the hierarchies and structures built-in to the multidimensional model, though the actual method of navigation varies according to the chosen front-end tool. At its simplest, the spreadsheet user logs-on and connects to the appropriate database and double-clicks on any cell to begin a query. The required level within a dimension can be found by either drilling-down, by typing in the name of the dimension level, or by using a select option that opens up the database outline for searching. Data is presented in a standard spreadsheet from which drill-down and slice-and-dice functions are directly available. This is shown in Figure 3. The Spreadsheet Client uses the native Excel or Lotus 1-2-3 environment for further analysis of data. A query wizard is also provided to help with the entire process. Alternatively, the Query Designer tool provides a graphical drag-and-drop method for selecting dimension members and filtering data.

Figure 3 Spreadsheet Client

Wired for OLAP offers a similar range of query tools, but in a more graphically-oriented fashion. Reports can be specified and additional calculations and sorting methods defined from within the interface. The designer tools provided by Wired for OLAP also support the presentation of data in briefing book-type applications and other EIS-style interfaces. The same capabilities are available on the desktop and via the Web. The Web Gateway can be used to access the Essbase database from a web browser as HTML pages. It provides full OLAP functionality such as drill- down and slice-and-dice, as shown in Figure 4. Write access is also supported via the web client.

Figure 4 Web Gateway

Future enhancements

Version 6.5 The next release of Essbase Server (version 6.5) is planned for the end of 2000. Version 6.5 will focus on enhanced parallel processing capabilities for the server, specifically for data loading, OLAP queries and OLAP calculations. Version 7.0 Version 7.0 (due at the end of 2001) will support a hybrid OLAP architecture, allowing data to be stored and accessed from either multidimensional or relational sources. This will allow metadata (essentially the Essbase Outline) and data to be wholly, or partially, stored in relational database tables. Other RDBMS-like facilities will also be provided, including security, rollback and recovery, and fault tolerance. Essbase administrators will be able to mix and match data storage options between the MDDB and relational tables for optimal performance. Version 7.0 will also deliver a Java version of Essbase Application Manager. Platforms A Linux version of Essbase is planned for the second quarter of 2000; Hyperion is a Red Hat Software development partner. Hyperion is also working closely with IBM to develop an OS/390 MVS mainframe port for Essbase, which is scheduled for general availability in the first quarter of 2000. Consolidation Integration between Hyperion’s tools is also planned on a number of fronts. Initial efforts will focus on rationalising the various client-server and web front-end tools to provide a more holistic product offering. For example: • a new client, called Hyperion Analytic Reporter will replace Hyperion Reporting and Spider-Man • a new spreadsheet add-in will combine the existing Essbase and Enterprise ones (Analyst & Retrieve) • version 5.0 of Wired for OLAP will also replace the Essbase Web Gateway

• the capabilities of Integration Server will be merged into the Essbase Application Manager component. Web-enablement Hyperion Information Portal is currently under development, and will provide a single web-based interface to access personalised reports and underlying business intelligence systems. Hyperion also plans to deliver a Java version of Essbase Application Manager. Analytic applications Hyperion will focus on the provision of ‘Essbase-powered’ analytical applications for its suite of Enterprise Performance Measurement (EPM) analytical applications. These will include: • Hyperion Consolidation – for financial consolidation and management reporting • Hyperion Planning – for financial budgeting and planning

• Hyperion Profitability – for profitability and allocations management • CRM and e-business – specifically targeting clickstream analysis, e- commerce sales and web marketing planning. The new applications are expected to be released throughout 2000.

Commercial background

Company background

History and commercial Hyperion Solutions Corporation was formed in August 1998 when Arbor Software and Hyperion Software announced the completion of their merger. Arbor and Hyperion were both public companies, but the similarity ended there; Arbor developed a client-server multidimensional database, whereas Hyperion focused on analytical applications. Arbor, founded in 1991, was the first vendor to challenge the relational model for databases when it introduced Essbase in 1993. Its early start is reflected in Essbase’s general maturity and wide recognition in the OLAP market. Hyperion, founded as IMRS International, was a more established and larger company. After an earlier foray into the world of financial software, it refocused its activities on providing analytical applications for financial reporting and consolidation, and enterprise budgeting. In May 1999, Hyperion acquired Sapling Corporation and inherited two new analytical applications: Hyperion Performance Measurement (previously Sapling’s NetScore) and Hyperion Activity Based Management (Sapling’s NetProfit). It also acquired KPI Technologies in November 1999 to provide a new CRM application. Revenues for fiscal 1999 grew a modest 13% to $425 million. Net income for the same year was $8 million. Approximately 50% of revenue is generated from Essbase licences. Hyperion has its headquarters in Sunnyvale, California (the old Arbor Software headquarters), and employs more than 2,300 people in 26 countries.

Character and direction The merger between Arbor Software and Hyperion Software seemed logical. Both were both strong, aggressive companies with large customer bases and powerful marketing. Both had established considerable mindshare in the OLAP and analytical applications markets respectively. However, fitting together two equals has proved difficult – the departure of key executives and concerns from Arbor shareholders are testament to the initial teething problems that have been encountered. Hyperion seems to have ridden this storm, and is now concentrating its efforts on product development and consolidation, and increasing market share. It has more than 6,000 customers worldwide. Essbase accounts for nearly half of these customers, with around 850 sites in the EMEA region. The merger has also resulted in a more balanced sales model; Hyperion plans to sell its financial analysis products via Arbor’s historic VAR channel, while Arbor gains direct access to Hyperion’s blue-chip customer base. On a product front, the merger has presented a number of opportunities for product synchronisation. The Essbase Server Engine is now firmly established as the back-end OLAP server for all the company’s current and future financial analytical applications – Hyperion Software previously had an OEM agreement with Applix’s TM1 for OLAP support and a full migration programme is in place. Partnerships are key to Hyperion’ strategy. Its partner model includes: • analytic application partners that resell or integrate Essbase and other Hyperion products within their application suites. Examples include: Comshare, PeopleSoft, Prism, Accrue, Lawson Software, i2 and Paragren

• OEM partners, such as IBM and ShowCase, that offer operating system and relational database solutions for Essbase • data integration (ETL) partners that either publish data to Integration Server or produce multidimensional cubes directly. Hyperion has alliances with leading ETL vendors, such as Acta, Informatica, Sagent, Ardent/ Informix and Constellar • consulting partners, for complementary systems integration, implementation and business consulting services. A key partner for Essbase is IBM, which develops a specialised version of Essbase, called IBM DB2 OLAP Server, aimed at DB2 customers. The product is bundled into IBM’s Warehouse Center data warehousing offering. The combination of application partners and the new capabilities in Essbase 6.0 open up new markets for Hyperion. Financial applications remain a core focus, but the company claims that approximately 35% of new applications are being developed for sales and marketing applications. Hyperion has already developed (in conjunction with its application partners) 12 CRM- based applications – focused on analysing customer and product mix. It also acquired KPI Technologies and has established partnerships with many of the leading CRM vendors in order to bolster its presence in this market. Hyperion has also set up an e-Business division to develop three analytical applications for e-business data: • e-Marketing Analysis – to gauge the effectiveness of web marketing campaigns • Web site Analysis – to analyse clickstream data for visitor behaviour • e-Commerce Analysis – to access purchasing and other transactions.

Hyperion has also entered into technology and marketing partnerships with companies that specialise in website monitoring, analysis and advertising including DataSage, DoubleClick, NetGravity and net.Genesis. Customer support

Support 247 hot-line telephone support as well as on-site support arrangements are available worldwide. The support operations of Hyperion and Arbor will be merged. Support is included along with software updates for an annual maintenance fee of 18%.

Training Three-day introductory training courses on Essbase are available for power users. A two-day course is also provided for systems administrators. Casual end users do not require much training beyond a familiarisation with the business model.

Consultancy services Hyperion has a large consultancy organisation, consisting of 350 consultants worldwide. However, it offers a limited range of consultancy services for Essbase, and no significant revenues are generated from this.

Hyperion also has formal relationships with many of the global management consultancies and systems integrators (such as EDS, Shell Services and Perot). Distribution North America Hyperion Solutions Corporation 1344 Crossman Avenue Sunnyvale CA 94089 USA Tel: +1 408 744 9500 Fax: +1 408 744 0400

Europe Hyperion Solutions UK Arbor House Old Bracknell Lane West Bracknell RG12 7DD UK Tel: +44 1344 664000 Fax: +44 1344 664001

http://www.hyperion.com E-mail: [email protected]

Product evaluation

End-user functionality

Summary

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The range of end-user analysis, presentation and reporting functions available depends largely on the front-end tool used – there are more than 50 tools to choose from. The Essbase spreadsheet interface can be used out-of-the-box, and is ideal for financial analysts. However, it does not offer the same flexibility or sophisticated reporting capabilities as other OLAP front-end tools. Wired for OLAP provides a more graphical presentation interface for OLAP analysis and reporting. There is no direct support for sharing and distributing reports; this is provided by integrating with third-party tools, such as Seagate Info.

Finding and understanding the model Finding and loading a multidimensional model Essbase presents the models that users can access in a standard directory structure. There are no search facilities for finding models based on a dimension name or other keywords. Metadata for end users Structural information about a model is provided in the database outline. Descriptive metadata about a model (such as ownership and authorship) can be input using the database outline comment window. These comments can be selectively viewed by end users. Annotation by the end user Descriptive information can be assigned to each dimension level within a model, either as a comment or via use of an alias or an alternative reporting label. Detailed information can be assigned to cells in the model by the linking in of textual, graphical, video or audio data using the Linked Reporting Objects facility.

Using the model Basic OLAP functionality Using the spreadsheet client, an additional pull-down menu appears in the interface through which Essbase data retrieval and control functions (including write-back) are accessed. Drill-down, pivoting and moving dimensions can be achieved easily using point-and-click operations. Wired for OLAP offers a more graphical interface for analysis. A range of analysis and presentation options are available through point-and-click, including enhanced navigational aids such as traffic-lighting. Changing the position of members in a dimension level Values within a dimension can be moved via drag-and-drop.

Visualising the drill-down hierarchies Essbase’s Outline Editor provides a graphical view of the model as a hierarchical tree structure. The tree view is also available through the Spreadsheet Client’s ‘member select’ option and via the Wired for OLAP ‘navigator’ facility. Drilling-down to detailed data A SQL drill-through option allows users to drill-down to the underlying detail data directly from the client interface. Integration Server enhances this capability by providing a direct mapping between the relational and multidimensional data. Range of front-end user tools Essbase can integrate with more than 50 Essbase Ready client-server and web tools via the Essbase API, including OLAP query and reporting clients, statistical analysis & data mining tools and custom-built analytical front- ends. However, not all the Essbase Server functionality is available through all the tool interfaces. Examples of such partner tools that connect to Essbase include Business Objects, Brio Enterprise, Cognos PowerPlay, CorVu, Hummingbird BI/Suite, Seagate Analysis/Info, SPSS, AlphaBlox, Painted Word and TrackObjects. A definitive list is provided at http:// www.hyperion.com/alliances.cfm Visualising the results The visualisation of results depends on the front-end tool employed. The Spreadsheet Client relies on the business charting and graphing conventions provided by Excel and Lotus 1-2-3. Wired for OLAP users can have a mixed reporting environment with a complete suite of tabular, textual and graphical components within the reporting application. Essbase provides links to a GIS tool from Environmental Research Institute (ERI) for mapping capabilities.

Saving and sharing results Designing a report Data is presented to the end user through the spreadsheet or other front-end tools. Thereafter, the full range of product capabilities is available to the end user. The ‘Linked Reporting’ option can be used to add information to spreadsheets, including OLE, text, graphics, video and image objects. Data from multiple models can be displayed on spreadsheet reports. Publishing a report The Spreadsheet Client does not provide support to schedule publication of reports to individuals or groups of users. Third-party tools need to be used to support this functionality. Targeted distribution via e-mail There are no additional facilities provided for end users to e-mail multidimensional tables from within the Spreadsheet Client. Wired for OLAP does allow users to e-mail static reports. Distribution lists can be maintained, but cannot be dynamically defined. Subscribing to reports Essbase does not support any report subscription services.

Building the business model

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Essbase provides a very intuitive interface for business modelling – thereby obviating the need for specialist DBA skills. The graphical representation of the dimensional hierarchy in the database outline view works very well, and tools are provided to make the definition of measures and data load rules easy. The structure of calculations and modelling simplicity of Essbase is especially suited to financial modelling (which usually includes profit and loss dimension) and it provides a good overview of calculations and aggregations. However, building complex functionality into models will often require a rules-based scripting approach that can be quite intensive. Integration Server provides a more drag-and-drop approach for general ad hoc business modelling. Essbase models can be shared to support multi-designer environments, though there is no direct support for versioning control.

Basic design Design interface Business models are defined and maintained in database outlines using the graphical and highly intuitive Outline Editor. Users (with appropriate security rights) can easily drag-and-drop dimensions, and specify relationships between dimensions from this interface. Visualising the data source The Data Prep Editor and the Integration Server allow model designers to view the file/table layout of source data held in relational databases and text files. A sample of the data can also be viewed through a simple graphical interface. Universally available mapping layer Integration Server’s metadata ‘catalogue’ provides a mapping layer to access data stored in a relational database. The mapping layer is universally available to end users. Prompts for metadata Designers are prompted to include names for dimensions when creating the database outline. They are also given the option to create additional model metadata about the status of a model, but are not explicitly prompted to do so.

Building the dimensions Selecting columns for the dimensions Columns in a source table can be selected for building a dimension via point- and-click. Dimension hierarchies can also be built from source SQL files.

Selecting the members shown in a dimension level Essbase usually builds dimensional structures by reading data from ASCII files or by direct access to relational tables – in the latter instance, the product generates the SQL necessary to extract the chosen field and requires no knowledge of SQL by the user. Essbase also supports a point-and-click additive and replacement policy for selecting members. Defining a dimension hierarchy Essbase prompts users to define dimension hierarchies graphically using point-and-click. Unbalanced or asymmetrical hierarchies are supported. Time dimension The time dimension is built in the same way as any other dimension, with aggregations built up from the base level (day, week, month and so on). Essbase supports eight automatic time series aggregations, such as year to date, week to date or season to date, which are calculated dynamically. Annotating the dimensions Explanatory notes can be added for any dimension level. For reporting purposes, a number of aliases can also be added to each dimension and selected according to reporting needs. Aliases can also be used to add information for each dimension level (for example, as short and long names). Default level of a dimension hierarchy When a model is built, it can have a default set of dimension levels pre- selected to minimise the amount of user navigation.

Defining the measures Calculated measures Essbase models are designed to understand common business rules and calculation logic – such as income versus expense accounts for variance analysis. Calculations can easily be applied to any dimension in the database outline by typing them into the outline or by using the Formula Editor. A point-and-click approach is available for selection from a range of mathematical, conditional, Boolean and cross-dimensional operators. For more complex calculations, server-based calc scripts can be used to define complex formulas using an integrated script editor. Support for multiple measures with a set of dimensions Multiple measures can be stored with a set of dimensions. The measures can also be arranged in a hierarchy. Non-aggregated hierarchies can also be specified.

Multiple designers Multiple designers Database outlines can be shared; the outlines are locked when being edited. However, there are no check-out/check-in facilities. Support for versioning There is no support for versioning control.

Advanced analytical power

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Generally, analytical functionality is built-in to Essbase’s server model during design. The main advantage of this approach is to ensure a consistent analytical environment. But it requires users to build their own analytical functions from scratch, using Essbase’s rules-based scripting language. However, version 6.0 of Essbase provides new out-of-the-box functions that can be used for standard financial analysis, forecasting and trending. Essbase also supports multi-user write back for ‘what-if’ analysis and budgeting applications. Its analytical capability can be expanded by integrating with specialist analysis tools.

Third-party tool integration Essbase integrates with spreadsheets and works with a range of third-party statistical analysis (SPSS), datamining (IBM and Data Mind) and data visualisation tools.

Defining specialised models Ranking and sorting Simple ranking and sorting of data can be achieved by both the Spreadsheet Client and Wired for OLAP. Essbase provides a special rank function. Mathematical methods Essbase supports standard mathematical functions. These functions can be enhanced using calc scripts. Financial functions Essbase provides a standard set of financial (consolidation) functions, including standard options for allocations, asset depreciation, compound interest calculations and discount. A Currency Conversion module is also provided to enable users to translate, analyse and report on foreign currency data (including the euro). Essbase can also integrate Hyperion Allocation Manager – for the assignment of revenues and indirect costs (an allocation model). Pre- packaged, re-usable allocation method templates serve as calculation building blocks that can be combined to define revenue allocation processes. Allocation process information is stored in an embeddable Microsoft Access repository or Oracle8 RDBMS repository for shared, enterprise-wide access. Statistical models Essbase supports a range of statistical functions, such as median, mode, correlations and standard deviation. Essbase also links to SPSS to support advanced statistical models. Trend analysis Functions are available for the definition of time-related calculations and trending. Single, double and triple exponential smoothing functions are also provided.

Simple regression Essbase relies on the Spreadsheet Client to provide regression analysis. Time series forecasting There is no support for time series forecasting methods.

User-definable extensions Calc scripts can be defined to provide server-based analytical functions. Calc scripts enable users to define complex formulas using procedural logic. More than 200 server-based functions are supported by the scripting language.

Write back for ‘what if?’ analysis Essbase supports concurrent multi-user write-back to the database for ‘what if?’ analysis.

Incorporating non-numerical data Essbase supports the ability to associate additional text and date information, or ‘attributes’ with the core numeric data elements in models. Essbase supports the ability to associate additional text, numeric, Boolean and date information or ‘attributes’ with the core dimensional members. Such examples define attributes (such as colour, flavour and size) or characteristics to a dimension. Essbase provides attribute information in the form of ‘dimensional attributes’ and user-defined attributes (UDAs). Dimensional attributes behave like dimensions, have structure and can be cross-tabulated and filtered. UDAs are a much simpler form of dimensional attributes in that they are only used for filtering and can only be text.

Datamining Essbase does not provide any support for datamining. Web support

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Essbase’s server-based architecture easily lends itself to web deployment and distributed OLAP applications. The Essbase Web Gateway provides the standard interactive analysis and HTML web publishing capabilities expected from an HTML-based implementation. Wired for OLAP uses Java applets for a more interactive experience, including reporting and charting options. Both clients offer write-back capabilities from the web browser. However, Essbase’s web capabilities have not been designed for data modelling and there is little support for utilising the Internet for dynamic distribution of reports.

End-user functionality via the Web Functionality of web access to explore models Essbase models can be interactively explored with full drill-down, pivot and slice-and-dice access to models. Web users can also write-back to the database and drill-down detail data stored in relational databases. The only restriction is that users cannot add new dimensions or calculations to models. Supports both registered and unregistered web access All web users must be pre-registered and require a valid ID and log-on. Range of users supported by the web interface Web users can be divided into those who need direct access to the database for interactive navigation and analysis, and those who will use the Web simply as a means of accessing ‘canned’ reports. For example, Wired for OLAP’s Analyzer Web Viewer Edition is designed specifically to support the simple reporting needs of casual information consumers.

Creating models via the Web Editing the mapping layer There are no facilities for development over the Web. Building and editing models It is not possible to create new models or edit existing models via the Web.

Distributing via the Internet and the Web Generate HTML and Java There is no point-and-click support for saving model results in HTML format using the Spreadsheet Client. The Web Gateway can be used to define HTML pages. Corporately organised distribution via the Internet The Web Gateway can be used to distribute static reports via the Internet as e-mails. However, there is no support for dynamically creating address lists. Include URLs in a report Wired for OLAP lets end users embed multiple URLs in reports.

Distribution of web server processing There is no integration with third-party middleware for distributed processing. Management

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Essbase provides facilities for deploying multidimensional databases to end users. It provides strong security facilities for both models and users, and the tool’s sparse data handling capabilities and intelligent calculation options facilitate efficient data storage and retrieval. But data loading schedules are

only supported through a scripting language; there is no point-and-click support. Event-driven scheduling is not supported. Essbase does not provide facilities for query monitoring or governance – though this is less of an issue for optimised multidimensional databases.

Management of models Separate management interface Application Manager serves as Essbase’s main management interface. All key administrative functions, including model building, data loading and security access, are managed through pull-down menus and toolbars. Security of models The integrity of models is controlled through a combination of: • user security profiles; individuals or groups of users are granted or denied the ability to view, change or create a model • a multi-layered approach for intra-model security; a filter layer defines read/write access levels for dimension levels (down to cell level).

Query monitoring Essbase does not provide any query monitoring facilities.

Management of data How persistent data is stored (not scored) The Essbase Server stores multidimensional data persistently. The data is refreshed periodically from back-end data sources – there is no caching of data on the server or the client. If application partitioning is used, a database may either be stored across multiple servers or be divided into a number of sub-models (or partitions). Scheduling of loads/updates The scheduling of data loads and updates is handled either through a batch control facility supported by the Essbase scripting language or by using third-party tools such as Seagate Info. Event-driven scheduling There is no support for event-driven scheduling. Failed loads/updates Essbase informs administrators of complete, partial and failed loads. It generates an error log file and provides a detailed list of records that did not load. Distribution of stored data The partitioning facilities in Essbase allow multidimensional models to be designed in a variety of ways and stored across separate servers. A single model may be partitioned across Essbase Servers, with a ‘virtual’ model for central consolidation. Cross-model calculations are supported via the use of location aliases – effectively a ‘join’ between models.

Sparsity (only for persistent models) Essbase uses two types of internal structures to store and access data: data blocks and the index system. Combinations of dense data are physically stored in data blocks, with the combinations of sparse dimensions held within the index system as a series of pointers. Data is only ever held for combinations that have data values. Essbase provides guidance as to which dimensions should be sparse and which should be dense. Methods for managing size Essbase’s dynamic calculation facility enables administrators to define that certain summarised or derived data values in a database are to be calculated only when a user requests it. Administrators can also specify whether or not to store the result of the user-driven calculation in the database. In-memory caching options In-memory caching options are supported which allow an administrator to define the amount of memory used to cache queries and dimensional members in memory. These are done by cache settings in the Essbase Application Manager. However, it does not provide any self-tuning or wizard- style interfaces to define settings. Informing the user when stored data was last uploaded There are no facilities for alerting users when a model was last refreshed with data. But planned integration with metadata from other tools (via the Integration Server) will give far greater information as to data genealogy.

Management of users Multiple users of models with write facilities Essbase employs a data block locking scheme for handling multiple users writing back to the database. It issues exclusive write locks for data blocks when they are being updated; other users are able to access the data blocks in a read-only mode. User security profiles Users with ‘supervisor’ privileges have full access to all system components and functions. Four other levels of security rights for model access can be defined for individuals or groups of users. These are read, write, calculation and database designer. Query governance Generally, there is no concept of query governance within Essbase. However, dynamic attribute calculations can be restricted by user security. Restricting queries to specified times There is no support for restricting queries to a particular time of day.

Management of metadata Controlling visibility of the ‘road map’ Essbase’s overall security mechanisms govern the visibility of the metadata road map. Users are only able to see the metadata and data they have been granted access to. Access to parts of the metadata catalogue in Integration Server can similarly be restricted. Other than this, there are no facilities to hide or show metadata selectively.

Adaptability

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Essbase models can adapt to change, but there is limited support for the management of change. Users can take advantage of the drag-and-drop method for adding new dimensions and measures in models. Changes in underlying data sources can be automatically uploaded to the multidimensional database as part of a standard batch update process. There are no facilities for ensuring that metadata remains synchronised with changes to models and/or data sources. Essbase does not provide any facilities for impact analysis and there is no direct integration with upstream metadata.

Change in business requirements Adding new dimensions to a model Adding a new dimension to an Essbase database outline model is a straightforward point-and-click task. Through the Integration Server, new dimensions can easily be added to existing models by accessing the predefined metadata catalogue. Re-use of dimension definition Each dimension can be cut and pasted from one model to another. The Integration Server also ensures that a permanent ‘catalogue’ of dimension definitions are available to users via a drag-and-drop interface. Adding new measures to a model Adding a new measure to a model is straightforward and can be achieved using a point-and-click interface. The process is not supported by change management or versioning controls to track the additions or edits. Re-use of a calculated measure definition It is not possible to store and recall calculated measure definitions in a library for later re-use across models. But calculations or rules can be stored and re-used in the metadata catalogue supported by the Integration Server. Changing the architecture to reflect business needs Through the partnership with IBM, Hyperion offers users the ability to store data in a relational form. These users benefit from exactly the same multidimensional functions and features of Essbase.

Changes to data sources Keeping the data source and model schema synchronised Essbase data is stored persistently in a MDDB. As such, there is no possibility of the data source and the model schema getting out of synch. Automatic updating of members in a dimension If new members are added to dimensions these will automatically be picked up by the front-end tools when a user navigates through a model. If dimensions change, Essbase will track the changes to OLAP metadata and automatically synchronise applications (even across multiple partitions).

Metadata Synchronising model and model metadata Metadata comments and descriptions added to the database outline are not automatically synchronised. Impact analysis Essbase does not support impact analysis. Metadata audit trail (technical and end users) Essbase does not provide an audit trail facility for end users. Access to upstream metadata Essbase cannot access metadata in third-party tools – although it does have a preference for Acta for ERP data integration. However, as part of the Integration Server development programme, a number of partnerships have been announced with vendors in the ETL arena. This will give Essbase the capability to access the metadata of these products and the underlying data for rapid model development and increased adaptability. Performance tunability

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One of Essbase’s strengths is its fast multidimensional database engine. Therefore, query performance is taken for granted. For data loading, performance can be tuned by specifying incremental data loads and dynamic calculations at runtime. Large-scale user and application concurrency is supported by the partitioning capabilities, SMP and a new memory-based data cache. Administrators can optimise calculation strategies using one of two options: precalculation or dynamic calculation. Version 6.0 improves Essbase’s ability to handle attribute-rich data. However, questions still remain over its performance with very large, and highly granular datasets typical of e-business and customer data.

ROLAP Essbase is a MOLAP-oriented product. Although Integration Server executes ROLAP-like SQL queries directly to relational databases, the returned data is staged in a prebuilt multidimensional model prior to analysis.

MOLAP Trading off load time/size and performance An Essbase model can have a mixture of precalculated and dynamically calculated variables to avoid database explosion. Essbase can load updates incrementally and subsequently calculate only those parts of the database that are affected by the changes. Parallel loading and recalculation of partitions also improve load performance.

Version 6.0 of Essbase also provides a new memory-based data cache for increased performance. This provides the ability to load the index file (dimension member combinations look-up for data blocks) in memory and set user retrieval buffers; when a user queries a block of data it goes straight into memory. Any dimension structure change will trigger a restructure that is done in memory.

Support for multiple users The data block architecture of Essbase allows multiple users to concurrently read from, or write to, the multidimensional database. Since each query is usually only ever accessing a tiny portion of the database, and will only be moving a very small amount of data to the client, there is unlikely to be any bottleneck in either the CPU or network access. This enables Essbase applications to potentially support thousands of concurrent users.

Processing Use of native SQL to speed up data extraction Data access is via ODBC drivers only. Distribution of processing Essbase’s Application Partitioning function enables developers to simultaneously load and calculate Essbase models across several Essbase Servers (or multiple processes in a single server). SMP support Essbase Server is based on 32-bit multi-threaded software that takes full advantage of SMP parallelism. Customisation

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Application development is provided by a set of ActiveX controls and via Essbase’s published API. The controls can be assembled to build custom EIS- type applications. They can also be integrated with third-party development tools. The Essbase API is functionally rich, and is extensively used by third- party tool vendors and VARs to integrate with Essbase Server. A toolkit is also provided to build custom OLAP capabilities directly into the Essbase Spreadsheet Client.

Customisation Option of using a restricted interface The Essbase Spreadsheet Client relies on the customisation features of Excel to provide restricted views. Wired for OLAP provides greater scope for customisation.

Ease of producing EIS-style reports Essbase Objects is a suite of ‘OLAP-aware’ ActiveX controls, and can be used to develop EIS-type reporting interfaces. Wired for OLAP offers a simple Designer tool for producing EIS reporting capabilities. Additionally, application designers can use the Essbase Spreadsheet toolkit to build ‘push button’ capabilities that support EIS functionality.

Applications Simple web applications The Essbase Web Gateway can integrate with standard web authoring tools to produce HTML applications. The Gateway can also integrate Java and ActiveX components and generates dynamic HTML form controls that can interact with standard JavaScript and VBScript to add greater functionality to web applications. Essbase Objects can also be used to produce ActiveX applications for web deployment. Development environment Essbase does not provide its own visual development environment. Essbase Objects can link into graphical development languages such as Visual Basic. Developers (or VARs) can assemble the ActiveX controls to build EIS-style interfaces to Essbase. A number of third-party ‘Essbase-aware’ controls are also provided by partners such as SPSS and John Galt Solutions. Use of third-party development tools Development tools (including Visual Basic, Microsoft Visual C++, Delphi and PowerBuilder) can be used to integrate with Essbase Objects or the spreadsheet interface.

Other customisation features Essbase API The Essbase API provides a comprehensive library of more than 300 Essbase Server functions that can be accessed by developers to build OLAP applications. The API offers a full range of database access functions including model building, calculation, navigation and write-back capabilities. It is fully documented and also available in HTML format. The Essbase API includes a special OLAP-aware API, called the ‘grid API’, that provides all the functions of the Essbase Spreadsheet Client – developers simply populate a spreadsheet array with the desired data layout and specify an OLAP operation. The API supports popular programming languages including C, C++ and Visual Basic. Essbase Spreadsheet Toolkit The Toolkit provides a library of 30 predefined Essbase-specific spreadsheet macros and 50 VBA functions to incorporate custom OLAP navigation and analysis features into Microsoft Excel or Lotus 1-2-3 applications. Additionally, all the Essbase spreadsheet add-in features can be managed programmatically. Localisation Localised Essbase versions are available in English, French, German and multibyte Kanji languages.

Deployment

Platforms Client Essbase Spreadsheet Client (Excel and Lotus 1-2-3) and Wired for OLAP run on Windows 95, Windows NT and web browsers. Personal Essbase and Hyperion Integration Server clients run on Windows 95. Server Essbase Server runs on Windows NT (Intel and Compaq Alpha), OS/2 and Unix (HP-UX, RS6000/AIX and Solaris). Showcase Corporation has also ported Essbase to the AS/400 environment. Hyperion Integration Server runs on Windows NT and Unix (HP-UX and Solaris). Data access Essbase can access data from the major relational databases (Oracle, Informix, Sybase, IBM DB2 and Microsoft SQL Server) and any other ODBC- compliant database. It can also access data in text files and spreadsheets. Hyperion Application Link can be used to integrate with third-party business applications. Hyperion currently has or is developing certified links to all the major ERP and CRM transaction systems, including SAP, Oracle, Lawson, JD Edwards and Siebel. Standards Essbase has a published API that has been adopted by more than 300 third- party application, service and tools partners to provide integration with Essbase. Wired for OLAP supports OLEDB for OLAP as a consumer. Published benchmarks Hyperion published figures for the OLAP Council’s APB-1 OLAP benchmark. Essbase 6.0 performance figures for the APB-2 benchmark is also planned. Price structure Essbase Server is priced at $60,000 for a ten concurrent user licence. Integration Server is priced at $20,000 per Essbase Server. Essbase Objects and Essbase Web Gateway are both priced at $10,000 per Essbase Server, with an unlimited developer/user licence. Wired for OLAP clients cost $600 per seat for Windows and from $100 per seat for Java. Other Essbase tools and modules are licensed separately.

Summary

At a glance ...... 2 Terminology of the vendor ...... 3 Ovum’s verdict...... 4 Product overview ...... 5 Future enhancements ...... 12

Commercial background

Company background ...... 13 Distribution ...... 14

Product evaluation

End-user functionality ...... 15 Building the business model...... 17 Advanced analytical power ...... 19 Web support ...... 20 Management ...... 21 Adaptability ...... 24 Performance tunability...... 25 Customisation ...... 26

Deployment

Platforms ...... 28 Data access ...... 28 Standards ...... 28 Published benchmarks ...... 28 Price structure ...... 28 At a glance

Developer Information Advantage, Eden Prairie, Minnesota, USA Versions evaluated DecisionSuite version 5.7 Key facts • A ROLAP tool with a server-based OLAP engine • Server runs on Unix platforms; clients run on Windows 3.1, Windows 95, Windows NT and web browsers • Acquired IQ Software, an enterprise query and reporting tool vendor, in September 1998 Strengths • A scalable system – query processing is automatically optimised between the server and the RDBMS • Supports a friendly notebook-style interface across all the client tools • Flexible scheduling, report sharing and messaging facilities are matched by few tools Points to watch • Server runs only on Unix platforms • Limited support for advanced analytical and forecasting functions • An expensive solution for small projects – aimed at organisations with a large-scale data warehouse strategy Ratings

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End user functionality

Building the business model

Advanced analytical power

Web support

Management

Adaptability

Performance tunability

Customisation Terminology of the vendor Agents A background process that users define to automatically run reports at a pre-determined date and time. When combined with event triggers, they also provide a means of automating analysis and reporting tasks. Alerts Alerts are part of DecisionSuite’s messaging system. For example, an agent can announce the availability of an automated report by sending an alert to one or more DecisionSuite users. Category Limits user access to the metadata tables to provide a particular view of the data warehouse. Facts and filters are associated with a category, and all reports are defined in relation to a category. Facts A generic term for data included in reports. Facts can be either data items stored in database tables or calculations that are derived from stored data items and formulae. Facts are defined in the metadata tables and viewed in reports. Filter A static or dynamic constraint on the data presented in a report. They are used to define logical groups of dimension items for inclusion in reports. Filters are re-usable components stored in the metadata tables. Metadata tables DecisionSuite uses relational databases to store two types of metadata. The first type is used to map a data warehouse structure to a business model. The second type of metadata concerns all the other elements of the DecisionSuite environment and client applications, including reports, templates and filters. Report A presentation of data organised according to a report definition that specifies a particular view of the business model and its layout. DecisionSuite reports are interactive, and different perspectives of the model can be achieved. Report template A report definition saved as a template that defines its layout, content and properties. Unlike a report, it does not contain results of processing the definition. Ovum’s verdict

What we think DecisionSuite is an attractive ROLAP solution for customers requiring access to corporate data stored in large, finely-tuned data warehouses. The tool’s scalability is underpinned by a well designed server-based architecture, including an object request broker and a proven ROLAP engine that maximises the use of RDBMS technology while addressing the limitations of SQL. The DecisionSuite designer and end-user tools are easy to use and well integrated; they share the same friendly notebook-style interface that will be appreciated by casual and power users alike. DecisionSuite’s flexible report scheduling, sharing and distribution options are matched by few other OLAP tools and provide strong support for group working environments. However, it lacks the analytical complexity needed for advanced and specialised analysis models. DecisionSuite requires a strong commitment to the ROLAP approach. The server component only runs on Unix and connects to Unix-based RDBMSs. Information Advantage argues that it has focused on the quality of its data access rather than the quantity. Hence, it concentrates on providing optimised, native access to those RDBMSs it has chosen to support. DecisionSuite, like most ROLAP tools, can be difficult to implement because any purchase decision usually involves a wider data warehousing consideration. Customers without a data warehousing strategy will probably need to buy-in some consulting and migration assistance. This means that DecisionSuite is an expensive OLAP solution; the tool’s pricing strategy is aimed primarily at ‘big-ticket’ accounts.

When to use DecisionSuite is suitable if you: • are already committed to a large-scale data warehouse strategy, or are preparing for one • want to develop customer relationship management applications that analyse large sets of data • have a requirement to easily share reports between large numbers of users • have a strong commitment to Unix. It is less suitable if you: • want to perform OLAP against normalised data sources • want to develop highly customised OLAP applications • have a need for advanced or specialised analytical functions • need a flexible business model – the business model is tied closely to the structure of the data warehouse. Product overview

Components DecisionSuite consists of the following components: • DecisionSuite Server version 5.7 • DecisionSuite Client version 5.7; includes Analysis, NewsLine Data Workbench and Application Workbench • WebOLAP version 5.7. Figure 1 shows the primary functions of the components and whether they run on the client or the server. DecisionSuite is a ROLAP tool designed to run directly against relational data stored in a data warehouse. It uses a server-based OLAP engine to interpret end-user queries and dynamically generate SQL queries. The tool works best against relational data (typically aggregate tables) that are organised in either star, snowflake, federation or constellation schemata. DecisionSuite assumes that it will work against a cleansed data source; therefore data warehouse population, data cleansing and advanced data transformations are beyond its scope. DecisionSuite Server A Unix-based ROLAP engine that processes client requests against a data warehouse. The server carries out a significant amount of data processing (joins, aggregations and calculations). The server also takes care of security and manages predefined DecisionSuite Agents to perform various background processing tasks and services. An Active Report Server component acts as a repository for storing reports and report definition templates. Report scheduling and distribution capabilities are also supported. The ROLAP engine uses an intermediary metadata layer to dynamically generate SQL for a query, and delivers formatted content back to the presentation tier. The metadata layer provides a business-oriented map of the underlying database table structures, which automatically synchronises applications with changes in the RDBMS. This information is stored in a series of metadata tables, usually in the data warehouse. The metadata can also map data stored in more than one RDBMS.

Figure 1 Component details

Model design OLAP analysis Web access Management

Client Data Workbench Analysis WebOLAP Application Workbench NewsLine

Server DecisionSuite Server Web OLAP DecisionSuite clients DecisionSuite clients are ‘thin’ implementations and provide design, presentation and management services. All processing of data and storage of reports is done on the server; there is no caching of data on the client. DecisionSuite clients come in three flavours, offering different levels of functionality for different types of user. Analysis An advanced end-user interface for defining ROLAP queries and new reports. Reports can be enhanced by creating custom calculations directly from the Analysis interface. A range of visualisation techniques are also provided. NewsLine A more basic interface for casual users. NewsLine users can tailor reports built with Analysis, or simply view predefined reports delivered by the DecisionSuite Server as part of a schedule or agent process. Workbenches Workbenches are aimed at designers and administrators. There are two types of Workbench interface: • Data Workbench, a DBA tool environment for creating, validating and maintaining the metadata tables • Application Workbench, for administering and managing the DecisionSuite environment. WebOLAP Enables reports to be accessed and analysed from a web browser. It provides NewsLine-like facilities. WebOLAP is closely integrated with the DecisionSuite Server (via CGI), with reports dynamically generated in HTML.

Architectural options Full mid-tier architecture DecisionSuite is a ROLAP tool and does not implement a full MDDB store on the server. Light mid-tier architecture The ‘natural’ configuration for DecisionSuite is a light mid-tier ROLAP architecture. It uses a ROLAP engine that sits on a mid-tier Unix server. Clients run under Windows. If WebOLAP is used, a web server can be added to the architecture to provide web access. The DecisionSuite Server is the central hub of the system that processes client requests against a data warehouse. DecisionSuite is optimised for variations of star, snowflake and federation and constellation database schemata and multiple table aggregation and partitioning strategies. The ROLAP engine processes queries and temporarily caches them on the server at runtime; there is no loading of data into a persistent MDDB store. The server is based on an object request broker (ORB) architecture, where messages are passed between the different service objects, such as those responsible for receiving client requests, connecting to the RDBMS or formatting reports. An important feature of the architecture is scalability. Depending on the nature of the query, data processing is carried out on the server, the relational database or a combination of both. DecisionSuite optimises how processing is split between the database and the server. For example, DecisionSuite Server can do simple aggregations or data filters within the database; more complex procedures that are inefficient to perform in SQL, particularly calculations, will be done on the server. Desktop architecture DecisionSuite is a server-based tool and does not support a two-tier desktop architecture; all processing is done on the server. Mobile architecture This architecture is not directly supported by DecisionSuite. However, a single-tier mode is possible via a partnership with Brio Technology. The two companies have developed DecisionSuite Brio Connection, a facility that supports the transfer of DecisionSuite data into Brio’s Brio.Insight client tool for detached, offline analysis.

Using DecisionSuite DecisionSuite provides a number of tools that distinguish clear responsibilities for designers, end users and administrators. The metadata tables used to map the data warehouse structure to business dimensions are defined by experienced DBAs using the Data Workbench tools. These users are expected to have a good understanding of the data warehouse and SQL syntax. Reports can be defined by DBAs, but can also be created and viewed by business end users using the Analysis client. Experienced power users can use this interface to enhance models by including their own custom measures and filters. The NewsLine interface provides a simple interface for ‘information consumers’ that only require easy viewing access to reports scheduled by the DecisionSuite Server. Administrators are provided with a separate Workbench interface for managing end users. The Application Workbench provides a number of graphical tools that enable managers to configure user security profiles and govern database queries according to time and the size of results sets returned from the RDBMS. The Workbench also provides the management interface for the Active Report Server component, allowing distribution schedules to be built and developing agents that ‘push’ results directly to end users via alerts, e-mails or report attachments. Building and using a business model Building and using an OLAP application in DecisionSuite is a three phase process. Creating the metadata model The first step is to map an existing data warehouse structure in metadata tables so that it is made visible to DecisionSuite Server. The mapping defines the logical elements of a business model, such as dimensions, measures and hierarchies. DecisionSuite Server references the metadata tables to build the SQL statements that it submits to the source database. Metadata tables are defined by an experienced DBA using the Data Work- bench, which gives a spreadsheet-style tabular view of the various metadata tables. Alternatively, the tables can be defined with an external editor. A graphical wizard provides a prompted interface to create and populate the metadata. Initially, DBAs need to set up around ten metadata tables that map the dimensions, attributes and facts, drill-down hierarchies and time periods held in the data warehouse. Figure 2 shows the graphical interface for defining drill-down hierarchies.

Figure 2 Data Workbench: defining drill-down hierarchies Add calculations and filters Designers (or power users) can elaborate the metadata by adding calculations and filters. Calculations are an important element of DecisionSuite models, and two types can be defined using a one-off formula or a calculation template: • simple report calculations, such as averages and totals for a group of cells, can be added to a report via the report calculations dialogue • custom calculations are calculated measures that are stored on the server and are available for use in all reports. Market share, for example, might be defined as a calculation. Calculation templates are skeleton definitions of calculations that use variables rather than actual dimensions. A calculation template can be re- used as the basis for different calculated measures; the user simply selects the appropriate dimension values to be used. DecisionSuite includes a number of standard calculation templates, but users can define their own calculation templates or build calculated facts from scratch using the Calcu- lation Builder facility. Filters define usable selections or groupings of dimension members for inclusion in reports. They are typically used to enhance the model without the user having to access the metadata directly. A filter, for example, can define a new group of dimension members based on an attribute value, such as all items whose product code starts with ‘88’. A filter is either a static or dynamic constraint on the data: • a static filter may define a specific number of dates or a group of items from a dimension: for example, all products beginning with ‘Diet’ • a dynamic constraint will change as the database changes: for example, it may select data for the last six months, or all sales of more than $500. Filters are themselves re-usable objects with DecisionSuite. They are defined through a set of dialogues, using an expression builder if required. All filters are stored in the metadata tables and can be re-used by anyone with the correct access rights. Creating a report The DecisionSuite development philosophy is focused on the concept of ‘reports’ that users create by selecting dimensions from the metadata tables. Reports are defined for or by users, sent to other users, scheduled by agents or published through the Web. Each report is based on a report template that defines its layout, content and properties. Report templates are created in the Template Editor. This is shown in Figure 3. Analysis and Workbench users can readily create templates, but NewsLine users can modify them by changing the layout of dimensions or including different dimension members. Typically, templates will be created for standard reporting requirements in an organisation, such as a market share summary or product ranking. Figure 3 Template Editor

Sharing and distributing reports The notebook-style interface The user interface for all end-user tools is based on the notebook metaphor of a ‘portfolio’. A portfolio is made up of a number of tabbed pages. The first page is always the ‘alert page’ and lists any alerts received, with a short description of each one. An alert might notify a user that a scheduled report has been completed, or it might have an attached report sent by another user. Other pages in a user’s portfolio are used to organise reports in an efficient manner; they can be set up according to each user’s preferred way of working. A portfolio can include folders shared by a workgroup. Distributing reports Support for the sharing of information between large numbers of users is an important element of the DecisionSuite architecture. A number of features within the product promote the easy sharing of reports. For example, a message icon is provided on a standard toolbar across all the client tool interfaces to allow end users and administrators to easily define alert messages, attaching, if required, one or more reports. When the report has run, an alert message appears in the portfolio of all the recipient users. This is shown in Figure 4. As well as sending reports to other users, developers and users can create DecisionSuite Agents to run reports. An Agent runs one or more reports and can deliver alerts to one or more users when the report is completed. Agents can be scheduled to run at set times or can be fired off by a specific trigger event, such as the loading of the data warehouse.

Figure 4 Alert messages

Future enhancements Information Advantage aims to release a Windows NT version of DecisionSuite Server in 1999. The company also plans to make DecisionSuite an OLE DB for OLAP data provider. Following the purchase of IQ Software, integration between the two companies’ products has started at several levels. Initial efforts will focus on providing an Internet-based ‘Active Content Server’ to provide users with a personalised web portal through which they can securely access DecisionSuite tools and data. The new product is expected by the end of 1998 and will be called Eureka! Server. Information Advantage is also seeking partners for developing specific vertical applications based on DecisionSuite tools. Commercial background

Company background History and commercial Information Advantage was formed in 1990, following IBM’s purchase of Metaphor, the EIS/DSS vendor. The Metaphor product group was absorbed into IBM, but the consulting arm set up a new company which became Information Advantage. For the first years of its existence, the new company concentrated on providing consultancy. It developed its first product, a Unix- based decision support engine called Axsys, as a by-product of its consultancy work. Axsys eventually evolved into DecisionSuite Server. The DecisionSuite client tools were first released in August 1994. In 1993, the company obtained venture capital funding in order to develop the product side of the business. Information Advantage is now focused on being a product vendor, and has been building up its direct sales force from offices in the US and Europe. In December 1997, Information Advantage completed its IPO. Revenue for fiscal 1998 grew 118% to $25.6 million. In September 1998, Information Advantage acquired IQ Software, an enterprise query and reporting tool vendor, for $65 million. Although the two companies had radically different product lines and sales models, Information Advantage is now working towards product synergy. The combined company (which retains the Information Advantage name) employs 420 people and has its corporate headquarters in Eden Prairie, Minnesota, with 27 subsidiaries and a network of distributors worldwide. It is valued at $75 million and has more than 1.7 million users of its products worldwide. Character and direction Following the merger with IQ Software, Information Advantage has taken a significant step away from its ‘dimensional relational’ stance, to encompass multidimensional, transactional relational and unstructured content data sources through IQ Software’s technology. The combined company is now repositioning itself to provide a comprehensive enterprise business intelligence suite with a strong focus on customer relationship management and other sales and marketing applications. Both companies have complementary, non-overlapping product lines that will be integrated to cover a range of business intelligence requirements and attract vertical application development partners. Information Advantage sells primarily to the enterprise and has a strong direct sales culture; more than 90% of sales are through direct channels. DecisionSuite’s strength lies in analysing large data warehouses, and is therefore particulary strong in the retail, consumer packaged goods, telecommunications and insurance sectors. Most of the company’s largest customers are Global 100 enterprises such as 3M (30,000 users), MasterCard and Nabisco. IQ Software sells principally to the departmental level, primarily through a vast network of VARs and distributors. Information Advan- tage claims that the two business models are complementary, and will provide increased opportunities to sell smaller, more frequent deals to small- to medium-sized enterprises looking to start small and scale up to an enterprise level. Customer support Support Information Advantage provides telephone hot-line support. On-site support arrangements are also available. Training A number of public and on-site training courses are provided. These include a one-day introductory course for casual end users, a two-day course for analyst-type users and a four-day technical course for IS developers and DBAs. Computer-based training is also available. Consultancy services Information Advantage’s Services division consists of experienced data warehousing consultants and systems integrators that focus on specific vertical sectors. Consultants use Information Advantage’s DecisionPath methodology for implementation.

Distribution US Information Advantage 7905 Golden Triangle Drive Eden Prairie MN 55344 USA Tel: +1 612 833 3700 Fax: +1 612 833 3701 Europe Information Advantage International 3 Furzeground Way Stockley Park Uxbridge Middlesex, UB11 1JF UK Tel: +44 181 867 4600 Fax: +44 181 867 4699

http://www.infoadvan.com E-mail: [email protected] Product evaluation

End-user functionality Summary

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DecisionSuite supports an extremely intuitive notebook-style interface that is consistent across all its client interfaces. Most OLAP functions are easily available from reports. Reports and calculations are easily defined by end users, without IS involvement. However, DecisionSuite’s real strength is its support for flexible group-working. It provides a number of useful features to promote the sharing and intelligent distribution of reports. Particularly impressive is the tool’s collaborative working environment, which allows end users to access shared reports via e-mail, customise them and store them in their own personalised workspace. Finding and understanding the model Finding and loading a multidimensional model The DecisionSuite interface is organised around a ‘portfolio’ containing tabbed pages. Users can have as many tabs as they wish, although generally there are three – alert information from agents, personal information for users’ own reports, and workgroup information (shared reports). Reports are organised in a directory structure. Each report in each tab also displays summary metadata (such as report author, report type, when it was created and when it was last executed) that can be browsed by report users. Keyword or content search facilities are provided for finding reports. Metadata for end users A description of the model in terms of its elements (dimensions and calculations), its author and when it was last updated is stored in the metadata tables. The descriptions are readily accessible to end users of the report. Annotation by the end user Only users with write access to the metadata model may annotate reports. Using the model Basic OLAP functionality DecisionSuite provides a friendly notebook-style interface. Core OLAP functions such as drill-down, slice-and-dice and pivot are easily accessible via point-and-click. Changing the position of members in a dimension level End users can change the location of dimension members (including rows, columns or blocks of data) in a report using drag-and-drop. Visualising the drill-down hierarchies Users are provided with pop-up menus to show their position in the drill- down hierarchy. They can also jump to specific levels in the dimensional hierarchy. Drilling down to detailed data Users can drill-down to access detailed transactional data directly from the report interface. DecisionSuite does not differentiate between aggregated and detailed data; the same user interfaces are used and the same processing is performed. The data does not have to undergo special preparation to be accessible at detail level. Range of front-end user tools DecisionSuite supports two native front-end user tools for the desktop PC: Analysis, for business analysts that wish to design, view and transmit reports to other DecisionSuite users; and NewsLine, for general business users that want to view and customise reports. A web interface is also provided. Third-party tools, such as Brio.Insight, can also access report data, but there is no spreadsheet add-in. Visualising the results The content of a report can be visualised in multiple variations; a default mode is provided to automate visualisation upon initial access of the report. Users can easily select and chart data from within reports. The charting tools support a range of business graphs, and a wizard facility is also provided. Users can simultaneously display multiple tables and charts in a report. However, it is not possible to drill-down or rotate dimensions from within a chart. There is no direct support for visualising data in maps, although integration is provided with MapInfo. Saving and sharing results Designing a report Reports can easily be defined from scratch or using templates. It is not possible to embed images, video, sound or OLE objects in reports displayed through the Analysis or NewsLine clients. However, this is supported in WebOLAP. Publishing a report Users can publish formatted report content to a report repository for access and distribution to other users: • the report caster function automatically publishes and distributes reports to end users based on either an individual or workgroup basis; public and dynamically defined distribution lists are supported • a narrow casting function limits publication to specific users based on their personal or workgroup exceptions. Targeted distribution via e-mail Reports can be distributed via e-mail from the client tool interface. DecisionSuite supports individual, project-based and corporate report in- boxes. DecisionSuite uses the Unix mail system to distribute reports. Ad- dress lists set up within Unix mail may be used, but these cannot be generated dynamically. Subscribing to reports DecisionSuite does not support any report subscription services.

Building the business model Summary

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In DecisionSuite, the report is just one perspective on the business model. Much of the work is done beforehand when defining a business-oriented map of the underlying database table structure (metadata tables). This allows developers to build a logical business model to simplify end-user construction of reports. The business model is flexible, and the use of filters and calculations allow for considerable adaptability. A wizard-driven interface guides designers through the process of describing complex drilling hierarchies and aggregation table information. However, a diagrammatic editor would ease the task of setting up and managing the metadata tables. Basic design Design interface The Data Workbench provides a graphical interface for mapping the data warehouse structure onto DecisionSuite metadata. This interface displays the metadata in spreadsheet-style tables. The Data Workbench is adequate for this task, but it would be better if there was an overview of the main elements, rather than just a series of tables. It would also help if it included dialogues and pick lists to help with the maintenance of the metadata. The wizard provides dialogues and pick lists during the metadata creation. Reports (sets of dimensions, calculations and filters) represent the business model. The design interfaces for both metadata and reports share the same general style of interface. Visualising the data source Designers can see a sample of data from a selected relational table. However, they cannot view the overall database schema. Universally available mapping layer Metadata tables can be defined to map dimensions, measures and hierarchies to specific parts of the data warehouse. Categories provide end users with a restricted view of the metadata tables. Prompts for metadata Designers are not automatically prompted to add additional metadata when creating the metadata tables or defining reports. Building the dimensions Selecting columns for the dimensions Columns for dimensions can be selected using point-and-click. A wizard facility is provided to speed up the mapping process. Selecting the members shown in a dimension level Filters can be used to select dimension members. Filters are created by point-and-click. There are three types of filter: dynamic, static and level. The differences are related to the type of SQL generated. Defining a dimension hierarchy Developers can easily define drill-down hierarchies using point-and-click. Multiple and split drill-down hierarchies may be defined. Unbalanced hierarchies are also supported. Time dimension Time dimensions must be defined according to standard or custom time periods in the business model. Multiple time dimensions are supported, and filters can be used to define non-standard time periods, such as fiscal year and lunar months. Annotating the dimensions Dimensions in the model can be assigned long and short name descriptions by designers, which can subsequently be viewed in a DecisionSuite report by end users. End users cannot edit these dimension descriptions. Default level of a dimension hierarchy Designers can define a default level for a dimension hierarchy when opening a report. Defining the measures Calculated measures Designers and end users can add new calculated measures to the business model at any time, either using a calculator-type interface or a calculation template. A scripting language is available for defining complex calculations. A library of mathematical, logical and relational operators is provided. Support for multiple measures with a set of dimensions Multiple measures can be stored with a set of dimensions. The measures can also be arranged in a hierarchy. Multiple designers Multiple designers DecisionSuite does not provide any special support for multiple designers. Support for versioning There is no direct support for versioning control. Other ‘building the business model’ features DecisionSuite has links to Logic Works’ Erwin data modelling software, which is able to create DecisionSuite metadata tables. The ETL tool Syntagma from Relational Matters also integrates with DecisionSuite. It is able to build and populate DecisionSuite metadata and aggregate tables.

Advanced analytical power Summary

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DecisionSuite provides limited support for advanced analytics, although a number of specialised functions geared towards customer-centric analysis are provided. Calculation templates could feasibly be used to add more powerful analytical capabilities to the product, but these need to be built into the model during the design phase. Users that wish to apply statistical analysis and sophisticated forecasting algorithms directly to model data will need to use specialist tools. There is no Excel add-in facility. Third-party tool integration DecisionSuite does not provide any direct integration with specialised third- party analysis tools. Nor does it provide an Excel add-in. Defining specialised models Ranking and sorting Support is provided for simple definitions of top and bottom order ranking. Mathematical methods Support is provided for logarithmic, trigonometric, exponential and factorial functions. Financial functions Financial functions are not supported. Statistical models Support is provided for a number of simple statistical functions including: moving averages and rolling sums, share and cumulative totals. Trend analysis There are simple functions available for analysing trends based on year-on- year percentage change. Simple regression DecisionSuite offers no support for forecasting. It relies entirely on exporting data to Excel or external statistics packages for this function. Time series forecasting There is no support for advanced time series forecasting algorithms. User-definable extensions A scripting language can be used to create add-ins that integrate with third- party products (such as SPSS) to access advanced analytical functions. Write back for ‘what-if’ analysis ‘What-if’ or budgeting applications that need write access to the database require special handling. In most circumstances, the data warehouse tables will be read-only, so a separate set of tables will need to be created that support write-access. These will then need to be integrated with warehouse data via a custom-built application. Incorporating non-numerical data DecisionSuite supports character string functions for comparing textual data. The results of counts for sub-strings and word patterns can be included in analyses. For example, the calculation builder allows analysts to create procedural if then else type functions that compare text strings held as metrics. The calculation could return a text string for display or a number, which may be summed or counted, for example. Data mining DecisionSuite does not provide support for data mining. Other analytical functionality DecisionSuite offers a number of functions geared towards retail and sales & marketing analysis applications. These include market share, average inventory, rate of sales, BDI (brand development index) and SDI (share development index).

Web support Summary

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WebOLAP provides strong web access for accessing and analysing predefined reports. However, web users cannot define new reports or add new filters or calculations to the report definitions. Reports can be easily published and distributed to a wide range of users over the Web using Internet-based search engines, hyperlinks and e-mail. End-user functionality via the Web Functionality of web access to explore models The WebOLAP client provides the same level of OLAP functions and reporting as the client-server desktop tools. However, it is not possible to add new filters or calculations to models. WebOLAP users can register reports with Internet-based search engines, granting access to reports via hyperlinks. It is also possible to include sound and video objects in web reports. Supports both registered and unregistered web access All WebOLAP users must be pre-registered. Range of users supported by the web interface WebOLAP is well suited for general business users that require easy access to predefined or scheduled reports. Users wishing to define new reports or implement their own calculations are not supported. Creating models via the Web Editing the mapping layer It is not possible to edit the metadata tables via the web browser. Building and editing models It is not possible to create new model or report definitions. Distributing via the Internet and the Web Generate HTML and Java All HTML generated by WebOLAP is dynamic and is built up from templates and rules built into the user profile. No conversion is required, as reports are held in a neutral format and automatically converted to HTML on-the-fly when requested by a WebOLAP client. Corporately organised distribution via the Internet The DecisionSuite Report Caster facilities can dynamically distribute reports via e-mail over the Internet. Include URLs in a report Users can include multiple URLs in DecisionSuite reports. The URLs can reference other reports. Distribution of web server processing There is no integration with middleware to support distributed processing.

Management Summary

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All DecisionSuite application, metadata and user management is defined and maintained on the server through graphical interfaces. The security of reports relies entirely on the Unix and RDBMS security. Agents are used for scheduling report updates and can be based on times and events in the data warehouse. As expected from a ROLAP tool, DecisionSuite provides strong support for query monitoring and governance, and produces detailed usage statistics. Management of models Separate management interface DecisionSuite provides two graphical Workbench interfaces that are similar in design: the Data Workbench is used for maintaining the metadata tables; the Application Workbench is used for administering application components, report objects and end users. Security of models The security of models is governed from a multi-level security model based on Unix, metadata and the RDBMS security systems. All models have associated properties which govern read/modify access. Query monitoring An audit log is generated for each query and report generated, including the author and the time it was run. Administrators can also bring up the SQL generated, and re-run the query for audit trail or debugging purposes. Management of data How persistent data is stored (not scored) DecisionSuite processes data directly from the RDBMS and creates multidimensional models at runtime which are cached on the server. However, once a report has been defined, the data can be stored persistently on the DecisionSuite Server or any other application server, and can be periodically refreshed for current data. Scheduling of loads/updates The loading of data into the data warehouse is outside the scope of DecisionSuite. Once it has been loaded and stored as part of a report definition, a scheduler facility can be used to automate the refresh of reports. Scheduling can be based on times, dates or events. Users can apply a refresh schedule to a group of reports. Event driven scheduling Event triggers can be specified for updating existing reports or scheduling new reports. Triggers can be based on events such as an update to the data warehouse or events external to the OLAP environment. Failed loads/updates An agent may be set up to look for the completion of a report update and then alert users. All agent tasks are persistent, and therefore automatically re-submitted if the update fails. Distribution of stored data Data is stored persistently in the database or the DecisionSuite Server (as a report). When a query is executed, the data is temporarily cached on the server at runtime; there is no caching on the client. Sparsity (only for persistent models) DecisionSuite uses two analytic ‘workspaces’ to efficiently process dense and sparse data returned from the RDBMS. DecisionSuite dynamically routes data to the appropriate workspace based on its sparsity percentage. For sparse data models, DecisionSuite automatically uses multidimensional b-tree, while for dense data models, data is returned as a multidimensional array. Methods for managing size The size of the server cache is subject to size restrictions based on query governance definitions. In-memory caching options In-memory caching is not supported. Informing the user when stored data was last uploaded Each report is time-stamped with information about when the data was last updated. This information is not automatically displayed. Management of users Multiple users of models with write facilities Typically, DecisionSuite is designed to permit simultaneous read-only access. User security profiles The DecisionSuite Server uses a flexible security model to connect to the RDBMS, with anything from a one-to-one user to connection relationship, to all users sharing the same connection. User profiles grant access to parts of the DecisionSuite application environment and metadata. Profiles can be assigned on an individual or workgroup basis. The profiles are also closely linked to categories, which define user access to parts of the data warehouse and available calculations and filters. Query governance Administrators can define the maximum number of concurrent processes used by DecisionSuite Server at any given time. They can also control the maximum number of rows returned and processed on a user profile basis, and specify the maximum time a query is allowed to run in the database. Restricting queries to specified times There is no support for restricting queries to specific times of the day. Management of metadata Controlling visibility of the ‘road map’ The category definition controls access to the metadata a user can access. This definition determines the model metadata, calculations and filters that can be included in a report for a particular user or groups of users. Adaptability Summary

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DecisionSuite’s metadata layer allows for an adaptable business model. New dimensions and measures can easily be defined and re-used across models. All additions are automatically time-stamped. Model metadata can be referenced to ensure that reports are kept synchronised at all times, but there are no facilities for keeping data sources and models in line. There is no possibility to change the architecture from ROLAP to MOLAP mode. Change in business requirements Adding new dimensions to a model New dimensions can be easily added to the metadata tables and subsequently used in reports. Each addition is time-stamped, but there is no direct support for change management. Re-use of dimension definition New dimension definitions are stored in the metadata tables and can be re- used across multiple models depending on the access rights assigned to them. Adding new measures to a model New measures can be added to models at any time by developers and Analy- sis users provided they have the necessary access rights. Folders exist within the model in which to save the calculation definitions and Unix-style security is applied to them. Re-use of calculated measure definition When an end user creates a new calculated measure, the specification is stored in metadata tables and is available for use by other users with the appropriate access rights. Changing the architecture to reflect business needs The Information Advantage tools are all ROLAP-based; there is no possibility of changing the architecture to MOLAP. Changes to data sources Keeping the data source and model schema synchronised Users are not automatically informed of changes in the data warehouse when a report is opened. Automatic updating of members in a dimension As the data warehouse is the only source of information for DecisionSuite, new members are automatically available. However, there is no support to lock a level to prevent new members being automatically updated. Metadata Synchronising model and model metadata A validation function exists to ensure that categories are synchronised with the metadata tables each time a report is created. Impact analysis There is no support for impact analysis. Metadata audit trail (technical and end users) DecisionSuite does not provide any metadata audit trail facilities. Access to upstream metadata DecisionSuite integrates with Informatica’s Metadata Exchange architecture. This enables developers to view extraction and transformation metadata about the columns in the data warehouse that provide the data for the model.

Performance tunability Summary

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DecisionSuite utilises the strengths of relational database technology, while ensuring that processing is optimised between the DecisionSuite Server and the database. It also provides a number of performance-tuning services aimed primarily at minimising access times, such as multipass SQL, native SQL access and SMP parallelism. ROLAP Multipass SQL DecisionSuite automatically generates multipass SQL statements. Options for SQL processing An important feature of DecisionSuite is its ability to intelligently balance SQL processing between the DecisionSuite Server and the database. Speeding up end-user data access The server cache is volatile, and cannot be stored and revised. Aggregate navigator DecisionSuite can automatically access the highest level aggregate tables in the database. It calculates the Cartesian cross-product of dimensional data models which then produces aggregate-level priority information. MOLAP DecisionSuite is a ROLAP tool. Support for multiple users Information Advantage claims that the DecisionSuite Server-based architecture can support many users without degrading performance. It has many customer sites with more than 1,000 concurrent users running DecisionSuite reports against large data warehouses. Processing Use of native SQL to speed up data extraction DecisionSuite uses native SQL interfaces to connect to all the major RDBMSs. It also uses ODBC for Unix to connect to Red Brick, Teradata and HP-Intelligent Warehouse data warehouses. Distribution of processing A client request is automatically routed to the least utilised DecisionSuite Server for processing. There is no automatic load balancing between these servers, because each functions independently. It is, however, possible to balance processing between the database server and the DecisionSuite Server. SMP support DecisionSuite Server takes full advantage of SMP technology.

Customisation Summary

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DecisionSuite provides limited support for application development. Add-ins and a procedural scripting language are available to customise applications and reports. Application development relies on the tool’s API, and using external development tools that can use the same DLL that links the DecisionSuite client modules to the server. Customisation Option of using a restricted interface Various aspects of the DecisionSuite tools’ interface can be modified to provide restricted or extended views and functionality. Ease of producing EIS-style reports Application Workbench provides an ‘add-in’ facility to extend or link pre- and post-process operations for reports. Typically, these are calls to an external procedure, such as a Windows application or a Unix shell script, and are used to customise the execution or results of a report, or add new capabilities. Applications Simple web applications A web gateway API is provided for the development of simple EIS interfaces in HTML or JavaScript. Development environment DecisionSuite does not have a visual development environment. It does provide a scripting language for defining server-based procedures for interaction with external systems or data. The scripting language is a cross between Visual Basic and Unix shell scripts, and uses the standard ‘vi’ editor. Use of third-party development tools DecisionSuite client DLLs can be called by development tools such as Visual Basic, PowerBuilder and Visual C++. Other customisation features DecisionSuite has language support for English, French and German. Deployment

Platforms Client DecisionSuite clients run on Windows 3.1, Windows 95 and Windows NT. WebOLAP runs on standard web browsers including Netscape, Microsoft and Mosaic. Server DecisionSuite Server runs exclusively on Unix: HP-UX, IBM AIX, NCR, SGI IRIX, Sequent, Sun Solaris, Data General DG/UX, Digital Alpha, Siemens Reliant and Unisys SVR4.

Data access DecisionSuite provides native access to the following relational databases: Oracle, DB2, Sybase, Informix, Tandem and MDI. ODBC for Unix drivers are supported to provide access to Teradata, HP-Intelligent Warehouse and Red Brick.

Standards DecisionSuite has its own proprietary server and client APIs. The DecisionSuite OLE DB connection provides support for Microsoft’s OLE DB for OLAP API. WebOLAP supports HTML, Java and JavaScript.

Published benchmarks Information Advantage has not published any OLAP benchmarks.

Price structure Pricing depends on the number of servers and registered users, and the size of the underlying database. Typical entry level pricing is $150,000 for the DecisionSuite Server and 50 users. Clients are priced separately: • NewsLine costs $200 • Analysis costs $1,200 • Data Workbench costs $16,500 • Application Workbench costs $6,600. SQL Server 7.0 OLAP Services

Summary

At a glance ...... 2 Terminology of the vendor ...... 3 Ovum’s verdict...... 4 Product overview ...... 5 Future enhancements ...... 12

Commercial background

Company background ...... 13 Distribution ...... 14

Product evaluation

End-user functionality ...... 15 Building the business model...... 16 Advanced analytical power ...... 18 Web support ...... 19 Management ...... 20 Adaptability ...... 22 Performance tunability...... 23 Customisation ...... 25

Deployment

Platforms ...... 26 Data access ...... 26 Standards ...... 26 Published benchmarks ...... 26 Price structure ...... 26 At a glance

Developer Microsoft, Redmond WA, USA Versions evaluated Microsoft SQL Server 7.0 OLAP Services (Beta 3 and Final Feature Editions) Key facts • A multidimensional engine that can support MOLAP, ROLAP and HOLAP • OLAP Services runs on Windows NT; end-user tools runon Windows 9x and NT • Comes free with SQL Server 7.0 Enterprise and Standard Edition Strengths • Extensive wizard support makes it very easy to use • Easy to move between MOLAP, ROLAP and HOLAP storage options • Wide range of end-user tools available from third parties Points to watch • Not a total OLAP solution, requires an end-user tool • Has no ‘ready-to-run’ web features • Not yet integrated with the Microsoft Repository Ratings

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End user functionality

Building the business model

Advanced analytical power

Web support

Management

Adaptability

Performance tunability

Customisation Terminology of the vendor Cube Microsoft’s term for the multidimensional business model. HOLAP The details are stored in a relational database, and the aggregates in the MDDB. Library The library supports the re-use of dimensions, mappings to data sources and roles. When defining a model, dimensions can be freshly defined (and optionally stored in the library) or library definitions used. MOLAP The details and aggregates are stored in the MDDB. Partition A model can have multiple partitions, all of which have the same dimensions. However, the partitions can be stored in different locations, have different data storage options (that is MOLAP, ROLAP or HOLAP) and different degrees of optimisation. ROLAP The details and aggregates are stored in the RDBMS. Ovum’s verdict

What we think OLAP Services is a product that sets new standards of ease of use and confirms OLE DB for OLAP as the de facto standard for accessing multidimensional databases. Outside of its use in a small workgroup, it is more accurately described as a component rather than a complete solution. The outstanding features of Microsoft’s OLAP Services are its ease of use and its architectural flexibility. It has more than 30 wizards, which enable straightforward multidimensional models to be built entirely using point- and-click. One of the most impressive wizards is the data storage and aggregation one, which enables the data storage architecture (MOLAP, ROLAP or HOLAP) to be selected with one click. The visual display of the trade-off between size and performance makes optimisation, even for the naïve user, an easy operation. This ease of use, combined with the fact that OLAP Services is are bundled with most versions of SQL Server 7, makes it an appealing introduction to OLAP. While the tool has high initial appeal, it is not a complete corporate solution. The most obvious need is for an end-user tool, but this poses little difficulty because entry-level tools can be freely downloaded from the Web, and many third-party tools use the OLE DB for OLAP interface. However, a basic end- user tool may still only provide a minimal system. Within a corporate environment, OLAP requires report production and distribution facilities, web access, customised applications, advanced analytics and metadata for end users. OLAP Services provides very little of this. In this context it is a useful component, but requires considerable supplementing.

When to use The OLAP Services multidimensional engine should be considered if you want: • ease of use as a priority • flexibility of storage options • to be able to use a wide range of end-user tools • a low-cost introduction to OLAP. It is not suitable if you want: • built-in financial or statistical functions for complex analytics • to define complex models with user-defined levels • more security than that provided by Windows NT • a complete (client and server) solution from one vendor. Product overview

Components The main components of SQL Server 7 Enterprise and Standard Edition are: • Data Transformation Services (DTS) • the SQL Server Engine • the Microsoft Repository • Microsoft SQL Server 7.0 OLAP Services. SQL Server 7 does not include a front-end tool for OLAP Services. The Microsoft product for this will be Microsoft Excel version 9 (not released at the time of writing). The main focus of this evaluation is OLAP Services. Figure 1 shows whether the component usually runs on the client or the server, and its primary purpose. OLAP Services OLAP Services is a multidimensional engine that can access data from any OLE DB source, and in turn can be accessed by any tool with an OLE DB for OLAP as a consumer interface. It offers a variety of storage options, including detail and aggregates stored in the MDDB (MOLAP), details and aggregates stored in an RDBMS (ROLAP) and a hybrid combination in which details are stored in an RDBMS and aggregates in the MDDB (HOLAP). Several multidimensional business models can be combined to form a virtual model. The most likely reason for this is that security is defined at model level, so this necessitates the provision of separate models for user groups. Groups requiring a more overall view will generally work with virtual models to avoid duplication of data and effort. Models can be partitioned. All partitions share the same dimensions, but each partition can have a different storage option and degree of optimisation. As in relational databases, the rationale for partitioning is performance gain. OLAP Services provides a wizard-driven environment with the facility to drop down into an editor to make alterations.

Figure 1 Component details

Main purpose

Data Relational Storage of OLAP engine End-user tool extraction data storage DTS metadata and data store for OLAP

Client Microsoft Excel

Mid-tier Data SQL Server The Microsoft OLAP Services server Transformation Engine Repository Services Data Transformation Services In theory, the Data Transformation Services can extract data from any OLE DB-accessible source and move them to any other OLE DB target, but in practice the target is likely to be SQL Server 7 because this is part of the package. As with OLAP Services, the interface is primarily through wizards, and enhancements can be added through a GUI editor and/or programmatically. Using the main import wizard, the DTS effectively maps one source only onto a database with no transformations. Using the editor, multiple sources can be joined, with SQL queries and tables used as a data source. Transfor- mations can be written in VB script, J script and Perl script. For greater flexibility, COM components can be called by a transformation. As well as data manipulation functions, the DTS also provides management facilities, including scheduling. The Microsoft Repository The Microsoft Repository is designed as a unifying thread to tie together data warehousing tools. This will be achieved through the definition of Open Information Models (OIM) to describe data transformations and multidimensional models. Thus, transformations specified by the DTS (or third-party tools) and multidimensional models specified by OLAP Services (or third-party tools) will be stored and exchanged through the use of the repository. At the time of the release of SQL Server 7.0, the OIM for data transformations was usable – but the one for OLAP was not. Thus, the version 7.0 release of OLAP Services uses the Microsoft Repository to store DTS and schema metadata, but does not use it to store multidimensional models. A private repository is used. The company intends to enable models stored in the private repository to be exported to the Microsoft Repository when the OIM models for OLAP are defined.

Architectural options A major feature of the SQL Server 7.0 architecture is that although the DTS, Repository, SQL Server engine and OLAP Services come as a complete datamart package, each of these has an open API. This means that DTS can feed into any OLE DB target, and OLAP Services can take data from any OLE DB source and feed it to any OLE DB for OLAP consumer. Similarly, within each of the components (that is, DTS and OLAP Services) the wizard- driven interface is useful for defining 80% of the required functionality, and what cannot be added with scripting can generally be added with a COM component. Full mid-tier architecture There are two full mid-tier options: • detailed and aggregate data are stored in the mid-tier in the OLAP Services (MOLAP) • aggregate data is stored in the OLAP Services, but detailed data remains in the source database (HOLAP). Changing between these architectures is simply a matter of making a selection in the Data Storage and Aggregation Wizard (a fuller description is given below). There is no in-built support for distributed servers. No end-user tool is provided with the SQL Server 7.0 package; the options are either a third- party tool or Microsoft’s Excel version 9. Thus the choice of the tool determines whether the client is thin or fat. The architectures supporting web access are described below. Light mid-tier architecture The light mid-tier option, in which the data is stored separately on an RDBMS and the mid-tier provides the engine to manipulate this, is an additional option. As with the full mid-tier options, the ROLAP architecture is selected using the Data Storage and Aggregation Wizard. Similarly, the client can be fat or thin depending on the end-user tool chosen. Desktop architecture OLAP Services is the server part of a client-server solution, so the desktop architecture using a two-tier model is not supported. Mobile architecture The mobile architecture is supported by PivotTable Services, a COM component on the client; this enables drill-down and similar pivot table features. In many ways, PivotTable Services is a ‘lite’ OLAP Services. Client tools incorporating this component can load and cache data, and can then disconnect from the data source. It is not possible to store this data persistently on the client. Web architectures Most OLAP tools use a four-tier architecture for web access, based on CGI with an OLAP web server between the generic web server and the OLAP database. Microsoft does not include a web server in the SQL Server 7.0 package, but has two alternative means of giving the user web access using the PivotTable Services COM component and Active Server Pages. PivotTable Services The use of this COM component results in what appears to be a thin client, inasmuch as a browser interface is used, but is in fact a fat client with the processing being carried out on the local machine. When the user accesses the web page, the first thing that happens is that Microsoft’s PivotTable Service COM component is automatically downloaded. The data for the model is then downloaded and the browser interface used to locally process this. There is no generation of HTML pages, so the web connection can be severed and the user can still continue to manipulate the data. Active Server Pages The second architecture makes use of Active Server Pages, Microsoft’s proprietary server-side scripting technology on the IIS Web Server. (The URL of the Active Server Pages ends in ‘.asp’ rather than ‘.html’.) The Active Server Pages are made up of embedded HTML, and script that is interpreted by the web server at runtime. Generally, the script will establish the connection, passing the user ID and password to OLAP Services and then issuing some MDX commands. The result of these is passed back to the Internet Information Server (IIS), which generates the HTML for the browser.

Using OLAP Services In this section, we examine the use of OLAP Services and describe some of the features that differentiate it from other OLAP tools. Extensive use of wizards The whole OLAP Services environment is wizard-driven. It is possible to build a model using nothing but point-and-click and minimal decision making. Figure 2 shows the wizard used in defining the dimensions, and Figure 3 shows the wizard for optimising performance.

Figure 2 Dimension definition wizard Figure 3 Performance optimisation wizard

Independence of OLAP Services from the SQL Server 7.0 relational data store OLAP Services is provided with SQL Server 7.0, so it is likely that many users will use the two together. However, it is important to stress that OLAP Services can access data in any database supporting OLE DB (and using the OLE DB to ODBC mapper this includes ODBC-compliant databases). Ease of changing storage options Storage options for a model are defined using the Data Storage and Aggre- gation Wizard. This wizard first offers a choice of three storage options: MOLAP (detailed data and aggregates are stored in the MDDB); ROLAP (detailed data and aggregates are held in a RDBMS) and HOLAP (detailed data is held in the RDBMS and aggregates in the MDDB). It then provides the designer with visual information about how the size of the model is related to the degree of aggregation selected, and thus the difference in performance that will result from different levels of stored aggregates. Using ‘under the bonnet’ algorithms, the system dynamically calculates this and draws the relationship between the two. This is shown in Figure 4. From the information generated, the designer selects a percentage of aggregations to be pre-calculated and stored. Selecting ‘save and process aggregates’ results in the model actually being built. To see the results of this, the designer can use the cube browser. This is, in effect, a cut-down browser. It provides drill-down and enables dimensions to be changed, but there are no graphics facilities. Partitions are supported A model can be made up of multiple partitions. Partitions within a model have the same dimensions and securities, but can have different measures associated with them, a different storage mechanism and a different level of aggregation. The main use of partitions is for incremental update and scalability. There is no specified limit to the number of partitions a model may have. Partitions are added via a wizard going through the same data storage and aggregation stages described above.

Figure 4 Calculation of performance levels The range of end-user tools Once the multidimensional model has been built, it can be accessed by any client tool with an OLE DB for OLAP consumer interface. This is the only interface that can be used to access data from OLAP Services. Microsoft provides a list of compatible client tools at http://www.microsoft.com/sql/dss. As OLE DB for OLAP becomes established, there is an increasing number of compatible ‘off-the-shelf’ end-user tools. These range from freely downloadable tools (in monetary terms and the degree of effort required) from the Web or tools from more established vendors such as Cognos and Business Objects. Building your own end-user tool Developers can build their own client tools using Microsoft’s ADO MD and the underlying OLAP provider, Microsoft PivotTable Service. The PivotTable Service provides a client-side cache and calculation engine, thus reducing network traffic and minimising response time. PivotTable Service ships with Microsoft’s Data Access SDK and also comes with Decision Support Service. If the end-user tool is written in Visual Basic, the developer uses ADO MD to access schema details of the multidimensional model. Information required to answer end-users’ queries is retrieved (and written back if this is supported by the database) using multidimensional expressions (MDX). MDX is a syntax for specifying a dataset that is defined in OLE DB for OLAP. A typical MDX statement has the same format as SQL, but contains multidimensional model details rather than table details. An example is shown below.

SELECT

FROM

WHERE

Supports member properties Member properties are attributes (for example, colour and size) that are attached to members as a means of handling information that is particular to individual members. For instance, in the product-level of a dimension there may be members as diverse as tennis racquets and blouses. The properties that you might want to store about tennis racquets could be weight and material, whereas for blouses you would want information on colour and sleeve length. If these are defined as member properties, they can be used in filtering and sorting queries, and as ‘virtual’ dimensions. This also helps to deal with sparsity issues. Balances the workload between server and client When a client, using the PivotTable Services COM component, processes a query, the work is done on the client by default. However, if the number of computations is large (as determined automatically by OLAP Services), the computation is moved to the server. Future enhancements Microsoft intends to release the next significant version of SQL Server 7 at the end of 1999 or the beginning of 2000. Plans for this release are still at a very early stage. The company’s intention is to include data mining functionality in this release. One of the uses of this will be to assist the designer in choosing what should be in the dimensions. A further enhancement, which may be released as a point release earlier than 1999 or 2000, is integration with the Microsoft Repository to allow OLAP Services to natively use it. Currently, the metadata is stored in a private repository and does not make use of the Microsoft Repository. Commercial background

Company background History and commercial Microsoft was founded in 1975 by Bill Gates and Paul Allen. Incorporated in 1981, it has become the largest independent software vendor in the world. Fiscal 1998 revenues rose 27.5% to $14.49 billion, and net income increased 30% to $4.48 billion. The style of Microsoft’s growth has been to combine internal product development with the acquisition of companies or important personnel. If the company perceives that a small company has developed a solution, it will attempt to buy that company in its entirety. In the case of larger companies, the same result is achieved by tempting away influential members of staff. Microsoft entered the industrial-strength relational database market by first licensing SQL Server from Sybase in 1987. In 1994, a new licensing agreement gave Microsoft full ownership of the source code of its version, and a clean break with Sybase. In the OLAP area, Microsoft bought OLAP technology and the R&D team from Panorama Software Systems in Tel Aviv, Israel in October 1996. In the wider data warehousing market, Microsoft’s activities were geared towards building up partnerships for its ‘Alliance for Data Warehousing’. During 1997, the most tangible activity was the announcement, in September, of the beta specification of OLE DB for OLAP, a set of COM interfaces extending OLE DB for access to multidimensional data. In 1998, Microsoft delivered products that will ensure it is regarded as a serious competitor in this area. The company has developed a ‘Data Ware- housing Framework’ that combines interface specifications and products. The interface specifications are OLE DB for OLAP (version 1 was made available via the Web in February 1998) and the extensions of the Database Information Model to cover the storage of metadata about data transformations and multidimensional business models in the Microsoft Repository. Character and direction The main business of the company has traditionally been the provision of shrink-wrapped desktop products that have been characterised by ease of use and low cost. In recent years, Microsoft has been seeking to move into the enterprise markets, as evidenced by its ‘scalability’ days in 1997 and the development of a website (www.teraserver.com) to demonstrate that SQL Server 7.0 is capable of dealing with terabytes of data. In the database area, as well as the trend towards greater scalability, there is an acknowledgement that there is more growth in decision support than OLTP. Microsoft has an agnostic approach to OLAP, with OLAP Services supporting MOLAP, ROLAP and HOLAP. OLAP Services is part of Microsoft’s data warehousing framework, covering all aspects of the process from data extraction through to storage and multidimensional analysis. OLAP Services is designed as a commodity data warehousing product, so the prevailing themes are ease-of-use and a concentration on providing 80% of the functionality that most users require, backed up with an open architecture enabling third-party tools to fill in the gaps. The scope of the SQL Server 7.0 solution and the competitive pricing (although details have yet to be announced, this is a low-risk prediction) will place Microsoft in a strong position to exploit the data warehousing and decision support markets. At the lower end of the market, the question is not ‘will the company dominate the space?’ but ‘to what extent?’ At the higher end of the market, the effect is less predictable. Microsoft is developing its indirect channels and intends packaged applications developed by ISVs to be a major outlet for SQL Server 7.0.

Customer support Support SQL Server 7.0 comes with the standard Microsoft helpline and support services. Training The company has stated that it has plans to support an extensive number of short courses, but details are not yet available. Consultancy services Consultancy is not provided by Microsoft, but is available through its partners.

Distribution US Microsoft Corporate Headquarters One Microsoft Way Redmond, WA 98052-6399 USA Tel: +1 425 882 8080 Fax: +1 425 936 7329 Europe Microsoft Europe Microsoft Properties France Tour Pacific Cedex 77 92977 Paris-La Defense France Tel: +1 33 1 46 35 1010 Fax: +1 33 1 46 35 1030 Asia-Pacific Microsoft Asia-Pacific headquarters 65 Epping Road North Ryde, NSW 2113 Australia Tel: +61 2 870 2200 Fax: +61 2 870 2769 http://www.microsoft.com/ Product evaluation

End-user functionality Summary

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The end-user functionality is almost entirely dependent on the end-user client tool used. The features described here are found in entry level tools based on Microsoft’s PivotTable Service, a COM component. More extensive features, such as subscription and distribution services, are outside of the scope of this component, but are provided by more powerful tools that can access OLAP Services models using the OLE DB for OLAP interface (such as Cognos’s PowerPlay and Business Object’s BusinessObjects, both of which are evaluated elsewhere in this report). Finding and understanding the model Finding and loading a multidimensional model Entry-level tools based on Microsoft’s PivotTable Service do not provide support for searching using metadata, and rely on models being grouped appropriately in folders. Metadata for end users No metadata is provided for end users. Annotation by the end user There is no support for additional annotations by end users. Using the model Basic OLAP functionality The PivotTable Service offers all the standard OLAP functionality for drill- down, pivot and changing dimensions. Changing the position of members in a dimension level There is no direct support for changing the position of members within a dimension level. Visualising the drill-down hierarchies It is possible using the PivotTable Service to provide a hierarchical view of the dimensions. Drilling down to detailed data This depends on how the storage for the model has been specified. If MOLAP mode is used, detailed data is available if stored within the cube. If ROLAP or HOLAP mode is used, the detailed data (in the relational database) can be accessed. Range of front-end user tools Any front-end tool offering an OLE DB for OLAP interface can access models in OLAP Services. These are listed on Microsoft’s web page, www.microsoft.com/sql/dss. As this becomes the de facto standard, most major front-end tools will become compatible. Visualising the results All the entry-level products offer a range of graphs, but do not usually include maps. Graphs and tables can be viewed on the same page. Saving and sharing results Saving and sharing results is outside the scope of the PivotTable Service and is generally not available in entry-level end-user tools. Enhancements, such as designing and publishing reports, distribution via e-mail and subscription services are features provided by more powerful (and generally more expensive) client tools. Other end-user features Web applications built using the PivotTable Service (PTS) can take advantage of its data caching facility. These applications download the PTS COM component and the data for the model, then when the network connection is severed the user can still continue to manipulate the data.

Building the business model Summary

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The distinguishing feature of building a multidimensional business model in OLAP Services is the extent to which the designer is assisted by wizards. While the product cannot be faulted on ease of use, it is prevented from getting a higher score in this criteria by its limited support for customising the dimensions and measures. Additionally, and possibly because the model so closely maps onto the columns in the data warehouse, there is no support to collect metadata about the model or its components. A further weakness, if the tool is to be used in a corporate setting, is the lack of support for multiple designers. Basic design Design interface The interface is easy to use and wizard assistance is available at all stages. There are three levels to the design interface: the high-level wizard-driven approach; editors for adding dimensions and calculated members; and the programmatical enhancement through the addition of COM components. Visualising the data source When using the cube wizard to specify the data source, a sample of data is shown. Universally available mapping layer There is no support for a universally available mapping layer. Prompts for metadata When the model is being built, there are no prompts for metadata about the model or its components. There is no direct mechanism for storing the metadata about the multidimensional model in the Microsoft Repository. Building the dimensions Selecting columns for the dimensions Columns are easily selected using point-and-click. Selecting the members shown in a dimension level There is no direct support for selecting members in dimension levels when the model is being built. Defining a dimension hierarchy The dimension hierarchy can be defined by the order of selecting the columns, so the system assumes that the second column selected is the child of the first. Additionally, columns can be promoted or demoted by clicking on arrow keys. The system warns the designer if the current child has fewer members than the parent. It is not possible to define unbalanced dimensions. User-defined levels can be specified by typing in SQL statements, using the Dimension Editor. Time dimension A dimension can be either ‘standard’ or ‘time’. If it is the latter, then a variety of time hierarchies is automatically available to the designer. Annotating the dimensions Dimensions have only one name, so there is no support for a shorter name to appear on tables and charts as well as a more descriptive, longer name. Default level of a dimension hierarchy The default level of a hierarchy is always the top level. It is not possible to change this. Defining the measures Calculated measures Calculated measures are specified using the ‘calculated member box’. It provides limited built-in functionality with arithmetic operators and a small range of functions. Support for multiple measures with a set of dimensions The tool supports multiple measures with a set of dimensions. Multiple designers Multiple designers There is no support to prevent ‘lost updates’ if multiple designers are working on a model. Support for versioning There is no support for versioning. Other ‘building the business model’ features There are two very useful wizards to aid optimisation: the Data Storage and Aggregation Wizard and the Usage-Based Optimisation Wizard. The former visually graphs the increase in size against the percentage of pre-calculated aggregates and the latter provides statistics on use.

Advanced analytical power Summary

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OLAP Services’ limited support for advanced analytical power emphasises that this is a tool for ease and speed of implementation and should not, by itself, be used for powerful analytical work. It is possible to extend the analytical functionality by incorporating COM components; this requires an additional sophisticated skill set. When writing applications that access data stored in OLAP Services, the user can use some multidimensional expression (MDX) functions. However, the functions available within the tool are those that reference a member rather than those that process sets. It thus provides very limited assistance in defining complex analytical functions using OLAP Services’ Calculated Member Builder. End-user tools accessing OLAP Services could make use of the MDXs mentioned below to provide users with some specialised analytical support. Third-party tool integration It is possible to access models developed in OLAP Services using Excel 97, although this requires some technical skills. When Excel 9 is released in 1999, Microsoft has stated that integration will be well supported, which should improve the score in this area. At the time of writing, none of the major third-party analytical tools (for example, SPSS, SAS ETS or Gauss) provide interfaces. Defining specialised models Ranking and sorting Supported by the MDX functions of rank, top-count, bottom-count and tail. Mathematical methods There is no support for complex mathematical models. Financial functions There are no complex financial functions Statistical models There is some support using the MDX functions of standard deviation and variance. Trend analysis There is no support for complex trend analysis. Simple regression Some support is provided by the MDX linear regression function. Time series forecasting There are no functions to support forecasting. User-definable extensions The limited built-in functionality can be enhanced by writing the function as a ‘.dll’ file and then registering it as a function. Write back for ‘what-if’ analysis Write back is supported. When the updates are written back, they are stored in a relational partition in SQL Server 7.0 or a relational database accessible using OLE DB. Incorporating non-numerical analysis There is no support for non-numerical analysis. Data mining There is no support for data mining functionality.

Web support Summary

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OLAP Services, by itself, does not provide web support. As with several of the features considered in this evaluation, if they are required, they have to be added on by the use of an appropriate third-party and/or end-user tool. Some of the entry level tools offer web access using a COM component or Active Server Pages on IIS. This enables users to explore models using browser access, but there is no support for the creation of models, nor for using the Web and the Internet as distribution mechanisms. End-user functionality via the Web Functionality of web access to explore models OLAP Services does not provide a component to support end-user functionality via the Web. It is, however, a feature of several of the end-user tools that can access OLAP Services via the OLE DB for OLAP interface. These tools do this either by downloading the PivotTable Services COM component or through the use of Active Server Pages, as described in the earlier section on Web architectures. Supports registered and unregistered web access It is possible for known users and ‘guests’ to use the web access described above. Range of users supported by the web interface There is no support for producing EIS-style reports for viewing with a web browser. Creating models via the Web There is no support for this. Distributing via the Internet and the Web This is outside the scope of the tool. Distribution of web server processing This is outside the scope of the tool.

Management Summary

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OLAP Services has good support for managing size and partitioning the data, but poor support for scheduling uploads and providing user security and controls. OLAP Services provides a published API, Decision Support Objects (DSO), to control the management aspects of the tool. This is used, for instance, by the cube-building wizards. Microsoft has not yet produced easy-to-use functionality to support the scheduling of loads and updates, so users have to either do this manually or write their own applications using the DSO. The most obvious enhancement needed in this area is the provision of wizard support for the management of data. Management of models Separate management interface The management of data, models and users is carried out through a variety of interfaces. Security of models OLAP Services relies on NT authenticating the user. Query monitoring Information on queries is via the Usage-Based Optimization Wizard, which enables the administrator to select a model and a partition within it, and can then see how many queries were made after a date, how many took longer than a specified time and which queries are popular. While it suggests which aggregates should be added or replaced, it does not suggest which aggregates should be deleted because they are not used. Management of data How persistent data is stored (not scored) OLAP Services offers three options for storing data: • MOLAP (both aggregates and detail data are stored in the MDDB) • ROLAP (both aggregates and detail data are stored in the RDBMS) • HOLAP (aggregates are stored in the MDDB and detail data in the RDBMS). Scheduling of loads/updates There is no direct support for this. Scheduling has to be done manually or developers could write an application using the Decision Support Objects API to control this. This requires programming skills. Event-driven scheduling There is no direct support for this. It could be done using DSO. Failed loads/updates There is no support for scheduling. Distribution of stored data Partitions of a model can be distributed on different servers. Sparsity (only for persistent models) Sparsity is handled ‘under the bonnet’ and the designer does not have to make any decisions about it. Methods for managing size The Data Storage and Aggregation Wizard gives an excellent visual representation of the effects of trading off size (a function of the amount of pre- calculated aggregates) for performance gain. From this representation, the user makes a choice. In-memory caching options There is no support for in-memory caching. Informing the user when stored data was last uploaded There is no support to enable end users to be informed of the currency of the data. Management of users Multiple users of models with write facilities Not applicable because write back is not supported. User security profiles There is no real support for defining user profiles, because user access to models is limited to read or not. Security is controlled by creating different models for different users. To minimise duplicated data, senior managers see a virtual model made up of several physical ones. Query governance There is no support for query governance. While this is not a problem when OLAP Services is in MOLAP mode, it may be required when used in ROLAP mode. Restricting queries to specified times There is no support for this. Management of metadata Controlling visibility of the ‘road map’ There is no metadata other than the model itself. While there is a mechanism to prevent users accessing the model, there is no improvement of the security offered by NT to restrict the visibility of the model.

Adaptability Summary

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The most notable strength of the tool with regard to adaptability is the ease of changing the storage architecture. It is also easy to add dimensions and measures. There is effectively no metadata to synchronise, which – although it is a limitation in other areas – does at least make implementing the changes straightforward. The tool is prevented from getting a higher score by the lack of support to track and predict the impact of changes. Change in business requirements Adding new dimensions to a model New dimensions are added using the Cube Editor, but there are no change management facilities to track these changes. Re-use of dimension definition Dimension definitions can be stored in the library and re-used from there. Adding new measures to a model New measures are added using the Cube Editor. Re-use of calculated measure definition Measure definitions cannot be saved and re-used. Changing the architecture to reflect business needs A particular strength of OLAP Services is the ease of changing the architecture to reflect different business needs. When a new partition is defined, the designer can switch between MOLAP, ROLAP and HOLAP modes. Changes to data sources Keeping the data source and model schema synchronised If all the data for the model is stored in the MDDB (via MOLAP), it will always be synchronised with the model. However, if either the detail data and/or aggregate data is stored in an RDBMS (HOLAP and ROLAP) then changes to the data sources in the RDBMS would lead to a lack of synchronisation between the data and the model. There is no automatic mechanism to forewarn the user if this is the case. Automatic updating of members in a dimension New dimension members are automatically imported. There is no direct support for limiting the import of new members. Metadata Synchronising model and model metadata Not applicable, because the only metadata held is schema details. Impact analysis There is no support for impact analysis. Metadata audit trail (technical and end users) There is no support to show the end user the history of the metadata. How- ever, there is very little metadata so this is not significant. Access to upstream metadata There is no integration with extraction tools to access metadata generated upstream.

Performance tunability Summary

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The administrator has most scope for performance tuning when the tool is used in MOLAP mode. The most useful feature is the visualisation of the relationship between database size and performance. In ROLAP and HOLAP mode there is limited scope for performance tuning. ROLAP Multipass SQL OLAP Services uses multipass SQL when in ROLAP mode. Options for SQL processing There are no options for specifying where the processing takes place. It is always carried out on the database server. Speeding up end-user data access When in ROLAP mode, retrieved data is cached for the duration of the session. When the cache is full, some data will be lost. Aggregate navigator There is no support enabling SQL queries to transparently make use of summary tables. MOLAP Trading off load time/size and performance One of the most impressive features of OLAP Services is its wizard support for trading off the percentage of pre-calculated aggregates (size) against the performance gain. This plots a graph in real-time showing the relationship between the two, and the user can then choose what percentage of aggregates to have. As well as reducing the number of pre-calculated aggregates, reducing load time can be done by limiting the recalculation of aggregates when new data is entered, so that only aggregates affected by the new data are recalculated. In OLAP Services, this is achieved by loading the new data into partitions leaving the original data as it was. The drawback of this is that end-user queries may then require access to multiple partitions. This can be coun- tered by merging partitions, and will generally be done at weekends or when the system is quiet. Multiple users OLAP Services has not been available long enough to establish the degree of support for multiple users. Processing Use of native SQL to speed up data extraction All data extraction is carried out using OLE DB for OLAP. Distribution of processing There is no support for distributing the processing between multiple OLAP Services servers. SMP support The architecture is multi-threaded and can take advantage of SMP. Customisation Summary

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The low score in this criteria reflects the absence of support provided by the tool to develop customised interactive applications. However, if viewed as a component within an application, then the ubiquity of the API and the low cost makes it attractive to developers. It cannot be used to customise, but is itself customisable. OLAP Services is a component, with an open API, that can be used within a customised application. The development of the application can be carried out in any COM-compliant environment such as Visual Basic or C++, but OLAP Services itself does not provide an environment for developing these. Customisation Option of a restricted interface This is a feature of the end-user tool. It is generally not an option in the entry-level tools considered in this evaluation. Ease of producing EIS-style reports There is no direct support within OLAP Services to provide a customised, easy-to-use environment. It could either be achieved via an end-user tool or by incorporating OLAP Services as a component within an application. Applications Simple web applications There is no direct support to build simple EIS applications to run in a browser. Development environment No OLAP-specific development environment is provided. Use of third-party development tools Applications that access data in OLAP Services using the OLE DB for OLAP interface can be developed in any COM-compliant development environment. Deployment

Platforms OLAP Services runs on Windows NT, end-user tools on Windows 95/ 98, and NT. Using Windows 95 or 98 is appropriate for personal use, but not as a server for multiple users.

Data access OLAP Services can access any data available via OLE DB or ODBC.

Standards OLAP Services uses Microsoft’s OLE DB for OLAP API.

Published benchmarks There are no published benchmarks for OLAP Services.

Price structure SQL ServerOLAP Services is not available separately, but is included in the Enterprise and Standard Editions of SQL Server 7.0. At the time of writing (pre-launch), the price structure was not available, but Microsoft states that it will be similar to SQL Server 6.5. (SQL Server NT 6.5 with ten-user licences costs approximately £1,500 in the UK.) Microstrategy DSS Product Suite

Summary

At a glance ...... 3 Terminology of the vendor ...... 4 Ovum’s verdict...... 5 Product overview ...... 6 Future enhancements ...... 14

Commercial background

Company background ...... 15 Distribution ...... 17

Product evaluation

End-user functionality ...... 18 Building the business model...... 20 Advanced analytical power ...... 22 Web support ...... 24 Management ...... 25 Adaptability ...... 28 Performance tunability...... 29 Customisation ...... 31

Deployment

Platforms ...... 33 Data access ...... 33 Standards ...... 33 Published benchmarks ...... 33 Price structure ...... 33 Evaluation: Microstrategy – DSS Product Suite Ovum Evaluates: OLAP

At a glance Developer Microstrategy, Vienna, Virginia, USA Version evaluated DSS Product Suite version 5.5, consisting of: DSS Architect, DSS Agent, DSS Server, DSS Administrator, DSS Web, DSS Broadcaster, DSS Executive and DSS Objects Key facts • A ROLAP product with a client-based engine generating SQL • End-user tools run on Windows 3.x, 95, 98, NT and OS/2; server runs on Windows NT. Web access is supported • Microstrategy is repositioning itself to provide commercial business intelligence applications for the e-commerce market Strengths • Easy to build and access models if data is stored in a data warehouse using a snowflake or star schema • Strong data analysis and broadcasting capabilities via the Web • Includes development tools for EIS applications, and an API for building applications in an OLE-aware language Points to watch • Highly dependent on the design and processing capabilities of the data warehouse for performance • Limited support for specialised analysis – relies heavily on Excel for analytical capabilities • Large implementations involve a wider data warehousing consideration – a significant amount of consulting is usually required Ratings

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End user functionality

Building the business model

Advanced analytical power

Web support

Management

Adaptability

Performance tunability

Customisation

Terminology of the vendor Agent A collection of reports, usually grouped under business themes. Attribute A level in a dimension hierarchy that either maps directly on to a column in the database, or can be a derived column. Broadcasting The timely delivery of business intelligence information to end users via a variety of devices. Cache A Microstrategy cache is persistent. It can be stored either on the server or the client. The place and duration (from hours to months) of storage is defined when the model is created in DSS Architect. Microstrategy refers to a ‘datamart’ as a cached subset of the data warehouse. Element The term used to refer to a member. Filter Used to constrain the data that appears in a report. In SQL terms it is the ‘where’ part of the query. Metric Corresponds to Ovum’s definition of a calculated measure. Project This includes both the multidimensional business model and the location of the data referenced by it. It is used as a basis for defining reports. Template Defines the layout, base content and presentation of a report. In SQL terms it is the ‘select’ part of the query.

Ovum’s verdict

What we think The DSS tools are well integrated and are becoming increasingly web- focused. They are also highly dependent on a data warehouse for design and performance. Any purchase decision will therefore involve a wider data warehousing consideration. Large implementations usually come with a lot of consulting, with rollouts taking from 6 to 18 months to complete. The ROLAP architecture makes the DSS tools well suited to the routine analysis of large volumes of data. The end-user tools are easy to use and provide most of the functionality required by casual and regular OLAP users. DSS Architect’s well designed interface simplifies the mapping of relational to multidimensional structures. DSS Broadcaster is a major component of the toolkit. It provides excellent support for distributing information in a timely manner via the Web and other wireless channels. The sophisticated reporting and broadcasting capabilities are in line with Microstrategy’s belief in the commercial benefits of distributing (and selling) analytical information to a wide range of users. However, the significant investment required to structure the data in order to take advantage of the features of the toolkit leads to a degree of lock-in. While the snowflake schema does not preclude using other reporting and query tools, it is unlikely to be the preferable design schema if other tools are to be used. However, the advantage of the Microstrategy approach is that the company can provide full support for building, maintaining and using a data warehouse for OLAP. Although DSS Web exploits the Excel function library, the client-server tools offer limited support for complex and specialised analytics. This is primarily a consequence of the ROLAP architecture, which uses specialised analytics created and stored in the data warehouse, rather than creating them within the OLAP tool. Microstrategy has yet to introduce DSS Server as an OLE DB for OLAP data provider; this limits end users to the vendor’s own front ends.

When to use The Microstrategy tools are suitable if you: • have already built a data warehouse using a snowflake schema • have customer management or other applications where the data volumes are large and there are many members in each dimension • need good broadcasting support using a variety of devices. It is less suitable: • as a departmental OLAP solution, because of its dependency on the data warehousing architecture • if you require many different or rapidly changing business models • if you want to develop highly customised OLAP applications • if you require specialised and highly complex metrics beyond those provided by Excel.

Product overview

Components The main components of DSS Suite (all version 5.5) are: • DSS Architect • DSS Agent • DSS Server • DSS Administrator • DSS Web • DSS Broadcaster • DSS Executive • DSS Objects. Figure 1 shows whether the component runs on the client or the server, the stage at which it is typically used, and its primary function.

DSS Architect This is a developer tool used to build multidimensional models and define the mappings to the physical database schema. It is also used to specify how the model appears to the user. The Microstrategy solution is closely tied to the dimensional design of the data warehouse, so the manual gives extensive advice about the appropriate warehouse schema to use. This generally involves using the snowflake schema, although a star schema can be used for simplicity (but will not give such good performance). DSS Agent An end-user tool used for report development (for example, applying filters and templates to the model defined in DSS Architect to specify a report) and ad hoc analysis of the business model.

Figure 1 Component details

Modelling OLAP Web access Management Distribution Development analysis

Client DSS DSS Agent DSS Objects Architect

Server DSS Server DSS Web DSS DSS DSS Executive Administrator Broadcaster

DSS Agent generates the SQL to retrieve data from the data source and can be configured in two-tier mode, with direct access to the data warehouse, or three-tier mode, with DSS Server as an intermediary. It also integrates with ETL tools to allow users to view metadata from data warehouses. DSS Server This is the core server component for the DSS Suite. SQL queries, whether generated by DSS Agent or DSS Web, are passed through this server. The server redirects queries to either the source database (where all processing occurs) or to cached datasets. DSS Server provides three tools: • Scheduler – uses scheduled agents to refresh caches and reports based on time- or event-driven criteria. The scheduling component can be run as a Windows NT Service • Cache Manager – a graphical console for monitoring report caches • a runtime management environment – allows information to be displayed about jobs that are running. A transaction log is provided to store statistical information about system usage and SQL queries. DSS Administrator This is the main administration component for the DSS Suite. It consists of two main interfaces: • Warehouse Monitor – provides information about performance trends, usage trends and statistics by user, report and time. It can be used to assist performance-tuning of the data warehouse (for example, the need for aggregation tables and indexing), and to determine the best time to schedule jobs and cache results • Object Manager – controls the management of users and DSS Suite objects (such as reports, templates, filters and measures). It can be used to generate user and group profiles, and to define access rights to application objects and system functions. DSS Web This is a web server that enables a web browser to be used as a thin client (an alternative interface to DSS Agent), as well as providing an environment for developing an EIS or a customised front end. Development is achieved with a combination of HTML and JavaScript, with some Java applets. DSS Web requires a four-tier architecture. Messages are passed from the browser to the standard Internet server (for example, Microsoft’s IIS), then to DSS Web Server where the query engine generates the necessary SQL, which is passed to DSS Server via RPC and then on to the data warehouse. DSS Web is licensed either for full functionality (DSS Web Professional) or as a viewing tool for pre-built reports, but without the facilities to drill anywhere or create reports (DSS Web Standard Edition).

DSS Broadcaster This is an information distribution and broadcasting server. DSS Broadcaster uses reports created in DSS Agent or DSS Web. It manages the distribution of these reports to a variety of end-user devices, including mobile phones, pagers, PDAs and fax terminals, and via e-mail clients. DSS Broadcaster includes a sophisticated HTML generation engine that exploits XML (extensible mark-up language) and XSL (extensible style-sheet language), to deliver formatted and highly functional e-mails. DSS Broadcaster’s administration console offers a number of graphical tools to manage content and set up the distribution environment. Reports can be scheduled or event-driven, and dynamic distribution lists can also be defined. DSS Executive This is an easy to use development environment that is used to create EIS- like front-end interfaces for casual users of DSS Agent. It provides a number of ready-to-use EIS objects (buttons and icons) that can be mapped on to reports to create simple briefing-book applications. DSS Objects This acts as an interface to DSS Server that allows for the development of custom applications using OLE-enabled application development languages (such as Visual Basic, Visual C++, Visual Basic for Applications and Delphi). The API provided in DSS Objects enables the application to make high-level function calls to the query engine and server, making use of predefined metrics, templates and filters. The developer can create user-defined filters and templates using the API. There are, however, no ready-to-use components that provide a GUI interface for creating these objects when building an application.

Architectural options Full mid-tier architecture Microstrategy only supports a ROLAP approach. Light mid-tier architecture The Microstrategy toolset offers two variants of the light mid-tier architecture: • fat client • thin client. The fat-client configuration uses DSS Agent as the client. SQL is generated by DSS Agent, but is then passed to the data source via DSS Server. (The primary role of DSS Server is not to generate the SQL but to monitor and manage the flow of data in realtime.) With a three-tiered architecture, the client can use centrally cached data. All processing of SQL queries is carried out on the database server. The thin client configuration is used for web access and requires the inclusion of both DSS Server and DSS Web. With this configuration, DSS Web Server, is the engine that generates the SQL to retrieve the data from the database. The role of DSS Server is to manage and monitor the data.

Desktop architecture Running DSS Agent directly against the data source is the simplest two- tiered configuration. DSS Agent generates the multipass SQL, which is processed on the database, and the resulting datasets are manipulated in DSS Agent. This configuration is usually only used for small projects and testing. Mobile architecture There is no direct support to download a subset of data from the relational database and run queries against this. By definition, ROLAP tools are not geared to support this architectural configuration.

Using the DSS Product Suite The need for a data warehouse Microstrategy provides a set of components to develop, use and manage multidimensional models. The tools do not provide data cleansing and transformation functions because the toolkit is intended for use against pre-cleansed data stored in a data warehouse (using a snowflake schema). An initial requirement therefore, which is not supported by the toolkit, is to design an appropriate data model and build a data warehouse. There is advice about this in the DSS Architect manual. As shown in Figure 2, building the dimension hierarchy assumes that the structure of the data source is a look-up table with an ID and description column. The use of cached data In the ROLAP model, data for a multidimensional model is retrieved as needed from a relational database. However, retrieved data can be cached in two of the following ways: • locally – in a binary format including crosstab data • centrally on the server – in a relational format without pre-calculated crosstabs. The cache is non-volatile and available over several sessions. DSS Server dynamically directs repeat queries to caches when information is currently available, rather than executing a fresh query on the data warehouse. Feeding a datamart The toolset provides the option of feeding retrieved data into a relational database on the network. Microstrategy calls this ‘dynamic datamarting’. It is used as a means of passing data to other tools for further analysis. Division of responsibility The tool lends itself to a clear division of responsibilities between designer and user. The mapping layer is defined by the designer using DSS Architect, and reports are defined by the user in DSS Agent.

Figure 2 Building the dimension hierarchy

Within DSS Architect, the designer defines the information that can be used to build multidimensional business models, maps this on to the source data and specifies how it is presented to the user. A report is based on a project defined in DSS Architect. The report adds templates (which define the slice of the model to be used) and filters (the rows to be included). In DSS Agent, users can build new reports, run previously created reports or run ad hoc queries. Support for casual users When the user first opens DSS Agent they have to elect to work in either EIS or DSS mode. Users work in EIS mode if they wish to use an EIS application developed with DSS Executive. Working in DSS mode gives the user full access to all the DSS Agent functionality. Three choices of interface are available: • power user – allows full functionality to develop filters, templates, reports and agents • DSS Analyst – provides reduced functionality • high-level user – runs previously-defined reports. Users can easily build a simple report using basic components Assuming that there are some previously defined templates and filters, the simplest way to create a report is to use the Report Wizard. This first guides the user to select a template from a list of those available, as shown in Figure 3.

Figure 3 Template list

A selected filter can then be modified if required. The results of a report can be viewed in grid, graph, map or alert mode. For alert mode, data meeting previously specified criteria is displayed with headlines, optional headers and optional footers. Building more sophistication into a report Define new measures The power user may wish to define new measures to add to a report. This is considered to be an advanced feature within the toolkit. New measures are defined using a calculator-like interface that provides arithmetic operators and the SQL aggregate functions. If the database being accessed is Red Brick, the additional RISQL functions (such as rank, tertile and cumulative) are also available. Compound measures can be created by combining standard mathematical operators. Values can also be incorporated, and can be defined as non-aggregable (for example, profit). All new measure definitions are SQL expressions. They can be re-used in a number of reports and edited if required. Define a filter A filter is a set of conditions that the data must meet in order to be included in the report. It can be used to define a subset of rows (for example, ‘product=spam’ or ‘sales>1,000’) and to handle data attributes. Compound filters can be built. A filter is prepared using the Filter Editor. As shown in Figure 4, there is a tab for each dimension in the Filter Editor, and one for each measure. Within each dimension, attributes dragged into the right-hand box open an attribute qualification box. This provides operands such as ‘<’, ‘>’, ‘like’, ‘not like’ and ‘between’.

Similarly, measures can be qualified based on value, rank or percentile. A further option is to use auto-prompts, which effectively parameterise the filter and prompt the user to insert a value when the filter is applied. Define a template A template specifies how the data retrieved from the data warehouse will be displayed in each of the reporting modes (that is, crosstab or tabular). If a report is created using the Report Wizard in DSS Agent, then the template definition is part of that process. Alternatively, it can be designed separately using the Template Editor. Essentially, it enables the developer to drag-and- drop attributes to define the row and column headers and then add measures to this layout. Information broadcasting A key differentiator of DSS Suite is the importance it places on ‘push’ technology (Microstrategy calls this ‘broadcasting’) in distributing information to large communities of users, both inside and outside an organisation. DSS Broadcaster is an information broadcast server component that enables large-scale report distribution. It includes a web-based self-subscription interface through which users can register themselves, subscribe to services and choose device types for delivery of information. Wizards are provided to guide administrators through the configuration of personalised broadcasting criteria, such as schedules, styles (using content filters) and threshold conditions.

Figure 4 Filter Editor

Internet e-mail is the main gateway out of DSS Broadcaster, although a range of devices are supported, including pagers, mobile phones, PDA handheld devices and fax terminals. DSS Broadcaster includes a sophisticated XML generation engine that exploits XML (extensible mark-up language) and XSL (extensible stylesheet language) technology to deliver customised and functionally rich HTML output to remote users. E-mails can act as small applications in their own right. They can be used as a starting point for accessing additional information or to drill deeper into the data for further analysis. Figure 6 shows that e-mails can include alert messages, as well as dynamically generated Excel workbooks with detailed reports and DSS Web URL links for ad hoc analysis capabilities. DSS Broadcaster also provides an administration console for controlling the broadcast environment. Monitoring and browsing tools allow systems administrators to view the status of services, schedules and style objects to which a user or group is subscribed.

Figure 5 A functionally rich broadcast e-mail

Future enhancements The next major release of DSS Suite is scheduled for late 1999. The new version will support a new server-centric architecture, with thinned down clients, allowing easier deployment and centralised management. The web tools will exploit XML and XSL technology for report generation and formatting. Microstrategy has given no further details of the new release; formal announcements are expected in mid-1999. Microstrategy is still considering support for Microsoft’s OLE DB for OLAP as a data provider support, but it has not announced a definite date for delivery.

Commercial background

Company background History and commercial Microstrategy was founded in 1989 by the existing president, Michael Saylor. The company originally offered consultancy rather than products. It built custom decision-support applications for large companies such as DuPont, Merck and Xerox. DSS Agent was released in 1994, and was the first in a line of decision- support software based on ROLAP. In 1995, DSS Server and DSS Administrator were released. The first web program was DSS Web, which was released in 1996; DSS Broadcaster was released in August 1998. In 1999 the company restructured itself into three principal business units: • Business Intelligence: focuses on traditional business intelligence solutions using its ROLAP tools and a central data warehouse • Commercial Intelligence: develops decision support applications to support business-to-business/customer/partner information provision, focusing specifically on the e-commerce market • Consumer Intelligence: provides business-to-customer information delivery applications and services to industry sector consumers such as telcos and ISPs. These applications are targeted directly at information consumers and revenue is derived mainly from advertising and subscription fees. One example is DSS Stockmarket, which delivers personalised stock market reports to subscribers Microstrategy is one of the fastest growing software companies in the OLAP industry. The company was entirely self-financing until it went public in June 1998, which raised $48 million. Microstrategy now employs over 900 people. It has its headquarters in Vienna, Virginia with 27 offices and a worldwide network of VARs and distributors. Microstrategy has had an impressive growth rate since going public. Revenues for 1998 increased by 99% to $106.4 million, and net income was $6.2 million. While originally a consultancy-based company, Microstrategy now generates around 70% of its revenue through software. Character and direction Technically, the philosophy behind the Microstrategy product strategy is a total commitment to the ROLAP approach. Its core focus is on large ROLAP implementations, which account for 80–90% of revenue. The ROLAP solution is dependent on the design of the data warehouse for optimisation, so the company is also in a strong position to design this. Microstrategy intends to make information universally accessible to all types of user. The company’s motto is ‘information is water’. This vision is characterised by Michael Saylor’s ‘query-tone’ concept: just as the dial-tone makes telecoms universally available, the query-tone makes access to information in a data warehouse a ubiquitous utility.

What differentiates Microstrategy from other OLAP vendors is its strong belief in the commercial opportunities of selling information resulting from OLAP queries, and in the importance of personalised information broadcasting technology in distributing information. Microstrategy has restructured its core business to deliver business intelligence applications that allow companies to strengthen their relationships with customers and suppliers. The DSS Broadcaster component of the toolset is a major part of this strategy, and pushes Microstrategy’s established ROLAP technology into the information broadcasting business. Microstrategy has around 700 customers worldwide and is active in the retail, finance and telecoms sectors. Large customers include Air Canada, Bank of America, General Motors, Kmart, Hallmark, MCI Xerox and Vodafone. The need for a data warehouse means that many of Microstrategy’s accounts are extremely large. In the company’s experience, the cost of a total data warehouse solution may typically be in the region of $2.5 million. Microstrategy markets its software products and services mainly through a direct sales force, but is increasingly starting to use indirect sales channels. The company has more than 150 systems integrators, application development partners and platform partners using its tools. These include: Acxiom, Andersen Consulting, IBM, HNC Software, Intrepid Systems, NCR and Retek.

Customer support Support Microstrategy offers worldwide support at various levels according to the maintenance agreement in place. This includes 24×7 and full support account management. Support is provided through two major centres located in Washington DC, US and Slough, UK. Local support numbers are available for all supported countries. Training Microstrategy runs a variety of courses for its customers and partners, including a foundation and advanced course (one day each), a two-day data warehouse design course and a three-day installation and management course. The company also provides a partner programme aimed at systems integrators, VARs, OEMs, partners and distributors, comprising all the above training plus certification. Consultancy services Consultancy is available directly from Microstrategy, and from partners such as Andersen Consulting and Renaissance Worldwide.

Distribution US Microstrategy 8000 Towers Crescent Drive Vienna Virginia 22182 USA Tel: +1 703 848 8600 Fax: +1 703 848 8610 Europe Microstrategy St Martin’s Place 51 Slough Rd Slough Berkshire SL1 3UF UK Tel: +1 44 1753 826100 Fax: +1 44 1753 826101 Asia-Pacific Microstrategy 41 Dillon Street Paddington Sydney, NSW Australia Tel: +61 2 9360 0240 Fax: +61 2 9331 3542

http://www.strategy.com E-mail: [email protected]

Product evaluation

End-user functionality Summary

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End users can easily access the multidimensional model via a desktop or web interface. Flexible access via the desktop is facilitated by the ability to select a ‘high level’ or ‘analyst’ interface. In general, the end-user interface provides most of the features that OLAP users expect in a well designed GUI. DSS reports provide a range of sophisticated presentation features, including mapping capabilities. DSS Broadcaster also adds some very useful publishing capabilities for easy and flexible distribution, including the ability to dynamically define address lists. Finding and understanding the model Finding and loading a multidimensional model Reports are collected together in report folders and can also be stored in an agent. An agent is used to group logically similar reports. If an agent is double-clicked, all of its reports are executed and each one is displayed in a separate window. There are, however, no search facilities for the end user. Metadata for end users All objects, except fields (for example, reports, templates, filters and projects), can have a description attached to them. This is defined by designers in Object Manager, and is visible to end users in reports. DSS Agent can integrate with ETL products. This enables the end user (or developer) to view extraction and transformation metadata about the fields in the data warehouse that provided data for dimensions and measures. However, caching has to be disabled for this to work. End users simply right- click on a data element in a model to view additional metadata (such as the source of the data and the date or time that the data was last updated). Annotation by the end user It is not possible for the end user to add annotations. Using the model Basic OLAP functionality DSS Agent provides all the expected facilities to drill-up, down and across and to pivot the model. Nested crosstabs are supported. Users can also specify stoplightling and alert functions. Ease-of-use is increased by the option of three types of interface in DSS Agent: power user (with full functionality), DSS Analyst (customised) and high-level user (for running reports).

Changing the position of members in a dimension level It is not possible to reposition members within a dimension level. Visualising the drill-down hierarchies There is a visual representation of the dimension hierarchy in the component window, but there is no link to the report to show which level you are currently in. Drilling down to detailed data The default allows users to drill-down to the detailed data. Locking prevents users from viewing all the members via the component window. This is implemented when the mapping layer is defined in DSS Architect. Within a report, users can be prevented from retrieving all the data using ‘drill governing’, which restricts the data retrieved from a report (for example, to the top five or over a certain value). Range of front-end user tools Reports can be run in DSS Agent, a web browser, a customised EIS environment (developed with EIS Executive) or a customised application (developed with DSS Objects). Apart from exporting data to Microsoft Excel or Lotus 1-2-3, there is no direct integration with third-party OLAP clients. There is no support for Microsoft’s OLE DB for OLAP interface. Visualising the results Reports can be displayed in crosstabs, graphs or with the alerts highlighted. DSS Agent (and DSS Web) also support the visualisation of data in maps and organisational charts. It is possible to combine crosstabs and graphs in reports and create multiple report workbooks. Saving and sharing results Designing a report A range of sophisticated formatting and design options are provided: • wizards simplify the report building process • a number of grid formatting templates are also provided • a library of statistical & arithmetical and date & string functions is provided to define local calculations in reports. Publishing a report DSS Broadcaster gives an enormous range of publishing options for reports. They can be published to dynamically defined address lists, to a wide variety of delivery devices including PCs, mobile phones (that support alpha messaging), pagers, handheld devices (for example, Palm Pilots and Windows CE machines) and fax terminals. Publishing schedules can be based on time- or event-driven criteria.

Targeted distribution via e-mail E-mail is the main gateway out of DSS Broadcaster. An active report can be distributed via Lotus Notes and Microsoft Exchange Server through a toolbar button. Gateways are also provided to Microsoft Mail and cc:Mail. E-mails can support plain text, rich text and HTML content. Dynamic e-mail address lists are also supported. Subscribing to reports Users can subscribe to specific services and reports using DSS Broadcaster’s subscription templates. Custom subscription interfaces can also be built by writing to DSS Broadcaster’s COM API.

Building the business model Summary

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DSS Architect provides a clear and easy-to-use set of graphical tools to design and use multidimensional models. Much of the work that results in the multidimensional model is done upstream when designing the tables in the data warehouse and defining the mapping layer in DSS Architect. This makes the building of models easy, but limits the flexibility due to the high dependency on the data warehouse for the model-building process. For example, it is not possible to introduce user-defined levels in the dimensions. Nor is there any tool support for defining the time dimension – it is limited to what is available in the data warehouse. Basic design Design interface The mapping layer for the reports is defined in DSS Architect using an intuitive graphical interface. All aspects of the mapping process – establishing connectivity, selection of warehouse tables, identification of fact columns and the definition of dimensions and metrics – is simplified with point-and-click functionality. A report (which is a set of dimensions, measures and filters) can be generated from this using a Report Wizard. The design interface, in all cases, is simple and easy to use. Visualising the data source The tables in the source database are listed and the structure of each can be optionally displayed. A sample of data from the tables can be viewed using the component window. Universally available mapping layer There is partial support for this, because the project serves as a mapping layer. This stores the metadata that describes the relationship between the logical model and the database. However, this layer is not universally available.

Prompts for metadata The developer is not prompted to add metadata when designing reports or creating the semantic layer. Building the dimensions Selecting columns for the dimensions In DSS Architect the columns for the dimensions are easily selected using point-and-click. The tool provides development hints by evaluating definitions in the model and suggesting appropriate tables and columns from the data warehouse. Selecting the members shown in a dimension level Row selection is achieved by defining a filter in DSS Agent. Although the Filter Editor is defined in the manual as an ‘advanced topic’, it is a well designed and easy-to-use point-and-click facility. Defining a dimension hierarchy Parent/child relationships between attributes are defined in DSS Architect. It is possible to have alternative drill-down paths, but there is no support for unbalanced hierarchies or for inserting user-defined levels. Time dimension The tool does not provide a time dimension. The assumption is that the data will be organised into appropriate summary tables in the data warehouse. The tool can only use these tables as they are and cannot generate new time categories. Thus, a time dimension can be built, but all levels must be mapped on to tables in the database. Annotating the dimensions In DSS Architect, the developer provides a single term to describe the dimension. There is no support for a more detailed description. Default level of a dimension hierarchy The definition of a report includes a default level within the hierarchy. Defining the measures Calculated measures New measures are defined using a calculator-like interface, which provides arithmetic operators and the SQL aggregate functions of ‘sum’, ‘average’, ‘maximum’, ‘minimum’ and ‘count’. The designer can also use functions provided by the database being accessed. However, they need to know what these functions are because no drop-down list is available. Support for multiple measures with a set of dimensions There is support for multiple measures with a set of dimensions. Multiple designers Multiple designers Only users with the appropriate security can edit objects, but within this group there is no mechanism to support multiple designers simultaneously working on objects.

Support for versioning There is no support for versioning. Other ‘building the business model’ features There are search facilities that use Object Manager (one of the interfaces in DSS Objects) to aid developers in finding components. DSS Architect provides error checking that alerts developers to specification errors, such as logical violations in the model and assignment errors.

Advanced analytical power Summary

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Standard ranking and sorting functions are particularly well supported by the DSS Server, but there is limited support for specialised analytics above that provided by the data warehouse. This is primarily a consequence of its ROLAP architecture, which exploits the analytical functions created and stored in the data warehouse. Within DSS Agent there is some scope for extending the range of analytics by combining basic facts from the data warehouse or embedding conditions inside the available metrics. DSS Web provides enhanced analytical capabilities through its integration with Excel. Datamining is not available. Third-party tool integration DSS Web (but not DSS Agent) can integrate with Microsoft’s Excel function libraries: web reports can include any Excel function and can easily be applied to data. There is no integration with other third-party analytical tools. Defining specialised models Ranking and sorting Measures can have qualifiers attached to them to limit the items returned. A measure can be qualified by value (‘<’, ‘=’, ‘>=’), rank or per cent. Ranking can be the top or bottom n, or n%. Per cent is used to define the percentage of the values being ranked (for example, 80:20 reporting). Quartile analysis is also supported. Data in reports can be sorted on one or multiple keys, and can be specified as ascending or descending. Mathematical methods Support is provided for standard arithmetic (for example, minimum, maximum, average and absolute totals) and algebraic calculation functions. If advanced mathematical functions are accessible in the database, they can be used. For example, if the database being accessed is Red Brick, the additional RISQL functions, such as rank, tertile and cumulative, are also available.

Financial functions There are no specialised financial functions provided by DSS Agent. Some support is available in DSS Web (via Excel). Statistical functions This is not provided by DSS Agent. It is only supported in DSS Web through its integration with Excel. Trend analysis This is not provided by DSS Agent. However, DSS Web users can use Excel for simple trend analysis. Simple regression This is not provided by DSS Agent. However, DSS Web users can use Excel’s linear regression techniques. Time-series forecasting There are no specialised time-series forecasting functions. User-definable extensions There is no scripting language available to extend the range of functions. However, there is considerable flexibility to build and re-use new measures in three ways: • using the function builder interface allows users to create their own functions and apply them to a data series for display in report writer mode • combining basic facts from the data warehouse in reports to build more advanced metrics (for example, combine the ‘inventory’ and ‘sales figures’ to calculate new measures such as ‘turnover’ and ‘sell-through’) • embedding conditions inside calculations, and then adding qualifications at runtime or building ‘self-adjusting’ measures that can be re-used across the enterprise and multiple reports. Writeback for ‘what if?’ analysis There is no support for writeback analysis. Incorporating non-numerical data There is no support for the analysis of non-numerical data. Datamining There is no support for datamining.

Web support Summary

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Microstrategy offers good web support via two products: DSS Web and DSS Broadcaster. DSS Web has several modes: it provides a development environment, acts as a server enabling thin client creation and access to sophisticated reports, and provides administrative control of web usage. It also integrates with the Excel function library to provide greater analytical capabilities than the DSS Agent client. DSS Broadcaster is designed to distribute reports to users. A unique feature is the ability to distribute these to a range of different devices simply by using a pull-down menu. End-user functionality via the Web Functionality of web access to explore models DSS Web uses both ActiveX and Java within the browser to provide a similar interface to that of DSS Agent. Web access is similar to desktop access, except users cannot move or pivot dimensions. A range of report-design features are supported, and asynchronous query handling and status monitoring facilities for reports are provided via the web client. DSS Web provides tight integration with Excel to offer a range of statistical functions. Supports registered and unregistered web access DSS Web employs a flexible multi-tiered approach to security. Security is flexible enough to be configured to support unregistered users. Range of users supported by the web interface The development options offered by DSS Web enable suitable web interfaces to be created for power users and occasional users. Creating models via the Web Editing the mapping layer Editing the mapping layer cannot be done via a browser. It has to be done using DSS Architect on the desktop. Building and editing models The underlying model definitions cannot be changed. However, the web API allows applications to be built using a Report Wizard Java applet, which enables new reports to be created from existing templates and filters, or by creating a new template.

Distributing via the Internet and the Web Generate HTML and Java DSS Agent does not allow reports in HTML format to be saved for loading on to web pages. However, DSS Web does integrate an HTML editor to provide support for writing HTML-based reports. There is no support to generate Java code. Corporately organised distribution via the Internet DSS Broadcaster is specifically geared to dynamically distribute reports over the Internet via e-mail. DSS Broadcaster supports plain text, RTF and HTML e-mail types. Customisable stylesheet templates are also provided for the enhanced presentation and layout of data in e-mails. Reports can also be posted to a web server to create personalised web pages for access. DSS Broadcaster supports Microsoft’s web casting and CDF standards. Include URLs in a report The applications generated using DSS Web are HTML-based, so they can include URL references. E-mail sent using DSS Broadcaster can also contain URLs. Distribution of web server processing There is no integration with middleware to support distributed processing.

Management Summary

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Management of models and users is supported in several of the components of the toolkit and is easy to use. As expected from a ROLAP tool, there is comprehensive support for query management and monitoring the usage of the system. However, some management features, notably user security, are largely delegated to the data warehouse and are not provided by the toolset. In ROLAP, persistent models are a pragmatic convenience rather than a fundamental aspect of the system. However, they still require management, and the product would be enhanced by more sophisticated cache refresh functions and more information on refresh schedules. Management of models Separate management interface There are two management interfaces: • DSS Server – for realtime management • DSS Administrator – for monitoring and tuning the system. Both provide graphical interfaces and most administration operations are supported by point-and-click and drag-and-drop.

Security of models The Microstrategy tools are based on the assumption that most of the security functions are provided by the database used for the data warehouse. Within the toolset there is limited support provided by the log- on ID of the creator of an object determining whether it can be seen by a group or an individual. The rationale of this facility is to share, rather than create, a secure environment. Query monitoring DSS Administrator’s Warehouse Monitor provides an easy-to-use interface for a range of useful monitoring information, including a list of most frequently used reports, a breakdown of web and non-web usage, user statistics (such as resource consumption and data volume per user) and information for load balancing. The information obtained includes: • table-hit frequency – to identify aggregate table utilisation and partitioned table utilisation • individual query statistics – such as total query execution time, SQL generation time, queue time and SQL execution times. Management of data How persistent data is stored (not scored) The Microstrategy toolkit does not automatically create persistent versions of all models, but there is an option to cache data. In this case, a cache is not a volatile store but a file that can be saved either locally or on the central server. The two advantages of this are that it: • reduces the processing load on the data warehouse • speeds up retrieval time. Scheduling of loads and updates The default schedule for refreshing the cache can be specified in DSS Agent, DSS Web and DSS Broadcaster. The schedule includes details about where the cache is to be stored and its duration. The units used to define the expiry date are days, weeks, months and years. DSS reports can have individual refresh schedules. Event-driven scheduling Administrators can create a schedule that refreshes the cache after certain events; for example, a data warehouse load, a general ledger update or a critical threshold within the data warehouse. Failed loads and updates There is no support to inform the administrator that a refresh schedule has failed. Distribution of stored data Caches can be optionally stored on the client or server. A mixture of caching options is supported.

Sparsity (only for persistent models) This is not applicable. Methods for managing size This is a minor issue, as the administrator only has to manage the size of the cached data. This data is subject to the size restrictions on reports defined in DSS Server. This enables the administrator to limit the number of rows in the report and/or the scheduled job time. Cached data is also subject to compression. In-memory caching options This is not applicable, because there is no tool support for in-memory caching. Informing the user when stored data was last uploaded When a cached report is used, the status bar displays the date and time that the data was last updated. Management of users Multiple users of models with write facilities This is not applicable: write facilities are not provided. User security profiles There is limited support to define security profiles for individuals or groups from within the tool. Users can, optionally, be allocated to groups by adding lines to the configuration file, and this will define the objects (that is, reports, metrics, templates and filters) that they can see. This is, however, more of a convenience than a security feature. Most of the security functions are provided by the database. Query governance DSS Agent generates a report cost estimate, which can be viewed by the user, and is also passed to DSS Server for governing and prioritising queries. A time warning can be attached to a report that generates a message box if a threshold is exceeded. Within DSS Server, the emphasis is on governing the overall set of query processes rather than particular users. These include size limits on number of rows in a results set, time-outs for long running queries and the maximum concurrent jobs per user. Restricting queries to specified times There is no facility to limit queries to particular times of the day or week. Management of metadata Controlling visibility of the ‘road map’ There is some control over the visibility of the metadata because, in the first instance, objects can only be seen by their creator, and then Object Manager is used to share the visibility. If the designer logs in as an individual user rather than a system user, this will limit the original visibility.

Adaptability Summary

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Adaptability in DSS Suite is generally a case of being able to add new dimensions and measures to a model and re-use these definitions. DSS Agent also provides access to metadata repositories from third-party data warehousing tools. There are a lack of facilities for keeping the data sources, multidimensional business models and the metadata all synchronised. Changes to the structure of the data warehouse therefore remain transparent to DSS Suite, but analysing the impact of changes made to dimensions and measures in a model on associated reports is supported. There is no possibility of adapting the ROLAP architecture to support MOLAP and hybrid architectures. Change in business requirements Adding new dimensions to a model A new dimension can be added to the metadata definitions using DSS Architect and can then be used in reports. There are no management facilities to track the changes. Re-use of dimension definition As the dimension definitions are stored as metadata they can be re-used. Adding new measures to a model New measures are added via DSS Agent. A wizard is provided to help users assemble calculations on-the-fly and add them to a model. Re-use of calculated measure definition Measures are named and described and can be re-used. All measure definitions are stored centrally. Changing the architecture to reflect business needs The Microstrategy tools are strictly ROLAP-based. There is no possibility of changing the architecture to MOLAP or hybrid modes to support changing business needs. Changes to data sources Keeping the data source and model schema synchronised There is no support to inform the user when a report is opened if some of the data sources referenced by the model are no longer available. Automatic updating of members in a dimension ROLAP systems work directly against the source data, so any new values in an attribute (for example, new product ID) are automatically included in a report.

There is no direct support to prevent new members being automatically incorporated, but an equivalent effect could be produced by defining a timestamp filter that does not admit members after a certain date. Metadata Synchronising model and model metadata The metadata is generated by DSS Architect (and stored as a project) and the multidimensional business models (known as reports) are created from this using DSS Agent. If some of the fields used in a report are removed from a project, the report will run – but without the relevant fields. The user is not informed of what has been lost. Impact analysis There is no support to inform the administrator of the effect that changing the structure of the data warehouse will have on reports. However, Object Manager lets administrators assess the impact of changes to a metric definition on reports. Metadata audit trail (technical and end users) There is no support for a metadata audit trail. Access to upstream metadata Integration with ETL products enables the developer to view extraction and transformation metadata about the columns in the data warehouse that provided data for the measures. However, caching has to be disabled for this to work. Metadata can be accessed from a range of ETL tools, including Informatica, Acta, Ardent, Constellar, D2K, ETI, Prism, Relational Matters and Systemfabrik.

Performance tunability Summary

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In a ROLAP model such as Microstrategy’s, the main performance issues are how to minimise access time, particularly with complex queries. This is achieved by providing options to cache data and use multipass SQL, enabling queries of greater complexity to be handled. DBAs can also change the aggregate table or structure, or create table partitions to improve query response times. The Microstrategy approach is for all the SQL processing is done on the database server so that minimum data is retrieved, which reduces the network load, but increases the load on the database server. A typical Microstrategy implementation also includes numerous aggregate tables to boost query performance (but which slow deployment and increase the management burden). Load balancing across multiple DSS Servers is not supported.

ROLAP Multipass SQL DSS Server uses multipass SQL. Options for SQL processing The processing of SQL queries is always carried out on the source database (usually a data warehouse). DSS Server can take full advantage of database- specific optimisation techniques such as hash and start joins, SQL extensions and predicate clause optimisation. Speeding up end-user data access Non-volatile caches are used. The refresh schedule for these is defined in DSS Agent. The information provided by Warehouse Monitor can be used to define an aggregate strategy in the data warehouse that would speed up user access. The tool depends on database features (such as table partitioning) to help speed-up data access. Aggregate navigator DSS Server is aggregate-aware. It automatically directs queries in order to reference smaller, precalulated aggregate tables, thus improving performance. For queries that require an aggregate table that does not exist, DSS Server performs a ‘tree-walk’ to reference the most appropriate table from which to calculate the aggregation. MOLAP DSS Suite does not support a MOLAP architecture. Support for multiple users The main limit on the number of users that can be simultaneously supported is the ability of the data warehouse to cope with the number of temporary tables generated when the SQL is processed. The number of users that can be supported is therefore related to the complexity of the queries and capacity of the data warehouse. Processing Use of native SQL to speed up data extraction DSS Server uses 16- and 32-bit ODBC connectivity to connect to leading relational databases. The queries use the pass-through facility of ODBC to use native SQL to retrieve data. Distribution of processing The processing of the SQL queries is always carried out on the source database (typically the data warehouse). The administrator can use DSS Server in realtime to tune the number of processes simultaneously running on the data warehouse. Dynamic database thread allocation and management is supported to prevent overloading of the data warehouse.

However, there is no automatic load balancing between the DSS Servers because each functions independently. However, using the Warehouse Moni- tor, the administrator can see which servers are overloaded and move projects accordingly. SMP support SMP support is provided by the database.

Customisation Summary

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DSS Suite provides three development environments: • an API to support application development in OLE-enabled application development language • development for the Web using DSS Web • the production of custom EIS interfaces using DSS Executive. None of these environments provide support for OLAP-aware components (such as crosstab and graphical objects), which limits the type of application that can be easily produced. Customisation Option of using a restricted interface This is well supported by a selection of interfaces in DSS Agent. When DSS Agent is opened, users can elect whether to work in EIS or DSS mode. The range of interfaces supports simple, intermediate, advanced and custom functionality. Ease of producing EIS-style reports The production of EIS reports is supported by DSS Executive. An EIS is a guided tour through a project with limited support for analytical exploration. Using point-and-click, the important objects (buttons, images, labels, grids, graphs and textboxes) are positioned and their properties specified. This feature is well supported in an easy-to-use, drag-and-drop environment. Applications Simple web applications Web applications are developed via DSS Web, using HTML authoring tools, JavaScript or Visual Basic Script. Development environment There is no provision of a development environment specialised for OLAP applications and providing ‘OLAP-aware’ components. DSS Executive only supports standard EIS objects and visual layout functions that can be used to build briefing books and simple EIS front ends.

Use of third-party development tools DSS Objects provides an API to support application development in OLE- enabled application development languages such as Visual Basic, Visual C++, Visual Basic Applications and Delphi. Other support for customisation The DSS tools support English, Spanish, German, French, Italian and double-byte Korean language versions.

Deployment

Platforms Client Clients run on Windows 3.x, 95, 98, NT and OS/2. Web access requires Microsoft Internet Explorer or Netscape Navigator browsers. Server The server-based tools (DSS Server, DSS Web and DSS Administrator) run only on Windows NT.

Data access DSS Servers uses ODBC to connect to the following relational databases: Oracle, Informix, Teradata, Tandem, DB2, Sybase, Microsoft SQL Server, Red Brick, ADABAS D and Microsoft Access. The queries use the pass- through facility of ODBC to use native SQL to retrieve data. There is no direct support for accessing specialised data sources such as SAP BW and ERP operational data. This is regarded as a responsibility of the database rather than the OLAP tool and is achieved mainly through the use of ETL partners such as Prism, Informatica, ETI, Acta and Systemfabrik.

Standards DSS Objects offers an OLE-based API for software development using a COM-compliant language.

Published benchmarks Microstrategy has not participated in any external benchmarking tests. However, it has conducted its own internal ‘stress test’ benchmarks.

Price structure Server pricing Pricing for server components, including support for up to 50 users, is as follows: • DSS Server – $21,125 • DSS Web – $11,375 • DSS Broadcaster – $11,375. Pricing for all servers increases incrementally with user-number categories (for example, the next category is 51–200 users).

Development tools The development tools are priced on a per-user model: • DSS Architect (one user) – $9,750 • DSS Executive (one user) – $9,750 • DSS Administrator (one user) – $19,500 • Development Bundle (two users) – $45,500. Interfaces The interface components are priced on a per-user basis, but discounted rates can apply according to total deal volume: • DSS Agent (one user) – $1,670 • DSS Objects (one user) – $1,335 • DSS Web PE (one user) – $1,335 • DSS Web SE (one user) – $830 • DSS Broadcaster (one user) – $495.

Summary

At a glance ...... 2 Terminology of the vendor ...... 3 Ovum’s verdict...... 4 Product overview ...... 5 Future enhancements ...... 11

Commercial background

Company background ...... 12 Distribution ...... 13

Product evaluation

End-user functionality ...... 14 Building the business model...... 15 Advanced analytical power ...... 17 Web support ...... 18 Management ...... 19 Adaptability ...... 21 Performance tunability...... 23 Customisation ...... 24

Deployment

Platforms ...... 26 Data access ...... 26 Standards ...... 26 Published benchmarks ...... 26 Price structure ...... 26 At a glance

Developer Oracle, Redwood Shores, CA, USA Versions evaluated Oracle Express Server, version 6.2; Oracle Objects and Express Analyzer, version 2.2; Oracle Web Publisher, version 2.0 Key facts • A MDDB with an integrated development environment. Can also be configured for ROLAP • Web- and Windows-based clients and Unix- and NT-based server • Oracle also produces sales and financial OLAP applications Strengths • A mature MDDB engine with a range of financial and analytical functions • An easy-to-use – but extensible – development environment, which can be used to create EIS-style and industrial strength applications • A complete package, including an end-user tool that can support power users and has a web publishing component Points to watch • Requires 4GL coding skills to supplement the GUI environment; for instance, to specify security levels for database objects • Limited support for the publication and distribution of reports • Not yet seamlessly integrated with other Oracle business intelligence products Ratings

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End user functionality

Building the business model

Advanced analytical power

Web support

Management

Adaptability

Performance tunability

Customisation Terminology of the vendor Formulae Formulae are calculated measures, but they are created dynamically and not stored. SPL The stored procedure language, also known as Express language, used in Express Server. It is a 4GL-like language used to define analytical functions and some Express operations. SNAPI Structured n-dimensional API. It is a C language interface for accessing Oracle Express Server and Personal Express, and used when developing applications in C/C++ or languages that support calls to C functions in DLLs such as Visual Basic. It is used by front-end tool vendors such as Business Objects and Cognos to access Express data. Variable The Express term for a measure. A variable is cross-referenced only against selected dimensions. Ovum’s verdict

What we think Oracle Express Server and its associated components have notable strengths on several fronts. Its multidimensional database is a mature product that combines GUI ease-of-use with the fully featured Express language giving procedural control. The application tools, Oracle Express Objects and Oracle Express Analyzer, enable applications and reports to be quickly prototyped and more fully developed if required. Underpinning all aspects is a wide range of analytical functions, particularly in the financial and forecasting area. A further strength is the possibility of changing the data storage architecture from MOLAP to ROLAP if the requirements change. There is good, but not yet seamless, integration with other Oracle business intelligence tools (such as Oracle Reports and Discoverer) and partnerships with data mining vendors if this type of analysis is required. Although generally Express is a GUI-based environment, it requires coding in some important aspects, such as the specification of user access controls. Express could be strengthened by the addition of more comprehensive publication and distribution facilities. Overall, however, the tool offers an excellent scalable and extensible database and development environment, with the support facilities of a major database player.

When to use Oracle Express should be on your shortlist if you: • are developing medium- to large-scale projects requiring customised applications of varying complexity • have complex analytical requirements where the ability to write new, complex functions is important • wish to enhance and further develop one of Oracle’s packaged applications (such as Financial Analyzer or Sales Analyzer). It is less suitable if you: • want an ‘out-of-the-box’ solution for end users • do not have access to the coding skills necessary to fully exploit the tools • want sophisticated publication and distribution support. Product overview

Components The main components of the Oracle Express Development toolkit are: • Oracle Express Server version 6.2 • Oracle Express Objects version 2.2 • Oracle Express Analyzer version 2.2 • Oracle Web Publisher version 2.0. Figure 1 shows whether the component runs on a client or server and its primary purpose. Oracle Express Server Express Server is at the heart of Oracle’s support for multidimensional analysis. It can function in three modes: • as a multidimensional store and engine • as a ROLAP engine accessing data in a star or snowflake schema in a data warehouse • in a hybrid (HOLAP) mode in which the summary details are held in the multidimensional store, and the detailed data retrieved as necessary from a relational database. Data is physically stored in Oracle Express in multi-cubes. Three kinds of dimensions can be defined: time, text and integer (which is used when unique names, such as for employees, cannot be guaranteed). Calculated measures (known as formulae) are defined in a Stored Procedure Language (SPL) and are usually calculated as required. Using SPL with the large collection of financial and statistical functions means that complex business models (including ‘what-if’ scenarios) can be created. When defining a model, the user has to make a decision as to whether the dimensions are sparse or not. When Oracle Express Server is configured as a ROLAP engine, it uses dynamic reach through SQL to access data in a relational database; the data is cached in the Express Server and then accessed in the usual way by end users.

Figure 1 Component details

Main purpose

Develop applications OLAP Develop web pages

Client Express Objects Express Analyzer

Server Express Server Web Publisher Oracle Express includes query monitoring, security and scheduling facilities. Express Server includes (at no extra cost) Oracle Web Server, Oracle Web Agent, Oracle Express Administrator and Oracle Batch Manager. Oracle Express Objects Oracle Express Objects is a visual development environment that can be used to develop applications or to enhance Oracle’s packaged financial and sales applications. It uses Express Basic to give programmatic control. It is similar in style to Visual Basic, with the addition of multidimensional objects such as table objects and graph objects and dimensionally-aware list boxes. The multidimensional aspects are defined through a database browser. It is an object-oriented environment offering inheritance and polymorphism. The development environment can be enhanced by the creation of new objects, which can then be added to the toolbox. An application is developed as a series of pages. Oracle Express Analyzer Analyzer can be used to carry out ad hoc exploration of the database models, to access applications written in Express Objects or as a visual development environment to create a briefing report consisting of customised pages containing tables and graphs. The reports can contain OLE objects, and include URLs giving web access, as well as all the usual drill-up/down, pivot and ranking features. If required, any of the properties (such as the dimension bar or drill-down) can be turned off to provide a less complex report with reduced functionality. When published as a briefing, the user navigates the report using VCR-like buttons to move from page to page. Briefings can also be exported in web or Excel format. Oracle Web Publisher Oracle Web Publisher is for developing web pages that can then be loaded onto a web server. It is an easy-to-use wizard-driven tool for placing tables and graphs onto web pages and adding text. The tool enables the developer to select whether Java code or HTML will be generated. (The HTML is offered because Java requires a 32-bit operating system and therefore cannot run on PCs with Windows 3.x.) The look-and-feel of the resulting pages is more elegant if the Java option is selected. Other Oracle business intelligence products The main Oracle tools supporting business intelligence are Oracle Reports, Oracle Discoverer and Oracle Express. In addition, the company provides two packaged applications, Oracle Financial Analyzer and Oracle Sales Analyzer. These packaged applications are based on Oracle Express and the components described in this evaluation can be used to enhance and develop the packaged applications. Oracle provides OLAP functionality in Oracle Discoverer and Oracle Ex- press. In this evaluation we have focused on Oracle Express because it is a more powerful environment, offering a richer set of analytical functions and development tools. Oracle Discoverer has ‘out-of-the-box’ ease of use, whereas Oracle Express is more of a toolkit.

Architectural options Oracle Express Server can be configured to support multiple architectures. Full mid-tier architecture The ‘best fit’ configuration is with Oracle Express as an MDDB server and engine, the full mid-tier option. The main client options are listed below. Using Oracle tools The three most common end-user tools for Oracle Express Server are: • Express Analyzer • Microsoft Excel, using an add-in • a web browser. Using Oracle’s end-user tool, Oracle Analyzer, users can carry out ad hoc exploration of the database models, write and run a briefing report, or run an application written using Oracle Express Objects. With an Excel add-in, users can view and explore models. A further means of access is through a web browser if pages have been prepared in Web Pub- lisher or exported from one of the other Express tools that have been put on the web server. Via the API The Structured n-dimensional Application Programming Interface (SNAPI) enables client applications running in a Windows environment to retrieve and use data stored in Oracle Express Server and Personal Express databases. Approximately 12 user vendors, the most notable being Business Objects, Cognos and SPSS, have used this interface to enable their tools to work with Express data. Light mid-tier option Using Relational Access Manager, Express can directly access data in an RDBMS and store it in a cache in Express, where it is available for analysis. In this configuration, it is comparable to a ROLAP tool, except that the administrator has some additional work to do in defining the handling of calculated measures. The choice of front end-user tools is the same as with the full mid-tier option. Desktop and mobile architectures The desktop and mobile user configurations require the use of Personal Express, a small footprint version of Oracle Express Server. Using Oracle Express Supports MOLAP, ROLAP and HOLAP Oracle Express can operate in MOLAP mode (with the data for the model stored in the MDDB), in ROLAP mode (with the data stored in a relational database system) or in HOLAP mode (with some data in the MDDB and some in a relational database). However, there is a considerable difference between the ease of use in the three different modes. The use with best fit is MOLAP, and when working in this mode the designer implements most of the features using a GUI. In ROLAP mode, a more technical skill set is required, because the administrator has to take special steps to enable the end-user tools to use measures that include qualified date references, time series functions or ampersands (alternatively, the administrator should tell the users to avoid using the functions and syntax that the Express client products cannot parse). In HOLAP mode, the most usual design is to store the summary information in the MDDB and then define the dimensions so that when more detailed information is required, the server generates dynamic reach through SQL to retrieve the data. Thus, from the users’ point of view, the source of data is irrelevant. As with ROLAP, some technical skill is required to design the model to run in this way. The administrator’s tasks to enable the retrieval of data from a RDBMS to supplement the data stored in the MDDB include: • preparing the code that connects to the SQL database • using the programs provided to make the connection • implementing the calculated measures in the Express database so they can be used for SQL reach-through. As this list suggests, this is not a set of point-and-click exercises. Provides an extensible environment When building the business model, most operations can be done at a basic level using a GUI environment and then extended using the stored procedure language. Wizard support to build a business model based on data sources There are several ways of using Oracle Express Server to build the business model. It can be used in a ‘top down’ manner, in which the business model is first constructed and then the data sources are mapped onto it. Alterna- tively, a simple business model based on data sources can be built using the database wizard. A sample of data can be seen. The developer can specify various options on the data to be loaded, including splitting fields, adding or deleting prefixes and suffixes, specifying values where none are available in the source data and replacing values. When dimensions are defined they are designated as sparse (or not) and hierarchies are created. Three ways of writing web applications The Oracle Express development tools enable developers to build web applications in three ways: • using Web Publisher. These web applications enable users to interact with, but not create, models. Web Publisher is an application developed using Express Objects and accesses Express Server via the CGI provided by Web Agent • using any third-party authoring tool and using Express HTML tags to gain access to the Express Server via the CGI provided by Web Agent • using the Express language to build a data driven web application. This requires substantial coding skills. A wide range of functions can be used to define the calculated measures A rich set of functions is available for inclusion in the calculated expression, primarily supporting financial and time series operations. They are defined by typing in an expression in Express Basic rather than using a calculator- type interface. This is shown in Figure 2. Further functions can be defined in the stored procedure language.

Figure 2 Function definition

In Oracle Express, measures have a data type of Boolean, date, decimal, ID, integer, shortDecimal, shortInteger or text. In Oracle Express, the calculated measures are not usually stored, but produced as required to reduce the size of the database. If they are particularly complex and/or used frequently, then they can be pre-calculated and stored. Support for incremental roll-ups The roll-up wizard is used to define the scheduling of the calculation of aggregates. It supports incremental roll-ups. The Selector tool is used to select members for roll-ups in the Administrator tool. It is also used in the end-user tool to specify which members should be included in tables and graphs. This is shown in Figure 3. Support for write back ‘What-if’ models can be built using the write-back facility. Measures are defined that depend on other measures. For example, profits depend on sales figures. Sales figures, like all components of Express, are objects with properties. To develop a ‘what-if’ model, the ‘cell entry’ property of the sales figures is changed to allow editing. The user can then enter a different value in that cell, and click a button to write this value back to the model, which then re-calculates the profit figure. This adjustment to the properties of a cell could also be done in the end-user tool.

Figure 3 Roll-up wizard

Future enhancements Oracle believe that the two main issues that will dominate the decision support tools marketplace over the next few years are the need for a single integrated framework for delivering business intelligence and the need to extend DSS tools to a much wider community of users across the enterprise. Oracle’s main development efforts are therefore focused on two important areas: • the further integration of its family of decision support products • the further development of these tools under Oracle’s Network Computing Architecture. NCA represents Oracle’s implementation of an open Internet standards model.

DSS family integration The current focus is on three principal areas: • common Java objects • extended integration with Oracle Server and data warehousing initiatives • implementation of common warehouse metadata standards.

Java objects Oracle’s integration strategy is focused on Internet computing, and the company sees open and portable components as critical to this approach. Oracle has begun by re-engineering the decision support clients into a collection of objects in the form of JavaBeans.

Data warehousing A major Oracle goal is to provide an environment to cover all phases of warehouse definition and operation, including integrated access tools. Initiatives include further data warehousing support in Oracle 8.x and closer integration with Oracle Express Server.

Metadata standard Oracle has been participating in an industry initiative to provide a common warehouse metadata standard. The company intends to use this in all future developments, thus integrating Oracle’s decision support tools at the metadata level.

DSS Over the Web Oracle intends to develop itsDSS Family in line with its Network Computing Architecture. Discoverer, Express client tools and Express applications will be implemented as Application Servers. These will support open Internet standards (that is, CORBA/IIOP), allowing the different components and tiers to interact, and ensuring that the solution is platform- and operating system-independent. Commercial background

Company background History and commercial Oracle was founded in 1977 and in 1979 brought the first SQL-based commercial relational database system to market. The company has grown year on year since its inception. In the early 1990s, it appeared to be faltering, but made its comeback under the direction of Ray Lane, who was promoted as Oracle’s president of worldwide operations in October 1993 and is now the chief operating officer. Revenues grew by a factor of four between 1991 and 1996, while income before tax expanded almost tenfold between 1992 and 1996. In the fiscal year 1997, revenue grew 35% to $5.7 billion, and in the year ending May 1998 the revenues were $7.1 billion, up 17% on 1997 and profits for the year were $955 million compared to $845 million in 1997. A breakdown of 1998 results points towards the main growth being in the services arm, Oracle Consulting, which comprises half Oracle’s business. On the software side, database software did better than applications. Express was originally owned and developed by Information Resources (IRI). In 1985, IRI acquired Management Decision Systems, a consulting company that had developed the first commercially available multidimensional database, Express, which was first released in 1972. In 1995, Oracle purchased the Express technology from IRI Development still continues in IRI’s original base at Waltham, MA. At that time, approximately 600 of IRI’s 900 staff joined Oracle. Ovum estimates that Oracle’s annual revenue from Oracle Express product suite licences (including packaged applications) is in the region of $180 million, with services and support bringing the total revenue in this area to $250 million. Character and direction Oracle aims to produce a spectrum of business intelligence tools covering the range of user requirements. The company is agnostic about whether OLAP is used in an independent datamart infrastructure, within a dependent datamart data warehousing framework or embedded in operational applications. The set of tools on offer covers all the architectural configurations and MOLAP, ROLAP and HOLAP options. The only visible gap is a data mining tool, and the company has no stated plans to acquire data mining technology. The Oracle Express Server, considered in this evaluation, is the MDDB component of the flexible architectures the company aims to support. The philosophy behind this particular product is that users need a range of analytical features that can be extended, and a development environment tailored for multidimensional analysis. If the purchaser is looking for a simpler environment, the company claims to meet these needs with Oracle Discoverer. The only part of the company philosophy that lacks certainty is how to handle the rapidly growing acceptance of Microsoft’s OLE DB for OLAP standard. While not ruling adoption of this out, Oracle is still putting its weight behind the OLAP Council’s MDAPI standard. Approximately 60% of Oracle Express sales are through its direct channels and the rest through indirect resellers, ISVs and SIs. Direct sales are generally to major accounts with an annual turnover in excess of $200 million. It is anticipated that indirect sales will grow to around 60% in the next few years.

Customer support Support Support is not included in the package, but offered as an additional service. Support programs include telephone support, around-the-clock coverage, on- site assistance, dedicated support account managers and priority call handling. Support is payable annually and is charged in line with software licensing (that is, per named or concurrent user). Prices are available upon request. Training Oracle Education has 217 education centres in 63 countries, offering a variety of courses. Consultancy services Consultancy is not packaged with the product. Service support is available from Oracle Partners as well as Oracle Consulting.

Distribution US Oracle 500 Oracle Parkway Redwood Shores CA 94065 USA Tel: +1 415 506 7000 Fax: +1 415 506 7200 Europe, Middle East and Africa Rijnzathe 6 NL 3453 PV De Meern The Netherlands Tel: +31 30 669 4211 Fax: +31 30 666 5603 Asia-Pacific 5 Tamasek Boulevard #15-03 Suntec City Tower Singapore 038985 Tel: +65 337 3797 Fax: +65 337 6109 http://www.oracle.com Product evaluation

End-user functionality Summary

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The end-user functionality depends on the tool used to access the model or application. Here, we mainly assess the functionality offered by Analyzer when accessing a multidimensional business model. Although the model is easy to use and offers the expected range of drill-down and pivot features, the product is prevented from getting a higher score for this criteria by the lack of support for cataloguing, publishing and distributing models and reports. The tool could benefit from absorbing some of the features of Oracle Reports. (There is no integration between the two tools.) Finding and understanding the model Finding and loading a multidimensional model There is no tool support for cataloguing models. Metadata for end users Most objects, such as dimension fields and functions, have a description property that can be viewed in Express Analyzer. Using the Express language, additional properties can be added to hold additional metadata. Annotation by the end user The end user cannot annotate the model. Using the model Basic OLAP functionality The expected functionality, such as drilling, pivoting and defining alerts, is available using point-and-click. Changing the position of members in a dimension level The desktop end-user tools use drag-and-drop, and in the web browser this can be done using the Selector tool. Visualising the drill-down hierarchies The cursor shape tells the user whether they can drill-down. Hierarchies can be viewed using the Selector’s List tool. Drilling down to detailed data If the model has been defined so that the summary details are in the multidimensional database and the detailed data is retrieved as necessary from relational tables (‘hybrid mode’), then the user can drill-down to detailed data. Range of front-end user tools The ready-to-use front-end tools are Oracle Analyzer, Microsoft Excel with an add-in, and a web browser. Vendors such as Cognos, Business Objects and SPSS have used the open API to develop interfaces to access Express databases. Visualising the results Tables and graphs can be seen in the same window. Map information can be incorporated using OCXs available from Map Info. Saving and sharing results Designing a report Reports can include text and OLE objects. Publishing a report There is no direct support in Analyzer for the publishing and distribution of reports. Targeted distribution via e-mail There is no direct support within Analyzer to e-mail a report. This functionality could, however, be implemented using Oracle Express Objects. Subscribing to reports There is no mechanism for seeing available reports and subscribing to them.

Building the business model Summary

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The tool provides a flexible and powerful interface to define the business model, and prototypes can be quickly built using the database wizard. It offers a full range of features for building the multidimensional business model. The main ways in which the tool could be enhanced are through more structured support for the collection of metadata (to collect richer information) and by the provision of version control. Basic design Design interface The business model is defined using dialogue boxes and point-and-click. Visualising the data source Source data can be seen. Universally available mapping layer There is no direct support for a universally available mapping layer. Prompts for metadata The developer is prompted for short names (for graphs) and a longer, more descriptive, term for objects such as dimension levels and measures. Building the dimensions Selecting columns for the dimensions Columns are selected using point-and-click. Selecting the members shown in a dimension level Members are selected using point-and-click in the Selector tool. Defining a dimension hierarchy User-defined levels can be inserted. Unbalanced hierarchies and alternative drill-down paths can be defined. Time dimension If the type property of a dimension is set to time, the developer can use the range tool in the Selector to create dynamic time periods such as ‘year to date’ and ‘period ago’ measures. Annotating the dimensions Long and short annotations are supported. Default level of a dimension hierarchy The developer can define different default levels for different users. Defining the measures Calculated measures Logical and arithmetic operators and a wide range of functions can be used to define the calculated measures, but the definition has to be typed in. Support for multiple measures with a set of dimensions Multiple measures can be attached to a set of dimensions. Multiple designers Multiple designers The need for control applies both to the database and the development environment. In the database, only one user can have write access at any one time. Oracle supports multi-write access to its Financial Analyzer application. Within the development environment, designers work on project libraries. Two or more users work on project libraries, which are then merged and compiled into a single project. Support for versioning There is no support for versioning in Oracle Express. Other ‘building the business model’ features A prototype model can be quickly built using the Database Wizard. Advanced analytical power Summary

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Support for advanced analytics is one of Oracle Express’s strengths. It has a rich collection of ready-to-use functions supporting financial, time series and forecast analysis. Users, with appropriate permissions, can change the property of cells and write values back so that values in the model can be recalculated. If required, additional functions can be written in the stored procedure language. The product would be enhanced by the addition of data mining functionality. Third-party tool integration There is an Excel plug-in and an SPSS interface to enable users to access data in Express Server and use the analytical power of these tools. Defining specialised models Ranking and sorting This is supported in both Oracle Express and Oracle Analyzer. Mathematical models There is no direct support for mathematical functions such as matrix algebra, linear and quadratic equations, cubes and polynomials. Financial functions There is a wide range of financial functions. For example, for calculating the depreciation of assets, a fixed interest schedule and an internal rate of return. Statistical models There are a few statistical functions, such as multiple linear regression. Trend analysis There are functions for exponential smoothing, moving annual totals and moving monthly averages. Simple regression There is no support for regression algorithms. Time series forecasting There are lots of functions; for example, functions for comparing values in one time series with those in another, calculating the series of averages over time, and showing the percentage difference between the current value of a time series and the value of a previous one. Forecasts are supported with functions based on three methods: the straight-line trend, exponential growth and Holt-Winters extrapolation. User-definable extensions User-defined functions can be written using the Express language. There is excellent documentation and online help for this. Write back for ‘what-if’ analysis If the ‘edit’ property of a cell is set appropriately, users can write back values to the database and new values are calculated from this. Incorporating non-numerical data Functions are available to handle date and text as well as numerical data, but there are no functions to directly support the analysis of non-numerical data. Data mining Data mining is not supported.

Web support Summary

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The Oracle Express development tools enable users to access models using Web Agent (which is bundled with Express Server) and to publish pages using Web Publisher. As with most OLAP tools, there is support for web access but not for the creation of new models. The main focus of web support is the publication of a web page. There is no support for using the Internet for personalised distribution. End-user functionality via the Web Functionality of web access to explore models Web access to models created using Web Publisher is very similar to access via desktop tools. The most obvious differences are that pull-down menus replace drag-and-drop to change dimensions and some of the charting is less elegant if the web pages have been generated as HTML rather than Java. Supports both registered and unregistered web access A user has the same rights whether viewing via the desktop or a web browser. New users can log-on as a ‘guest’ with limited access rights. Range of users supported by the web interface Web Publisher can be used to produce simple. non-interactive reports without drill-down or a dimension bar (by turning off properties), as well as OLAP reports. Creating models via the Web Editing the mapping layer Not applicable because no mapping layer is used. Building and editing models There is no support to build models via the Web. Distributing via the Internet and the Web Generate HTML and Java Users with appropriate permissions can generate HTML pages from Analyzer. Corporately organised distribution via the Internet There is no support to define a dynamic address list for web distribution from the desktop tool, Oracle Analyzer. Include URLs in a report URLs can be included in reports generated using Web Publisher. Distribution of web server processing Oracle Express uses the conventional architecture of messages passing from the browser to the web application server and then, using CGI, to the Ex- press Web Agent and then to Express Server. Web Agent generates HTML pages and data for Java graphs in response to queries. There is thus no distribution of web server processing.

Management Summary

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The Oracle Express development tools offer developers a large number of options and configurations, and managing these is unlikely to be a simple task. While support for most of the important management functions is available, it assumes a DBA mind and skill set. Some tasks (for example, defining securities) require the administrator to write stored procedures in the Express language, rather than doing most of the work in a GUI environment with the occasional need to script. Thus most of the expected features are available, but the task of the administrator could be eased with wizard support and more intuitive interfaces. Management of models Separate management interface Many of the management tasks are done using Oracle Express Administra- tor. Scheduling, however, is organised using the Express Batch Manager. Security of models Security is defined using both the operating system and Oracle Express’s functions. It is defined using the Express language. Query monitoring Through the ‘query statistics’ option, the administrator can get information about performance statistics and about how the levels in the dimensions are being used and infer the need for summary tables. Management of data How persistent data is stored (not scored) When Express is used in MOLAP mode it is stored in a multidimensional database. When it is used in ROLAP mode it is stored in a cache in the MDDB. Scheduling of loads/updates Scheduling is supported through Express Batch Manager (a graphical utility to create, monitor and control batch processes) which comes with Express Server. Event-driven scheduling Event-driven scheduling, contingent on flags, the existence of a file or time- stamps, is supported by the administrator writing scripts in the Express language. Failed loads/updates If an upload fails there is error reporting, but there is no facility to specify that the schedule is automatically re-run. Distribution of stored data The database can be stored wherever the developer wishes. Sparsity (only for persistent models) Oracle Express has a method to handle the indexing of sparse measures, but does not offer wizard support for this. The simplest method of reducing indexing, if the measure is sparse in only one direction, is to specify this as the last dimension. This will reduce the size of the database because pages containing ‘n/a’ values are not saved. If sparsity is more randomly distributed the measure is defined as a ‘sparse variable’ along one or more of its dimensions. When a measure is defined as sparse in this way the system automatically creates a ‘composite’, which is the list of dimension value combinations that provides an index into one or more sparse variables. For efficiency, measures can share composites so that one combination of dimension values is used to access more than one measure. If the sparsity patterns are different, individual composites can be defined. By default, composites are created using BTREE algorithms but, optionally, a HASH method can be chosen. There is no system support to help the user see the benefits of the alternatives. Methods for managing size There are two main ways of managing the size of the database: • the multi-cube architecture reduces the size, as cubes are generally comprised of dimensions that are densely populated with regard to each other • by processing calculated measures as required. There is no wizard support for these administrative decisions. In memory caching options No options are available. Informing the user when stored data was last uploaded There is no direct support to inform the user about when the data being accessed was last uploaded. In ROLAP mode the data may be freshly retrieved, cached during the user session or cached more permanently. Using Analyzer for ad hoc queries, the user is not aware of the type of cache in use. Applications built using Express Objects can include information to inform the end user of the currency of the data. Management of users Multiple users of models with write facilities As described in Building the business model, only one user can write to the model at any one time. User security profiles User access is defined using the Express language. Query governance Query governance is primarily needed when the tool is used in ROLAP mode. There is no direct support. Restricting queries to specified times There is no support for this. Management of metadata Controlling visibility of the ‘road map’ The visibility of models and their metadata can be controlled using ‘permit’ commands in the Express language.

Adaptability Summary

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The most useful feature in Oracle Express supporting adaptability is the ability to change the data storage architecture. The system can be configured so that all data is held in the multidimensional database or, using Express Relational Access Administrator, data stored in a relational database can be used in the model. Finally, the system can be configured using Express Administrator, so that some (usually the summary) data is held in the MDDB and the rest is retrieved using SQL commands (HOLAP). Although the toolset offers a great deal of flexibility, it does not offer wizard support and requires a competent DBA to manage it. Adding new dimensions and measures is straightforward, but the tool lacks facilities to re-use and track these changes. The simplicity of the metadata, while in other contexts a negative feature, does simplify the process of adapting the model. Change in business requirements Adding new dimensions to a model Adding a new dimension to a model is straightforward, but there are no change management facilities to track these. Re-use of dimension definition It is not possible to name, save and thus re-use a dimension definition. Adding new measures to a model Adding a new measure to a model is straightforward, but there are no change management facilities to track these. Re-use of measure definition A measure can be saved as ‘custom measure’, with a description and either local or global availability. Changing the architecture to reflect business needs Oracle Express can operate in MOLAP mode (with the data for the model stored in the MDDB), in ROLAP mode (with the data stored in a relational database system) or in HOLAP mode (with some data in the MDDB and the rest in a relational database). Changes to data sources Keeping the data source and model schema synchronised There is no direct support for this when Oracle Express in being used in ROLAP mode, but a stored procedure could be written to check the data sources before the query was executed. Automatic updating of members in a dimension The way to lock a dimension so that new members are not automatically added is to specify in the data load definition that new data should be matched rather than appended. This is done on a per-dimension basis. Metadata Synchronising model and model metadata This is not an issue because there is little metadata to synchronise. Impact analysis There is no direct support for impact analysis. Metadata audit trail (technical and end users) There is no support for this. Access to upstream metadata There is no integration with third-party tools to give access to metadata generated at an earlier stage. Performance tunability Summary

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Express Server can operate in both MOLAP and ROLAP mode, so it could potentially be finely tuned for both approaches. As expected, its tunability strengths are as a MOLAP engine. The appropriate design of multi-cubes can enhance performance, but there is no automatic support for this. The tool supports SMP. One weakness of the tool, when used in ROLAP mode, is that the users have no direct way of knowing how long the data they are viewing has been cached. ROLAP Multipass SQL Multipass SQL is supported. Options for SQL processing The SQL statements are always processed on the database host machine. Speeding up end-user data access Retrieved data is stored in a cache in Express. Caching options are: • transient cache, in which the data is held in an Express cache for the duration of the user session • do not cache, but always query the RDBMS • permanent cache – all or some levels of the data are permanently stored in the Express cache. It is expected that the user will know what form of cache is being used because it will be a result of their requirements, thus there is no direct means of indicating the currency of the data to the end user. Aggregate navigator Express can transparently make use of summary tables. Information about summary tables is entered using Express Relational Access Administrator. MOLAP Trading off load time/size and performance The multi-cube architecture assists in size reduction. When specifying the cube definition in Express Administrator, any measure defined as a formulae (a calculated measure) can be pre-calculated or not. Support for multiple users There is no software limit to the number of users that can simultaneously access Express Server. Typically, such limitations are imposed by the hardware and communications set-up. Processing Use of native SQL to speed up data extraction In general ODBC is used, although as expected there is native access to Oracle’s relational database. Distribution of processing There are three main processing options determined by the architecture, rather than under administrative control: • thin client/web, in which all processing is done on the Express Server • client-server, in which the balance is application-dependent. Functions and stored procedures can be processed on the server or data can be extracted and processed locally on the client • processing on the RDBMS, using embedded SQL in the Express language, stored procedures on the RDBMS and aggregate SQL commands. SMP support Express Server uses a multi-threaded architecture which can exploit SMP.

Customisation

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Summary Oracle Express and its associated products provide excellent support for application development. Reports with multidimensional features can be developed using Oracle Express Analyzer’s visual development environment. Using Oracle Express Objects, fully featured applications can be developed combining ease of use with the power of a procedural language. Finally, Web Publisher enables ‘browser aware’ applications to be created. Customisation Option of using a restricted interface There is no direct support for users to use a restricted interface. Ease of producing EIS-style reports Using the visual development environment in Oracle Express Analyzer, users can create a customised report with EIS-type functionality. Applications Simple web applications These can be developed using Web Publisher. Development environment Oracle Express Objects is an environment and a set of components. It uses Express Basic to give programmatic control. In style it is similar to Visual Basic, with the addition of multidimensional objects such as table objects and graph objects and dimensionally aware list boxes. The multidimensional aspects are defined through a database browser. It is an object-oriented environment offering inheritance and polymorphism. The development environment can be enhanced by the creation of new objects, which can then be added to the toolbox. An application is developed as a series of pages. Use of third-party development tools SNAPI provides a C language interface to Express Server, and can be used to write programs in C/C++ or any other Windows programming environment (such as Visual Basic or Delphi) that supports calls to C functions in DLLs. Deployment

Platforms Oracle Express Server is available for Microsoft NT and various Unix platforms, including IBM AIX, Sun Solaris, HP-UX and Digital Unix. Personal Express is available for Windows 95 and NT.

Data access ODBC is used to access the data sources, thus any data source for which there is an ODBC driver can be accessed.

Standards The published API is Structure n-Dimensional Application Programming Interface (SNAPI). This is compliant with the OLAP Council’s specification.

Published benchmarks In May 1998, Oracle published figures for the OLAP Council’s APB-1 OLAP benchmarks.

Price structure Oracle Express Service (including Oracle Express Web Agent, Oracle Ex- press Administrator, Oracle Express Spreadsheet-In and Relational Access Manager) is licensed on a concurrent basis. Prices start at $4,995 for three concurrent users. The single user version of the Server, Personal Express, is $870 per named user. Oracle Express Objects (including Oracle Express Web Publisher) is priced at $4,995 per named user, with Oracle Express Analyzer at $745 per named user. All prices are Oracle’s standard global prices. Contact your local Oracle office for local country pricing. Pilot Software Pilot Decision Support Suite

Summary

At a glance ...... 3 Terminology of the vendor ...... 4 Ovum’s verdict...... 5 Product overview ...... 7 Future enhancements ...... 15

Commercial background

Company background ...... 16 Distribution ...... 17

Product evaluation

End-user functionality ...... 19 Building the business model...... 20 Advanced analytical power ...... 22 Web support ...... 24 Management ...... 26 Adaptability ...... 28 Performance tunability...... 30 Customisation ...... 31

Deployment

Platforms ...... 33 Data access ...... 33 Standards ...... 33 Published benchmarks ...... 33 Price structure ...... 33 Evaluation: Pilot Software – Pilot Decision Support Suite Ovum Evaluates: OLAP

At glance Developer Pilot Software, Cambridge, Massachusetts, USA Versions evaluated Pilot Decision Support Suite version 6.1 Key points • A hybrid OLAP server with client-server developer and end-user tools • Server runs on Windows NT and Unix; clients support Windows 95/98/NT. Web access is provided • Pilot has developed analytical applications for retail, CRM and business performance measurement Strengths • A mature MDDB engine that supports a range of analytical functions • Dynamic dimensions and hierarchies increase the scalability and flexibility of multidimensional models • Supports an extensible library of analytical modules for immediate analysis of data Points to watch • Pricing is geared for high-end sites – hybrid OLAP and web access are expensive options, instead of being bundled with the core product • Server set-up and management can be complex • Questions still remain about the company’s stability and growth Ratings

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End user functionality

Building the business model

Advanced analytical power

Web support

Management

Adaptability

Performance tunability

Customisation

Terminology of the vendor Analytical applets Predefined analytical applications that are provided with Pilot Desktop. The applications are driven by the business model and can be used interchange- ably across different models. Dynamic dimensions Dimensions that are calculated on-the-fly at runtime. They typically relate to attributes of a customer (such as salary or age) or product (such as colour or size) that normally generate very sparse datasets when cross-dimensioned with other categories. Dynamic hierarchies Groups of members that are dynamically defined at runtime in order to create new aggregation levels. A salesperson may, for example, want to create their own custom hierarchies based on best and worst customers. Structural dimensions Dimensions defined in a business model and preconsolidated in the Pilot MDDB.

Ovum’s verdict

What we think Pilot Decision Support Suite (PDSS) is a good choice for companies that want to perform complex analysis against large multidimensional datasets. The analytical power is expensive, however, making the product a high-end solution. PDSS offers high performance and scalability. The tools are built on a mature multidimensional database (MDDB) engine and a well-conceived architecture that gives database administrators (DBAs) considerable flexibility over where data is stored – in a MDDB or relational database, or combinations of both. Pilot’s dynamic dimensions and hierarchies are unique features that significantly increase adaptability, enabling end users to define new member groups in the model on-the-fly without having to rely on DBAs to redesign the core model. They provide an efficient means of tackling the requirements of large customer analysis or product management applications that analyse data at an attribute level. The prebuilt analytical applets included in the Analysis Library are an important aid to productivity, allowing for fast and easy analysis of data from different perspectives. Developers are spared the effort of building these applications from scratch and have access to a set of customisable objects as a basis for further development. PDSS is priced as a high-end tool – the hybrid functionality is considered a luxury rather than a core part of the product set, and is priced at 50% above the standard Analysis Server. The set-up and management of the MDDB can be complex, and the product lacks graphical tools and aids that help to build and maintain the hybrid system – many functions still rely on Pilot’s command-line interface. Apart from this shortcoming, there are few reasons to criticise PDSS in technical terms. The greatest challenge for Pilot is to improve its commercial standing after some turbulent years. To its credit, the company has implemented wholesale changes to its sales and marketing organisation and is targeting new markets via partnerships and the provision of vertically-focused analytical applications.

When to use PDSS is most suitable if you: • are developing medium- to large-scale customer analysis or product management applications that need attribute-level analysis • wish to analyse data over a range of time periods, using a variety of forecasting methods • want the flexibility to store data in a MDDB, a relational database or combinations of both • wish to provide users with out-of-the-box analysis with a minimal development effort.

It is less suitable if you: • do not have a well structured and highly-cleansed data source – there are no data extraction or transformation capabilities, so third-party tools will have to be licensed • are looking for an inexpensive and easy-to-use OLAP solution • have users that want to build their own custom models by sharing and re-using dimension and measure definitions – the Pilot tools are geared towards building fewer (but larger) business models that are centrally maintained by IS • have the necessary DBA skills in-house to set up and maintain the servers.

Product overview

Components Pilot Decision Support Suite 6.1 consists of the following components: • Pilot Analysis Server • Pilot Designer • Pilot Desktop • Pilot Internet Publisher • Pilot Desktop Reporting • Pilot Data Mining (Discovery Server). Figure 1 shows the primary functions of the components and whether they run on the client or the server. Pilot Analysis Server Analysis Server is Pilot’s MDDB. Three features are particularly notable: • dynamic dimensions and dynamic hierarchies • flexible time handling • integrated forecasting and modelling functions. It also includes a command-line ‘administrator’ interface for managing data, and a ‘supervisor’ graphical interface for administering end users and security. Hybrid option Analysis Server has a ‘hybrid option’ that provides a relational storage facility for model data. This option supports any amount of consolidation: • a fully-consolidated model stored in relational and multidimensional structures • an input-only ROLAP structure where all consolidation is done on-the-fly • anything in between.

Figure 1 Component details

OLAP analysis and Web access Data mining Development reporting

Client Pilot Desktop Pilot Designer Pilot Desktop Reporting Pilot Internet Pilot Data Mining

Server Pilot Analysis Server Publisher (Discovery Server) Analysis Server Hybrid Option

Pilot Designer This is a client-server development environment for building analytical applications. Designer also provides a Visual Basic-like scripting language for development. The development environment is closely integrated with the Analysis Server. Components of the development environment – the table object for building screens, for example – are ‘dimensionally aware’ of the models that they are working with. Pilot Desktop This is a client-server end-user tool that creates and analyses multidimensional models. Desktop provides a client runtime environment for Designer applications and includes a standalone version of Analysis Server that data can be extracted to from a network server. One aim of Desktop is to provide support for mobile computing. Desktop integrates three components: • Model Builder • Pilot Analysis Library • Pilot Excel Add-In. Model Builder A graphical development tool used to create multidimensional models. Model Builder can be used to build models in Analysis Server or the PC version of the OLAP engine. Pilot Analysis Library A library of prebuilt ‘analytical applets’ for ad hoc OLAP analysis and more specialised analysis, such as complex ranking, Pareto analysis, trend-line, budgeting, exception reporting and forecasting. The applets can be used across any Pilot model or customised using the Designer component. There are more than 14 prebuilt analytical applets provided in the Analysis Library. Pilot Excel Add-In A DLL add-in that enables Microsoft Excel users to access and analyse models in Analysis Server. It provides a dimensional selection object, and drill-down and rotate/pivot facilities. Users can also format and display data using standard Excel tools. Pilot Internet Publisher An application server that provides a web interface to Analysis Server. It accesses multidimensional data using a combination of HTML, ActiveX and Java. Significantly, Pilot has rejected the standard CGI approach for connectivity in favour of writing directly to Microsoft’s Internet Application Program- ming Interface (IAPI) on Microsoft’s Internet Information Server (IIS).

Pilot Desktop Reporting An integrated module for advanced reporting capabilities. Desktop Report- ing is an OEM version of Seagate Software’s Crystal Reports and is tightly integrated with the PDSS tools and applications. Pilot Data Mining In September 1998, Pilot announced that it was phasing out its Discovery Server and has now aligned itself with Thinking Machines, a data mining tool vendor. Pilot Data Mining is the integrated result of Darwin and the Pilot Decision Support Suite. It is available as an add-on component and provides data mining against relational data; the results of the mining operation are stored in a table in the relational database management system (RDBMS) and loaded into the Analysis Server like any other source data. Pilot Data Mining is targeted at the business user rather than the data mining specialist, and focuses on the specific needs of the marketing department. Pilot provides two applications (Segment Viewer and Profit Chart) that are aimed at the marketing manager and make impressive use of the data mining results within an OLAP environment.

Architectural options One of the most important features of PDSS is its flexibility in supporting a range of OLAP architectures for both client-server and web implementations. Full mid-tier architecture This is the ‘natural’ architecture of PDSS. In a full mid-tier architecture, aggregated data is loaded and stored persistently on a mid-tier server (where all OLAP calculations are done). When detail data is requested, the system drills through the multidimensional database to the underlying relational database. PDSS supports ‘full’ and ‘thin’ client implementations: • full client – uses the Desktop module to directly access Analysis Server’s MDDB • thin client – needs the inclusion of a web server, and an Internet Publisher server and clients. Internet Publisher uses Microsoft’s ISAPI (rather than CGI) for connectivity. The full client architecture uses a separate queue on the server to service each Windows client. By contrast, Internet Publisher uses a shared queue to allow multiple web users to access analysis applications. Light mid-tier architecture Analysis Server supports a ROLAP mode (where model data is stored persistently in a relational database) in a star schema. It also supports a metadata layer to access the relational dimensional structures for analysis on the server. A hybrid (MDDB and ROLAP) architecture is supported, allowing users to store, access and analyse data in a multidimensional or relational database.

Desktop and mobile architectures The Desktop component includes a standalone version of Analysis Server for desktop and mobile architectures. This allows data to be downloaded from the server on to a desktop PC for local processing, or a laptop for offline analysis.

Using PDSS Hybrid OLAP PDSS is a hybrid OLAP tool that provides a server-based multidimensional database as well as a relational storage option. What differentiates PDSS from other hybrid OLAP tools is how it implements the hybrid system. In most hybrid systems, the upper level aggregate data is stored in the MDDB; lower level (or detail) aggregate data is stored in the relational format. When detail data is requested, the system drills through the MDDB to the relational database in order to retrieve the data. PDSS can store data in a multidimensional or relational database by analysing usage. For example, if the upper tier of aggregate data is rarely accessed, it can then be stored in the relational database with the detail data; mid- level aggregate data that is frequently accessed can be stored in the MDDB for quick access. Although Pilot gives administrators considerable flexibility over where the data is stored, the set-up and management of the servers can be complex. Figure 2 is a typical PDSS architecture that shows how different levels of data can be stored in an MDDB server or a relational database. The benefits of dynamic dimensions and hierarchies Dynamic dimensions A unique feature of PDSS is its support for dynamic dimensions. Dynamic dimensions are calculated on-the-fly at runtime (as opposed to ‘structural’ dimensions, which are preconsolidated), but they act and appear as normal, precalculated dimensions. For example, two dynamic dimensions can also be cross-tabulated. For users, dynamic dimensions offer two main benefits: • scalability – very large models can be defined with tens (or hundreds) of dimensions without the resource problems typical of MDDBs • efficient analysis of data at an attribute level – dynamic dimensions relate to attributes of a customer (age or salary) or product (colour or size). These dimensions normally generate very sparse datasets when cross-dimensioned with other categories. Most other MDDBs allow users to select dimension members on the basis of attributes only (all red garments, for example). But Analysis Server allows these attributes to be used for multidimensional analysis. Dynamic hierarchies Dynamic hierarchies enable groups of members to be defined on-the-fly, creating new aggregation levels; a salesperson may, for example, want to create their own groups based on best and worst customers.

Figure 2 Hybrid architecture

Pilot Desktop Pilot Excel Add-In Web browser

Web server

Pilot Internet Publisher

Pilot Analysis Server (OLAP Engine and MDDB)

Pilot Data Highly aggregated and frequently-accessed data Mining OLAP metadata

RDBMS

Detail and/or rarely-accessed aggregate data

The main advantage for end users is flexibility when working with a business model. Users can define their own custom aggregation levels on-the-fly, without the need to wait for a DBA to redefine the core business model. Built-in time dimensionality PDSS has always had strong time-handling functions. The built-in time dimensionality within models automatically comprehends how data changes over time. Data collected from predefined timeframes is automatically consolidated, without additional programming or maintenance. Time intelligence also ensures that measures that do not add over time (such as ratios) are correctly calculated and displayed within the requested time periods. In a Pilot model, time is held at the lowest level of granularity within Analy- sis Server. Aggregation levels for time are dynamically created within the server, which understands standard and non-standard calendars (for example, months based on the pattern of four or five weeks). Many predefined views are also available, including ‘prior period’, ‘moving average’ and ‘rolling views’. A custom view capability accommodates special timeframes such as promotional and seasonal periods.

Pilot Analysis Library From an end-user perspective, PDSS’s analytical capability lies in its Analy- sis Library. The library consists of a series of point-and-click ‘analytical applets’ that can be used out-of-the-box for immediate analysis. The term ‘applet’ is slightly misleading, as they are in fact fully-functional applications built with Designer; they can also be customised using the Designer tools. In practice, however, they function as ‘templates’ because they are data-driven, picking up all relevant values dynamically from a multidimensional model (including all dimensions, aggregation hierarchies and measures). An analytical applet therefore works automatically against data from any Pilot business model. The applets provide a range of functionality from a standard tabular display of multidimensional data to an advanced application for trend forecasting. Some applets come in multiple versions, each of which offers specialised variations of a similar analytical theme. The main analytical applets available are: • Navigator, which supports ad hoc analysis. It provides a table view on a business model, as well as the basic OLAP functions such as drill-down, dimension selection from your model, rotation, and row and column calculations • Exception Reporting and Exception Stars (for exception and variance reporting over two time periods) • Ranking Analysis, as shown in Figure 3, for ranking dimensions according to a single measure. Ranking Stars ranks a measure for two time periods; Ranking Plus displays two measures side by side (for example, sales and margins) • 80/20 Analysis, which is used, for example, to determine what percentage of total business is represented by any group of products, regions, accounts, customers or channels. 80/20 Stars analyses two time periods; 80/20 Plus displays analysis for two measures side by side • Trend Forecast, which allows users to select from the various forecasting algorithms that are supplied to produce a trend analysis on the basis of any selected data • Quadrant Analysis, which creates a four-square grid based on two selected business measures and shows the correlation between the two. If, for example, the measures are sales and margins, the top quadrant shows products with high sales and high margins. By default, the bottom half of an analytical screen shows what dimensions and measures are available – users can choose their preferred type of analysis. These controls can be hidden to provide an ‘executive’ view, showing only results. Moving from one kind of analysis to another keeps the same perspective on the data. Whatever view users have of data, when they switch applications – from 80/20 to Quadrant Analysis, for example – the new application looks at the data from the same view.

Figure 3 Ranking Stars application

Developing applications PDSS provides Designer, its own integrated development environment, which offers a number of graphical tools for creating analytical applications easily. Designer applications are based on the concept of ‘sheets’, which are composed of ‘objects’. Each object has associated properties defined in a set of tabbed dialogues. Designer has a Visual Basic-like scripting language that is used to define dialogues or fine-tune functionality. Most development is done by tailoring presupplied objects such as tables, charts, OLE containers or listboxes. The Object Manager interface gives a hierarchical view of components of a sheet, as shown in Figure 4. Its aim is to make the task of managing complex screens easier. The table object is particularly important, because it provides cross-tabular functionality for multidimensional analysis and no additional programming is needed. The table object can be linked to a dimensional model, SQL procedure, text file or DDE source. When using the table object against a business model, you select a view on the model and the application runs immediately, providing default cross- dimensional analysis capabilities. Default features include: • charting • the selector object (for choosing measures and dimensions) • an inherent understanding of drill-down • access to Pilot’s calendar functions.

Figure 4 Object Manager interface

The features that are made visible to the end user can be limited by the developer. As with prebuilt analytical applets, applications that are created in Designer are inherently data-driven rather than procedurally defined. This means that an application should run against any model without change.

Future enhancements Version 6.2 of PDSS is due in mid-1999. It will include the following enhancements: • model partitioning – allowing data to be stored across separate models with shared structures. The models can then be treated independently, and can be loaded and consolidated incrementally by different processes • the ability to re-use and share dimensional structures and measure definitions between models • writeback capabilities via the Excel Add-In facility • agent-based distribution services – through an ongoing partnership with Blue Isle Software, Pilot will provide agent-driven analysis and distribution capabilities. For example, users will be able to create an agent process to watch for specific criteria (for example, exceptions or other events in the database), triggering data loads or updates and then notifying users via e-mail (or other devices such as pagers and mobile phones) • extended data access from Internet Publisher – web users will be able to access MDDB and relational data from the same screen without additional third-party data access tools. Tight integration with Thinking Machines’ data mining tool, Darwin, is also scheduled for 1999. The aim is to have the data mining engine talking to the Analysis Server, so that results from data mining will feed directly into the MDDB server model. The integration will provide the focus for developing new fraud detection applications that extensively integrate data mining functionality. Pilot intends to develop additional vertical applications that build OLAP analysis solutions on top of CRM vendor packages. It is seeking to form partnerships with campaign management, salesforce automation and call centre software vendors.

Commercial background

Company background History and commercial Pilot Software was incorporated in the US in 1983. It is one of the longest- established OLAP vendors. Pilot launched one of the first EIS tools (Com- mand Center) in 1984 and became a leading vendor in the mainframe EIS market. The company has claimed a number of ‘technology firsts’, including time-series analysis and the use of a multidimensional database. Pilot Lightship, first launched in 1992, marked a departure from a mainframe- centric approach towards a client-server architecture. Lightship has now been replaced by PDSS. In 1994, Pilot was bought by Dun & Bradstreet and was incorporated as part of its Cognizant business division. The takeover provided Pilot with resources to complete its transition to a provider of client-server tools. How- ever, it did not deliver the growth in revenues that was expected; conflicting visions between the two management teams resulted in significant problems with Pilot’s sales and marketing activities. In late 1997, Pilot was sold to Platinum Equity Holdings, a US company that specialises in buying high- tech companies. A new business model that focused on channels and partners was then defined, and unprofitable operations were shut down. Pilot also restructured its salesforce and management team, and appointed a new CEO. Pilot employs around 175 people. Ovum estimates that the company had revenues of approximately $35 million for the 1998 fiscal year. The company’s corporate headquarters is in Cambridge, Massachusetts, USA, with regional offices and distributors worldwide. Character and direction Pilot Software has been in the business intelligence market for several years, and was a founding member of the OLAP Council. The company’s reputation for technical innovation and extensive experience make it an important player in this market. Despite Pilot’s established presence in the market and the steady evolution of its OLAP products, it has been unable to build up any serious commercial momentum. The takeover by Platinum Equity Holdings, however, has introduced wholesale changes; Pilot’s newly-appointed CEO now refers to the company as a ‘mature start-up’. Pilot’s traditional focus was on sales and marketing applications. This horizontal focus has been further refined though important partnerships to develop and market packaged analytical applications for vertical industry sectors, including: • retail – Retail Performance Monitor and Retail StoreCard • financial services – Commercial Credit • consumer banking – Customer Attrition • telcos – Churn Prophet and Channel Wizard.

Pilot has more than 500 customers and has sold more than 100,000 user licences worldwide. Most of the customers are large Global 2,000 companies. Pilot has a particularly strong presence in the retail, telco and consumer packaged goods sectors. Large customers include AT&T, Office Max, Burger King, Kmart, Baskin Robbins, Whirlpool and Lucent Technologies. The company sells its products mainly through direct channels, but is increasingly using channel partners to penetrate new markets such as customer relationship management, manufacturing and healthcare, and balanced scorecard. Major application partners include Foresight Software, Lightbridge, American Software, Synertech, IMS Health and Touch.

Customer support Support Telephone, e-mail and fax support is available worldwide; it is primarily aimed at developers rather than end users. Support centres are located in the US, Europe and Australia. Support is priced at around 20% of the licence fee. Training Pilot offers training for all components of PDSS, which is available either on- site or from its worldwide offices. Consultancy services Pilot provides a range of consultancy services for PDSS implementation and application development. Most consultancy work focuses on defining business requirements, building models and implementing data access strategies.

Distribution US Pilot Software 1 Canal Park Cambridge, MA 02141 USA Tel: +1 617 374 9400 Fax: +1 617 374 1110 Europe Pilot Software Maxfli Court Riverside Way Camberley Surrey GU15 3YL UK Tel: +44 1276 687000 Fax: +44 1276 687077

Asia-Pacific Pilot Software Level 1, Building A Forest Corporate Park 18 Rodborough Road Frenchs Forest NSW 2086 Australia Tel: +61 2 9975 2380 Fax: +61 2 9975 2386

http:// www.pilotsw.com E-mail: [email protected]

Product evaluation

End user functionality Summary

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Users that are familiar with analysis tools will find Desktop easy to navigate, but OLAP novices may initially feel overwhelmed by the interface. Desktop comes with a number of point-and-click analytical applets that can be run immediately against any model. The look-and-feel of the navigation is similar, regardless of the analysis at hand; once users learn how to navigate one application, the others will be easy to use. Advanced reporting relies on an OEM-enabled version of Crystal Reports, but customers must pay extra for this. The client tools would benefit from more metadata (to help end users understand the model better) and the ability to drill through to source data. Publish- and-subscribe capabilities for widespread distribution are not supported. Finding and understanding the model Finding and loading a multidimensional model A Portfolio applet allows specific data views to be collected and used as a starting point for analysis. Apart from this, there is no support for logically grouping business models or searching for them using keywords. Metadata for end users There is no scope for viewing metadata about a model. Annotation by end users The direct annotation of models by end users is not supported. Using the model Basic OLAP functionality The Navigator applet can provide ad hoc OLAP analysis functions, such as drill-down, drill-up, and pivot and rotation. Changing the position of members in a dimension level A dialogue box is provided for re-ordering members in a dimension level. Visualising the drill-down hierarchies The Selector dialogue box provides a graphical representation of the drill- down hierarchies. There is no support for showing the users’ position within it, however. Drilling-down to detailed data It is not possible to drill-down through models to source transactional data.

Range of front-end user tools Desktop supports more than 14 analytical applets that can be used as front- end interfaces. A Microsoft Excel add-in is also provided. Custom front-ends can also be developed by third-party tools such as Visual Basic and PowerBuilder, and integrated using the Designer tool. Third-party OLE DB for OLAP clients cannot access Analysis Server, however. Visualising the results PDSS supports more than 50 business charts. OLAP (drill-down and rotation) from within charts and graphs is not automatic, but can be built-in by developers. PDSS integrates mapping technology from MapInfo for geospatial presentation and analysis of data. Saving and sharing results Designing a report PDSS does not have a dedicated report design tool; reports are based on tables that users can add headers and footers to. For advanced report design, PDSS relies mainly on Pilot Reporting, an OEM version of Seagate Soft- ware’s Crystal Reports. The Desktop Reporting component uses ActiveX to feed data from Pilot applications to a report template – the report template can have any structure supported by Crystal Reports. Nested crosstabs are supported, as is the ability to embed OLE objects in report templates. Local calculations can also be defined. Publishing a report There is no direct support for publishing a report. The only way to do so is to set up a shared server environment and allow users to publish reports in a public portfolio for access by others. Targeted distribution via e-mail PDSS is MAPI-enabled and integrates with Windows-based e-mail systems. It also works with Lotus Notes – the Notes link provides an e-mail and a document interface. Notes documents can be integrated into a ‘sheet’ in a Pilot application. Subscribing to reports Report subscription services are not provided.

Building the business model Summary

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Model Builder provides an easy-to-use graphical environment in which to create a basic model structure. The modelling capabilities are quite sophisticated. The tool’s efficient handling of time series calculations and the ability to define dynamic dimensions and hierarchies at runtime is unique among OLAP tools, and simplifies the task of building and maintaining large models. However, developers will often have to use Pilot’s proprietary command- line language IDQL (Interactive Dimensional Query Language) to add greater complexity to the multidimensional structure.

Basic design Design interface Model Builder is a menu-driven GUI tool for building business models and is part of the Desktop and Designer clients. Model designers can select the dimensions, hierarchies and measures required in a model through a set of menus, pick lists and dialogue boxes. Model Builder is best used to build a standard base model, but lacks advanced design functions. This can be remedied using the IDQL (Interactive Dimensional Query Language) command-line interface. Visualising the data source It is not directly possible to visualise the data source, but a check-box is associated with every field listing and allows developers to display values from the data source during the design process. Universally available mapping layer A universally available mapping layer is not supported. Prompts for metadata The level of metadata captured during the design process is minimal. Building the dimensions Selecting columns for the dimensions To create a new model, developers select a source and define a SQL query to retrieve information on the target data structure. Model Builder provides a set of dialogue boxes and menus to select columns for dimensions from the results set. Selecting the members shown in a dimension level Members can be selected using point-and-click or SQL. Defining a dimension hierarchy Developers can define their own ad hoc aggregation hierarchies using point- and-click. Multiple hierarchies in a dimension are also supported. It is not directly possible, however, to specify ragged (that is, unbalanced) dimensions. Time dimension PDSS makes time an implicit part of the data model. Model designers do not have to define the time dimension; only a fiscal calendar has to be specified, so that period calculations can be done. Time is held at the lowest level of granularity (determined by the source data); any required aggregation levels are generated dynamically. Analysis can be based on a range of time factors, including daily or hourly, and various non-standard time periods such as retail calendars based on four- or five-week months and 13-month lunar calendars. Users can also define their own calendars and periods, including dynamic periods. The time options also ensure that measures that are not additive over time – for example, balance sheet items or ratios – are correctly calculated and displayed.

Annotating the dimensions Model designers can define long and short names for dimensions. End users can choose which type of name to use for display in reports and charts. Default level of a dimension hierarchy Default dimension levels are typically specified at the application design level. Defining the measures Calculated measures Calculated measures can be defined graphically using a formula. But the range of predefined functions provided is limited. Advanced calculations can be created through logic sets such as ‘if, when and else’ statements; these can be applied to stored measures or virtual measures (calculated at runtime). Support for multiple measures with a set of dimensions This is supported. Multiple designers Multiple designers Other than locking a model, there is no shared repository to support multidesigner environments. Support for versioning Versioning control is not supported.

Advanced analytical power Summary

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Pilot’s Analysis Library is the most significant resource for analysis. The analytical applets fulfil standard and more specific analysis tasks, and support a wide range of statistical analyses, correlation methods and forecasting techniques. The Analysis Library is powerful and easy-to-use. Most applets can be used out-of-the-box and can be applied to data from any Pilot model; however, users would benefit from additional help or tutorials to interpret the results. Developers can also customise the applets (using the Designer tools) and create new functions using IDQL, the non-procedural language. Data mining capabilities are also provided.

Third-party tool integration Integration with Microsoft Excel and data mining tools is provided. There is no integration with other analysis front-end tools, such as SPSS. Defining specialised models Ranking and sorting The Analysis Library includes four ranking modules that support basic ranking on a single measurement and ranking a dimension based on different measures. Support is also provided for Pareto analysis. The Rising Stars application enables users to analyse the fastest gains and losses at regular time intervals. Mathematical methods Support is provided for a range of advanced mathematical functions. These functions are extensively used by the analytical methods discussed in this section. Financial functions Support is provided for standard financial functions such as net present value (NPV), internal rate of return (IRR) and depreciation. Statistical models A built-in statistical library for trend and forecasting techniques is provided. A number of correlation statistics are supported, including moving averages, standard deviation, skew, kurtosis and the DurbinWatson method. Trend analysis Support is provided for a variety of curve fitting and smoothing techniques. The Trend and Forecast analytical object makes many of these functions readily accessible to the end user. The user selects from the various forecasting algorithms supplied to produce a trend analysis for any selected dataset. The user picks the forecasting and smoothing method to be used from a menu. Simple regression Support is provided for linear, polynomial and stepwise regression techniques. Time series forecasting A range of complex forecasting functions are available as menu options. These include extrapolation, exponential, hyperbola, quadratic and rational. User-definable extensions Users can also extend functions and build their own functions using IDQL, Pilot’s proprietary language. IDQL is a simple non-procedural language similar in syntax to Pascal. The functions that are created can be stored in a common repository for re-use.

Writeback for ‘what if?’ analysis The Analysis Server only supports single-user writeback. For ‘what if?’ models or budgeting applications that require changes to the model data, a user can save a ‘snapshot’ of the data for local access. The data can be loaded back into the server later. Pilot also provides a special What If? application that can be used to create business scenarios by increasing and changing the values of various measures in a model. The What If? application, developed in partnership with Extend (a Brazilian partner), is fully integrated with the PDSS tools but is sold as an optional component. Incorporating non-numerical data Analysis Server can store and manipulate textual data within the multidimensional database. It does not provide any text analysis functions. Data mining Predictive data mining capabilities are available via Discovery Server; however, Pilot is in the process of phasing out this application, and now has an agreement with Thinking Machines to integrate with (and resell) its Darwin data mining tool. Further information on Darwin can be found at http://www.think.com. Other analytical functionality A number of analytical objects, as discussed in Using PDSS, are included in the Analysis Library for specialised analysis.

Web support Summary

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Given the analytical nature of Pilot applications, Internet Publisher is most useful for internal or intranet use rather than deploying applications over the Internet. The web interface provides a similar level of analytical capabilities as the Desktop clients through a combination of HTML and Java applets. Users that need more complex functionality will be better served using the Desktop client. By artificially limiting the maximum number of server resources, administrators can control and balance server loads on a shared system. Desktop also provides server caching and resource pooling for increased performance and scalability. Internet Publisher works exclusively with Microsoft’s Internet Information Server – connectivity (via CGI) to a wider range of web servers is not supported.

End-user functionality via the Web Functionality of web access to explore models Internet Publisher’s web interface uses HTML to present the data and most of the interface functions to the browser; Java is used for complex analytical features. The web interface supports picklist-driven views – users can select and combine picklist choices to perform drill-down and rotation functions. Web users can also create their own dimension member groups or summarised results for custom analysis needs. The same analytical functions included in the Analysis Library are available as Java or ActiveX applets, but there is no support for writeback or locally- defined calculations via the Web. Supports both registered and unregistered web access All web users must be registered and named users. Range of users supported by the Web interface Internet Publisher generates HTML tables that can be colour-coded for casual users requiring simple reports or EIS interfaces. Power users are supported by Java-based analytical applets. Creating models via the Web There is no support for creating and editing models via the Web. Distributing via the Internet and the Web Generate HTML and Java Internet Publisher generates HTML tables that can be displayed in web pages, but does not provide its own web page editor. It can also provide a datastream for Java or ActiveX objects. Corporately organised distribution via the Internet In partnership with Blue Isle Software, Pilot enables users to distribute models as personal models via the Internet as a compressed e-mail attach- ment. Models can also be distributed via URL links in an e-mail or personal web page. Include URLs in a report It is possible to include URLs in reports. Distribution of web server processing Internet Publisher can distribute processing over a heterogeneous cluster of servers. Administrators can define maximum server processes for each server. When the maximum load is exceeded, additional processing requests are automatically queued and dispatched in a balanced fashion. Alternatively, administrators can select groups of users that have their own pool of dedicated server resources. This guarantees optimal performance for users that require preferred response rates.

Management Summary

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PDSS gives administrators considerable flexibility over where the data is stored. Detailed monitoring tools are provided to refine the system’s configuration based on the most popular queries and usage patterns. The servers are complex to set up, however, and would benefit from graphical tools and aids to make the process less time-consuming. Most management functions are accessible via a proprietary command-line interface; the only exception is the Supervisor utility, which provides a graphical (but inflexible) environment for defining user groups and access rights. Developers can build a set of procedures that can be run to refresh the model. Analysis Server does not have its own scheduling services, however; they must be performed using operating system functions. Management of models Separate management interface The Administrator interface is used to manage models and data. The interface has a command line; administrative functions are accessed by programming directly in IDQL. Security of models Three access modes are available for models: • exclusive – enables only one user to access the model at a time in the read or write modes • read – enables all users to access the model in read mode • shared – enables all users to access the model in the read or write modes. While one user is using the model in shared mode, no other user can access it in the exclusive or read modes. Because Analysis Server must continually check whether the data has changed, the shared mode results in a much slower response time and is not recommended. User and/or group access to models can also be limited by dimensions, aggregation levels or measures. Users are simply provided with filtered views of the business model and are not aware that they are excluded from elements of the model. Query monitoring The TrackerTable monitors queries that are made to the MDDB and relational database. The number of hits and the time taken to process a query based on an intersection of specific model elements is recorded and stored in a relational table for further analysis and performance tuning.

Management of data How persistent data is stored (not scored) Data can be stored in a MDDB, a relational database, or combinations of both. Scheduling of loads and updates Once a model is defined, Model Builder creates a set of procedures that can be run to update a model. The Analysis Server does not, however, have its own scheduling services; this has to be done using operating system functions. Event-driven scheduling Event-driven scheduling is not supported. Failed loads and updates All loads and updates to a model are logged and a trace is provided of any rejected data. Distribution of stored data Data can be stored in Analysis Server’s MDDB or in the relational database as base level data or in summary tables. This flexibility allows DBAs to configure where the different aggregate layers in a model are stored. Data can also be stored locally on the client. Sparsity (only for persistent models) Sparsity handling is automatically defined – the MDDB only stores and indexes data that exists. Analysis Server uses hashing techniques to handle sparse data. Methods for managing size Storage requirements for large models can be controlled through dynamic dimensions and the ability to selectively cross-dimension measures (for example, price) may relate dimensionally to product but not to geography. Dynamic measures, calculated at runtime, can also be defined to save space. In-memory caching options In-memory caching options are not supported. Informing the user when stored data was last uploaded There is no provision in the default applications for informing end users of the currency of the data that they are working with. A ‘text’ measure called ‘last_update’ (or similar), with no dimensions associated with it, can be used. With each load, a string is stored in this measure with the time and date of the last update (which can subsequently be queried in reports). Management of users Multiple users of models with write facilities Only single-user writeback is supported. When the writeback mode is used, the entire database is locked and write access is exclusive to a single user.

User security profiles The Supervisor interface provides a graphical environment for managing end users and setting up groups. But there is no support for creating and re-using individual user profiles. Query governance Query governance is not supported. Restricting queries to specified times There are no facilities to restrict resource-intensive processing to specific times of the day. Management of metadata Controlling visibility of the ‘roadmap’ In the case of PDSS, the visibility of data is principally governed by the access rights granted to specific users. Additionally, any controlled view of data can be defined in IDQL.

Adaptability Summary

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A significant feature of PDSS is its inherently model-driven approach. All analytical applications can run against any model; changes to a model are automatically reflected in analytical applications. Within a Pilot model, adaptability is usually a case of being able to add new dimensions and measures. This is a simple process, although there are no change management facilities provided to track the changes being made. The use of dynamic dimensions and hierarchies increase adaptability by allowing changes to be implemented with minimal effect. Each Pilot model is distinct, however, and there is no scope for re-using definitions across different models. Because PDSS has no native control over loading schedules, data sources and models are not automatically synchronised. Impact analysis and access to upstream metadata is not supported. Change in business requirements Adding new dimensions to a model New dimensions can be added to a model using Model Builder or IDQL’s command-line interface. There are no change management facilities to track the changes made to a model. Re-use of dimension definition It is not possible to re-use dimension definitions across multiple models.

Adding new measures to a model New measures can be added to a model using Model Builder or IDQL’s command-line interface; however, there are no change management facilities to track the changes made to a model. Re-use of calculated measure definition Measure definitions cannot be re-used across different models. Changing the architecture to reflect business needs PDSS is a hybrid OLAP product that can be configured for ROLAP and MOLAP modes of operation. The availability of a standalone desktop version of Analysis Server extends the flexibility of the product to support mobile architectures. Changes to data sources Keeping the data source and model schema synchronised There are no automatic facilities to keep the relational data source and model synchronised; this is a manual process. Administrators must rebuild the structure of each ‘load and consolidate’ process by querying the relational source to keep the two synchronised. Automatic updating of members in a dimension New members are picked up by the data loading procedures and added to the business model under the control of the DBA. There are no automatic facilities to identify and remove non-existent items in the model; these items must be deleted manually. If a new member has no associated hierarchy in the incoming data, then it is added to the model as a data point but it is not included in any calculations; Pilot refers to this as an ‘orphan member’ because it has no aggregate parents. Metadata Synchronising model and model metadata Apart from structural metadata, which remains synchronised in the MDDB at all times, there is little metadata to synchronise. Impact analysis There is no support for impact analysis. Metadata audit trail (technical and end users) A metadata audit trail is not supported. Access to upstream metadata There is no access to upstream metadata from data warehousing tools.

Performance tunability Summary

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The ultimate limit on scalability is the capacity of Analysis Server; however, performance depends largely on the design of the system and the model – the excessive use of dynamic dimensions will inevitably affect performance. Analysis Server provides dynamic monitoring tools that help administrators to determine the optimal server configurations, including the specification of consolidations and where they should be stored, and to tune the system for quicker data access. Internet Publisher connects to web servers directly using Internet Application Programming Interface (IAPI). While this effectively restricts the choice of web servers that can be used, Pilot also claims that it provides a faster and more secure gateway, and avoids the bottlenecks associated with CGI. ROLAP Multipass SQL Multipass SQL is not supported. Options for SQL processing Processing is carried out on the database, whereas advanced and time-based calculations are carried out on Analysis Server. Speeding up end-user data access Server-based caching of frequently-requested data is supported. If the underlying data is changed, however, then the cache is automatically deleted. Aggregate navigator PDSS is aggregate-aware. By using TableTracker and metadata, the SQL generator is aware of the nearest neighbour for consolidations and uses that data. MOLAP Trading off load time/size and performance Dynamic dimensions remove the need to reconsolidate the model every time new data is loaded into the MDDB. There is a trade-off in terms of performance, however, for models that analyse a large number of dimension attributes. Analysis Server provides a graphical interface specifying preconsolidation or consolidation on-the-fly based on usage statistics returned from the TableTracker monitoring tool. Support for multiple users Pilot claims that some of its larger customer sites have several thousand users. The company has shown usage logs of up to 500 concurrent users on a single Unix Analysis Server.

Processing Use of native SQL to speed up data extraction Native access is only provided for Oracle and Sybase relational databases. Access to other RDBMSs is via ODBC. Distribution of processing PDSS does not support distributed server architectures (for example, through peer-to-peer processing between Analysis Servers). Internet Publisher does not support load balancing across multiple servers. SMP support There is no support for SMP parallelism.

Customisation Summary

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Designer provides graphical tools and a Visual Basic-like scripting language to customise analysis modules or create new applications. Components of the development environment are ‘dimensionally aware’ of the models that they are working with; however, the development approach lacks the features that come with fully object-oriented environments. The client interface can be extended with Windows development tools through the OLE interface or by extending the Designer environment using the Pilot Software Development Kit (SDK). Customisation Option of a restricted interface There is no provision for giving users the option of a restricted interface. Ease of producing EIS-style reports A portfolio can be defined for presenting a series of EIS-style reports in a briefing book paradigm. Similarly, a personal page can be created to define EIS-style web homepages. Applications Simple web applications Web developers can build custom Internet Publisher applications using HTML, JavaScript, Java, VBScript or ActiveX programs. Development environment Designer provides a graphical object-based development environment. Applications are built from a set of a standard visual objects (such as push buttons or dialogues) and special dimensionally-aware objects such as tables

and charts. OLAP functionality can be built-in to applications easily using the predefined objects without programming. A Visual Basic-like scripting language is available to tailor applications further and add more custom functionality. The development approach, however, is object-based; it lacks features such as inheritance and polymorphism that come with fully object-oriented environments. Although this limits the re-usability of application components, it does make it easier for novice developers to understand and maintain Pilot applications. Use of third-party development tools Third-party development tools (such as Visual Basic, C++ and PowerBuilder) can access Analysis Server functions and objects through the OLE2 interface. A SDK is also available to support third-party development using the Analysis Server API. Other customisation features Localisation Analysis Server handles multibyte character sets that allow users to run Pilot applications against non-English data warehouses. The language display and format is fully client-controlled.

Deployment

Platforms Client The Desktop and Designer clients run on the Window 95/98/NT formats. Internet Publisher supports Microsoft Internet Explorer and Netscape web browsers. Server Analysis Server runs on Windows NT and Unix (Solaris, HP-UX, AIX, Digital, AT&T, Pyramid, NCR and Sequent). AS/400 is supported through an exclusive partnership with SystemSource. Internet Publisher server runs on Windows NT and Solaris. It uses Microsoft’s Internet Information Server (IIS) as its web server.

Data access PDSS provides native access to Oracle and Sybase relational databases. Other relational sources are accessed via ODBC. It can also access ASCII files. SDKs are provided to access ERP data sources, such as SAP.

Standards PDSS does not support Microsoft’s OLE DB for OLAP API; support as a consumer is planned.

Published benchmarks Pilot has not participated in, or published any, OLAP benchmarks.

Price structure Pricing for Analysis Server starts at $25,000 for up to ten users for Windows NT and Unix platforms. The hybrid Analysis Server OLAP option is an additional 50% of the server price. The Internet Publisher prices start at $10,000 for up to ten users. Internet users are charged $50 each; Desktop users are charged $895 each (which includes the Analysis Library). A Designer developer licence costs $4,000. The Reporting module costs $9,995. The price of the packaged applications starts at $12,500 for ten users; please note that these applications also need Analysis Server and Desktop licences.

At a glance ...... 2 Terminology of the vendor ...... 3 Ovum’s verdict...... 4 Product overview ...... 6 Future enhancements ...... 14

Commercial background

Company background ...... 15 Distribution ...... 16

Product evaluation

End-user functionality ...... 17 Building the business model...... 19 Advanced analytical power ...... 20 Web support ...... 21 Management ...... 22 Adaptability ...... 24 Performance tunability...... 25 Customisation ...... 26

Deployment

Platforms ...... 28 Data access ...... 28 Standards ...... 28 Published benchmarks ...... 28 Price structure ...... 28 Evaluation: SAP AG – SAP Business Information Warehouse Ovum Evaluates: OLAP

At a glance Developer SAP AG, Walldorf, Germany Version evaluated SAP Business Information Warehouse (BW), version 1.2A Key facts • A data warehouse that is preconfigured to work with SAP R/3 data • Server runs on Windows NT and Unix; clients run on Windows 95 and Windows NT and use Microsoft Excel as a presentation layer • BW is an independent product and has a separate release cycle from SAP R/3 Strengths • SAP delivers preconfigured business content and plans to add more in future versions • Preconfigured multidimensional models, extraction routines and reports make initial implementation quick and easy if solely SAP data is being used • Central and easy to use tools for administering the whole data warehouse Points to watch • Limited range of end-user tools – no web access to models • Building models from non-SAP data relies on R/3 expertise and the use of third-party tools • Requires SAP skills to set up, enhance and manage Ratings

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End user functionality

Building the business model

Advanced analytical power

Web support

Management

Adaptability

Performance tunability

Customisation

Terminology of the vendor BAPIs Business APIs. A published programming interface that potentially gives access to software applications from a range of vendors. BAPIs are proprietary, though SAP has a certified partner programme to encourage use by third parties. Business content A key feature of the BW philosophy is that the information is organised into meaningful ‘business content’. The preconfigured data extraction, storage and presentation content is designed with business needs in mind. Characteristic SAP term for a dimension used in an InfoCube. InfoCube Conceptually, an InfoCube is a multidimensional database that is used for analysis and reports. It is implemented using a set of relational tables arranged in a star schema. An InfoCube contains InfoObjects and corresponds closely to Ovum’s definition of a business model. BW comes with more than 20 preconfigured InfoCubes. InfoObjects A generic term for business objects, such as ‘customer’ and ‘revenue’. They are similar in function to a data structure, but with additional meta information. InfoSource InfoSources are the main method of feeding data into BW. For data to be used in BW, it must be part of an InfoSource. BW provides predefined InfoSources for accessing R/3 data. New InfoSources can be created using the Administrator Workbench tool. Key figures These are quantifiable values, such as ‘revenue’, which are either extracted from a source system or derived from a calculation. This corresponds with Ovum’s definition of a calculated measure.

Ovum’s verdict

What we think SAP’s Business Information Warehouse (BW) comes fully equipped for SAP infrastructures and will therefore slot smoothly into any R/3 environment. If the preconfigured data extraction routines, multidimensional models and reports that come with BW map closely to your organisation’s business intelligence needs, then implementation can be relatively quick and easy. If there is also a preference to ‘buy from a single vendor’, then BW will be a compelling product for users wanting OLAP access to large amounts of SAP data. The tight integration with the R/3 OLTP modules and preconfigured business content are undoubtedly the greatest strengths of the product, and should allow SAP to carve itself a niche for quick turnkey implementations. However, the downside is that it can best be used and extended by developers with strong SAP skills. Business users are therefore highly dependent on IS to set up and supply them with specialised models. BW is primarily geared to use R/3 data and SAP has yet to prove that it is easy to integrate with non-SAP data sources and end-user tools; this relies on proprietary BAPIs, expensive R/3 skills and third-party applications and tools. SAP does not have great OLAP experience and BW, in the first release at least, falls short of providing the advanced modelling, analytical and reporting capabilities found in more mature OLAP tools. Nor is it clear whether SAP can rollout functionality fast enough to satisfy complex user needs. Although the Excel client is sufficient for standard OLAP, BW is hindered by its lack of front-end tools. The market is flooded with OLAP clients that provide more flexible and powerful front ends. BW comes with a degree of openness not usually associated with SAP. However, SAP has not yet published the specifications for the BAPI that OLAP tool vendors would use to get access to BW data. Full support for the OLE DB for OLAP interface is also lacking.

When to use SAP BW is most suitable if you: • have most of your corporate data in R/3, and need direct analysis of data in the transaction databases for decision support • can closely map BW’s preconfigured models and extraction routines against your organisation’s business intelligence needs • want to buy a turnkey data warehouse package from a single, well-known vendor, rather than build your own • have SAP development skills in your organisation.

It is less suitable if: • the amount of SAP data required for your decision support needs is minimal • you have already made a large investment in a data warehouse • you want to support a small proof of concept project, with a small investment • you have users with minimal SAP skills that want to build their own models from a variety of data sources • you have significantly customised your base SAP system, and thus need to develop a significant number of additional extraction routines and models.

Product overview

Components The key OLAP components of BW version 1.2A are: • BW Server • Administrator Workbench • Business Explorer • Data Extractors (for R/3) • BAPIs (Business APIs). Figure 1 shows the primary functions of the components and how they relate to client-server systems. BW Server A mid-tier server that includes an OLAP engine, a metadata repository and a database – all of which are preconfigured for R/3. The server processes all OLAP requests and returns results data to clients. InfoCubes The BW database is structured into self-contained multidimensional data ‘containers’, called InfoCubes. An InfoCube is stored in a number of relational tables in a star schema. The database can reside within BW Server or on a remote database server. SAP provides more than 20 preconfigured InfoCubes. Users can also extend existing InfoCubes and create additional ones. InfoCubes contain InfoObjects (dimensions and measures). They are fed from InfoSources that extract data from R/3 systems or external systems such as relational data warehouses, flat files or other source systems. OLAP Processor An OLAP engine that is used for processing data in InfoCubes. It provides the methods needed to query data and perform OLAP analysis.

Figure 1 Component functions

OLAP analysis Warehouse management Integration administration

Client Business Explorer Administration Workbench

Server BW Server Data Extractors BAPIs

Staging Engine The Staging Engine requests an extract from an InfoSource and performs the necessary mappings and transformations needed to create InfoCubes. It uses SAP’s ALE (Application Link Embedding) middleware for data transport. Metadata repository The metadata repository stores business-related and technical metadata in catalogues. ‘Business metadata’ includes content definitions, descriptions and rules. ‘Technical metadata’ describes structures and transformation and mapping rules for the data extraction and staging process. Operational Data Store An optional component that temporarily stores transactional data in BW. The data format remains unchanged; no aggregations or transformations take place. The Operational Data Store (ODS) is organised as a set of flat tables, each assigned to an InfoSource. The ODS is primarily used as an intermediate store for the staging process, allowing custom data scrubbing and transformation tasks to be performed (using either SAP or third-party tools) on a complete extract before it is mapped to InfoCubes. It also provides a method for end users to drill-down to transaction-level data without entering the OLTP system. Administrator Workbench An administration tool for managing and extending the data warehouse environment. It provides a graphical interface for scheduling data loads/ updates and monitoring processing tasks. Graphical tools are also provided for defining and maintaining InfoCubes, InfoSources, metadata, setting security and maintaining a report catalogue. Business Explorer The client component for BW. It consists of two parts: • Report Browser – a web interface that enables the end user to display metadata information about reports, and choose what models to explore • Analyzer – an ad hoc query and analysis interface that uses Microsoft Excel to display data. A BW add-on provides OLAP capabilities directly from the spreadsheet. The report catalogue can be consulted via the Internet using a web browser. If the user wishes to interact with the data, Business Explorer fires up Analyzer, which is really Excel with BW extensions. BW’s OLAP engine is only activated when the data needs to be refreshed or a new view of the data needs to be computed. Data Extractors A set of programs for the extraction of transaction data from R/3 OLTP applications into BW. BW provides extract programs for all the major R/3 applications, including Logistics, Controlling, Finance and HR (human resources). Tools are provided to extend the extractor routines. Initially, the extractor programs pull the entire dataset across; on subsequent extractions, they pull only incremental changes.

Non-SAP data sources must be extracted using third-party or custom extraction tools. BAPIs BAPIs (Business APIs) are proprietary programming interfaces for business applications, which SAP promises will remain stable. There are more than 400 provided by SAP. BAPIs are published at www.sap.com/bapi. BW supports BAPIs for loading data from non-SAP data sources into BW and integrating with third-party applications. SAP has four certified BAPI partners for data extraction – ETI, Informatica, Prism and TSI – and is working closely with a number of front-end OLAP tool vendors for integration. Programmers can include BAPIs in programming languages such as Visual Basic, Java and C, as well as SAP’s own development language – ABAP/4.

Architectural options Full mid-tier architecture SAP BW does not support a full mid-tier MDDB architecture. Light mid-tier architecture BW is a ROLAP-oriented tool and is based on a light mid-tier architecture. Figure 2 depicts the architecture of BW. Data is stored in a star schema in a relational database, either in the BW Server or on a separate database server. BAPIs provide back-end links for R/ 3 transactional systems and other external data sources, and front-end links to Business Explorer clients. When used with R/3, BW Server resides independently of the OLTP applications, although there is some sharing of the kernel technology. Users can also access native R/3 reporting tools. The standard BW configuration uses a central data warehouse. However, it is also possible to create multiple, independent warehouses for specialist needs. The architecture allows consolidation on several different levels and Business Explorer provides a single front-end for one or more BW Servers. SAP plans to develop a BAPI for the extraction of data from BW (but with no official release date). This may result in more architectures where BW acts as the hub feeding several non-SAP datamarts. Desktop and mobile architecture SAP BW does not support desktop or mobile architectures.

Using SAP BW Business content A key feature of the BW philosophy is that the information is organised into meaningful ‘business content’ – a term that SAP uses to describe preconfigured storage, presentation and data extraction objects that are designed with business needs in mind. The objects provided by BW are based on business processes that are executed in the R/3 system, of which there are more than 900.

Figure 2 BW’s architecture

Business Explorer Favourites

Catalogue browser

Reporting and Report builder analysis for Excel

BAPI Administration Business information workbench warehouse server OLAP processor Administration

Metadata Metadata Data InfoCubes Scheduling repository Manager Manager

Staging Operational Monitor engine datastore

BAPI

Non-R/3 production Production OTLP Data Extractor Data Extractor reporting

Non-R/3 OLTP applications R/3-OLTP applications

SAP is uniquely positioned to understand the semantics behind R/3 data. The company therefore views the business content context of BW as its unique selling point. SAP provides a wealth of business content, and plans to add extra content in future releases. There are four main classes of business content: • InfoCubes – SAP supplies more than 20 preconfigured InfoCubes for analysing key sets of R/3 data. The InfoCubes provided are primarily function-based, but some also reflect a task-based view of data. Examples include profitability analysis, market segment analysis, financial overviews, stock inventory analysis and corporate indicator systems • InfoSources – BW comes with 50 preconfigured InfoSources to build InfoCubes from. Each InfoSource is assigned to a R/3 application component

• Data Extractors – data extractor programs are supplied for all the major R/3 modules, including Finance, Controlling, Logistics and Human Resources. SAP also provides customised routines to access data in older R/2 systems or proprietary file structures • report templates – BW includes a range of predefined reporting templates for particular user types, such as production planners, financial controllers, product managers and human resources managers. Task-related reports (or queries) combine information from related InfoCubes and organise them into report clusters which, in turn, form the basis for a channel, in which a so-called ‘business role’ is defined. Templates are also available for commonly required business parameters, such as contribution margin. Metadata repository BW has a central metadata repository that contains information about both the meaning of BW data, and its origins and transformations. The metadata repository is preconfigured for R/3 and is dynamically linked to the enterprise data model. All developer activities are automatically captured in the repository. The repository organises the information into four catalogues: • InfoObject catalogue – all the attributes and measures are described. These InfoObjects can be re-used within multiple InfoSources and InfoCubes • InfoCube catalogue – stores the definitions of InfoCubes (the attributes and measures contained within each InfoCube) • Report catalogue – contains report descriptions and definitions. Using Business Explorer the end user can view these and select reports to open • InfoSource catalogue – as well as the InfoSource definitions, the catalogue stores information about the mappings on to InfoCubes. The Administrator Workbench provides a metadata management tool (Metadata Manager) for maintaining the different catalogues. Building new models in BW Creating new models (InfoCubes) based on previously defined dimensions and measures (generically called InfoObjects in BW) can be easily achieved in the Administrator Workbench. A R/3 system connected to BW provides a wealth of predefined InfoObjects, as well as InfoCubes and InfoSources. All these objects can be easily loaded into BW and referenced in the appropriate catalogue in the metadata repository. However, competitive advantage is likely to require that new InfoCubes be defined which integrate a wider range of data, including non-SAP data. In order to understand how this is achieved, it is first necessary to understand how data is transferred into BW and mapped to multidimensional OLAP structures using a metadata layer. Within BW, there are three layers of representation (source data, InfoSources and InfoCubes), with two levels of mapping.

Mapping source data to InfoSources The first level of mapping is the source data mapped to the InfoSources layer. Extract programs deliver source data in an extract source structure. A source structure can be transaction data (dimensions and measures, which are called InfoObjects) or master data attributes, texts and hierarchies. Multiple extract source structures are mapped to a single transfer and communications structure called an InfoSource. InfoSources are data providers for BW. They contain data, information about the communication protocol to be used when transferring the data and any rules about the data transfer and updates. InfoSources are the main method of feeding data into BW. Preconfigured InfoSources and extraction routines supplied by SAP allow for quick and easy development of InfoCubes based on R/3 data. However, the InfoSources provided are intended to be used as they are, rather than modified. It is possible to create new InfoSources in BW, using the Administrator Work- bench, although this requires familiarity with R/3. Data from other non-SAP sources is loaded using BAPIs. This can be achieved either by users writing applications, the flat file extraction facilities or through the use of third-party ETL vendors that have been certified by SAP. However, the complexity and difficulty of extracting data from systems outside R/3 to load into BW should not be underestimated. The extract, transform and load (ETL) process is widely acknowledged to be the most technically challenging task in data warehousing. SAP’s data extraction capabilities are beyond the scope of this report, and are examined in greater detail in Ovum Evaluates: Data Warehousing Tools. Creating InfoCubes from InfoSources The second level involves mapping InfoSources to InfoCubes. This is achieved using the Administrator Workbench tool. InfoCube designers are provided with a metadata management tool for accessing predefined InfoSources, InfoCubes and InfoObjects in the metadata repository. A hierarchical overview of all the metadata catalogues is provided by Ad- ministrator Workbench. Using this interface, designers can assign InfoObjects to InfoSources to define new InfoCubes. Menu-driven dialogues are provided for defining attributes, properties, transfer and communication structures and accessing further functions. Business logic can also be applied, ranging from simple aggregations to arbitrary calculations on key figures and additional attributes and user-defined functions. Using OLE DB for OLAP to access the data Version 1.2A of BW provides limited support for the use of OLE DB for OLAP to access data. Client applications that use OLE DB for OLAP as a consumer have to use an OLE DB for OLAP ‘wrapper’ object provided by SAP. This wrapper maps the methods of the OLE DB for OLAP objects on to the SAP OLAP API. Only a subset of the MDX grammar specified in OLE DB for OLAP is supported by the wrapper.

Using the Analyzer component of Business Explorer, queries – with dimensions and measures – can be defined from InfoCubes. The data for these is retrieved using the OLE DB for OLAP calls, which are then interpreted by the SAP wrapper. Effectively, Excel is used for the presentation of the information and the processing is carried out by the OLAP server. Using the Business Explorer Business Explorer is the primary front end for BW and is used to: • define queries and save them in reports • analyse data by navigating through the queries • select and manage reports. Defining queries In Business Explorer, a query represents a subset of data that is ‘cut’ from an InfoCube. Queries are defined graphically by selecting characteristics and key figures (InfoObjects) and distributing them via drag-and-drop to filters, rows, columns and user-defined characteristics to create a view of the data. A preview of the results area of the query is also provided. SAP also provides a number of predefined query templates for InfoCubes. Figure 3 shows the Business Explorer interface for defining queries.

Figure 3 Queries interface

Queries are saved as part of an Excel workbook as a report. A report can have multiple queries attached to it, each of which represents a different slice of the data. Analysing data If the user wishes to interact with the data, Business Explorer fires up the Analyzer, which is effectively a local copy of Excel with a BW add-on. Users analyse data by navigating through queries in order to generate different views of the data (known as navigational states). The navigation of the query can include functions such as drill-down, slice-and-dice, sorting and filtering. Selecting and managing reports Reports are accessed by end users via channels, which deliver collections of reports grouped according to topic areas or user roles and business processes. Within a channel, reports can be logically grouped into clusters. BW administrators assign channels and reports to end users from a central InfoCatalog in the Administrator Workbench. The InfoCatalog provides a grouped and structured display for report management. Access rights can also be defined for channels, reports or elements of data within a report. Reports are accessed and managed by end users via a web browser, using the Report browser facility shown in Figure 4. Users can also assemble personal folders of reports. Frequently accessed reports can be assigned as ‘favourites’ for quick and direct access.

Figure 4 Report browser

Future enhancements SAP is developing BW and its partnerships. The next planned release is version 1.2B, due in the first quarter of 1999. The release will mainly include additional business content rather than major technological developments. OLAP-specific enhancements will include drill-through to transaction-level data in OLTP and databases directly from the Business Explorer interface, archiving and data replication, server load balancing and capacity planning functions, and enhanced data visualisation capabilities. SAP plans to extend the range of servers supported to include MVS in early 1999, and AS400 at a future (as yet unspecified) date. The next significant release (version 2.0) is planned for the third quarter of 1999 and will include technology and content improvements. SAP plans to provide BAPIs for accessing data in BW, but these are not due for release until the end of 1999.

Commercial background

Company background History and commercial SAP (Systems, Applications and Products in Data Processing) is the fourth- largest independent software company in the world. The company was founded in 1972 in Mannheim, Germany, by five ex-IBM software engineers. SAP has not changed direction radically over the years and has been successful in extending its market through renewing and expanding its core business application software offering. SAP’s first major product was its mainframe R/2 manufacturing solution. In 1991, SAP released R/3, the first fully configurable client-server ERP system available on Unix. R/3’s business process orientation, which permitted views of management accounts based on multiple business views, was very much in tune with the business process re-engineering movement at the time. This has allowed SAP to successfully pilot R/3 to a leading position in the US and Europe. Ovum’s figures indicate around a 40% share of the software licence revenues for the worldwide ERP market, with more than 17,000 R/3 installations worldwide. SAP BW was first released in September 1998, although the pilot programme started in May 1997. By the end of 1998, SAP claimed around 300 shipments of the product. As expected, almost all of these were to current SAP R/3 users. SAP is publicly held and has its headquarters in Walldorf, Germany, with offices worldwide. The company is structured around 17 industry-specific business units and employs more than 17,000 people. SAP is listed on several exchanges, including the Frankfurt stock exchange, the Swiss stock exchange and the New York stock exchange. Figures for fiscal 1998 show that revenues grew 40% to DM8.4 billion ($4.8 billion). However, pretax profit growth of 15% was well below expectations. This shortfall was attributed to the weak Asian market and the decline in SAP’s Japanese activities. Character and direction SAP has made no secret of its desire for its users to use SAP technology to support as much as possible (if not all) of its business. SAP BW fits neatly into this all-conquering vision, by keeping the data warehouse inside the ‘SAP space’ while allowing data to be imported from external systems; SAP even suggests that non-R/3 users may use BW. Most BW users are R/3 customers. Early adopters include Colgate-Palmolive, Intel, DEC and Bay Networks. Historically, SAP has targeted large companies. The company has long been interested in increasing its products’ attractiveness to the SME market, but has yet to succeed in this aim outside its home market. In order to do this, it is starting to use VARs in the US market. SAP is developing a partner programme for the product at both the back and front end. ETI, Informatica, Prism (recently acquired by Ardent) and TSI have been BAPI-certified to load non-SAP data into BW; being ‘certified’ is primarily a stamp of quality and commitment. At the front end, SAP is working with Business Objects, Arcplan and Cognos, with plans to add IBI to the list. The planned result of the partnership is that tools from these vendors will integrate so that they can seamlessly make use of the InfoCubes built in BW.

Customer support Support SAP offers the same level of support for BW and R/3. This includes around- the-clock telephone hotline and on and offsite support worldwide. Training BW training is available from most of SAP’s subsidiaries worldwide. SAP has only fully translated training materials from German into English. Consultancy services SAP provides consulting services for BW for implementation, application creation and use. Consulting accounts for around 20% of SAP’s revenues and is growing faster than software licence revenues. SAP also has partnerships with global management consultants and system integrators.

Distribution Europe SAP Neurottstrasse 16 69190 Walldorf Germany Tel: +49 6227 747 474 Fax: +49 6227 757 575 Americas SAP America 701 Lee Road Wayne, PA 19087 USA Tel: +1 610 725 4500 Fax: +1 610 725 4555 Asia-Pacific SAP Asia 750A Chai Chee Road 7th Floor Chai Chee Industrial Park Singapore 469001 Tel: +65 446 1800 Fax: +65 249 1818 E-mail: [email protected]

http://www.sap.com

Product evaluation

End-user functionality Summary

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Business Explorer uses Microsoft Excel (with special BW extensions) for the analysis and presentation of data. The interface is easy to use and flexible enough to support a significant degree of ad hoc query and analysis, but lacks the advanced functionality found in more mature OLAP client tools. Preconfigured report templates are provided to quickly build up a catalogue of reports that can be consulted via a web browser. There is no integration with third-party OLAP front ends, and support for the targeted distribution of reports is limited. Finding and understanding the model Finding and loading a multidimensional model Typically, end users do not access models directly; they access reports that contain specific views (queries) of the model data. Graphical access to reports is provided by the Report Browser. Reports can be grouped logically into clusters and stored in public and private directories. ‘Favourite’ reports can be selected from the file system. Search facilities are not provided, but it is possible to display a preview of queries and reports to ease selection. Metadata for end users When a report is opened, it shows metadata such as author details, the query name, when it was last changed, the source InfoCube and a textual description. Annotation by end user Models cannot be directly annotated by end users. Excel features can be used to edit comments to spreadsheet data. Using the model Basic OLAP functionality Standard OLAP functions, such as slice-and-dice and drill-down and drill- across, can be defined using the BW add-on facility that is embedded within the Microsoft Excel worksheet. Exceptions can be set for traffic-lighting applications. Changing the position of members in a dimension level Members within a dimension level can be repositioned, and the sequence of the display can also be sorted in ascending or descending order. Visualising the drill-down hierarchies End users can bring up a full visual representation of the hierarchies.

Drilling down to detailed data Drill-down to records in the OLTP or other source systems is not supported. However, it is possible to drill-down to transaction data that has been extracted to BW’s Operational Data Store. Range of front-end user tools The primary front-end tool is Microsoft Excel. BW supports a subset of Microsoft’s OLE DB for OLAP protocol, but makes no claims about this enabling access by any end-user tools other than Excel. BW can also be accessed by other SAP applications, such as Advanced Planner and Optimizer (APO), SAP Sales and SAP Marketing. Visualising the results BW uses Excel to display results data in tabular and graphical format. There is no integration with GIS mapping tools. Saving and sharing results Designing a report Report design relies entirely on Excel’s formatting and layout features. Multiple queries can be included in a single report. SAP provides a number of predefined templates for specific reporting needs. Publishing a report Reports can be published using channels. Targeted distribution via email Reports can be saved as static Excel files and attached to an e-mail in the usual way. Subscribing to reports BW does not support conditional subscription services.

Building the business model Summary

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SAP provides preconfigured models that are designed for analysing R/3 data. New models based on previously defined dimensions and measures can be easily built in the Administrator Workbench. If there is a need to integrate non-SAP data in models, then new InfoSources will have to be defined. The InfoSources provided by BW are intended to be used as they are; creating new ones requires designers to have strong SAP development skills and use third- party tools.

Basic design Design interface InfoCubes can be easily designed using the menu-driven tools provided by Administrator Workbench. If predefined dimensions and measures are available then this is a straightforward process. Otherwise, developers will need to use additional SAP and third-party tools to define upstream integration with data sources. Visualising the data source The data source is only visualised upstream, when defining the InfoSources. It is not possible to bring up a sample of data when creating InfoCubes. Universally available mapping layer Dimensions and measures defined in BW’s metadata layer can be re-used across models. However, a universal mapping layer is not directly supported. Prompts for metadata Designers are prompted to include descriptive metadata, such as owner details, contact details, description and rationale when building InfoCubes. Building the dimensions Selecting columns for the dimensions The preconfigured InfoCubes automatically map dimensions to source R/3 data. Mapping dimensions to non-SAP sources is performed when creating new InfoSources. This requires a familiarity with R/3, ABAP and third-party tools. Selecting the members shown in a dimension level Once a dimension has been created, members in a dimension level can be selectively included or excluded using point-and-click. Defining a dimension hierarchy Dimension hierarchies can be defined graphically. Custom, alternative, unbalanced and time-dependent hierarchies are supported. Time dimension Standard fiscal year variants and quarters are supported. Custom and dynamic time periods (for example, the last six months) can also be defined. Annotating the dimensions Short name and long text descriptions can be assigned to dimensions. End users can decide whether short or long text is displayed on screen. Default level of a dimension hierarchy A default view of dimensional hierarchy is established based on the first navigational state of a query definition.

Defining the measures Calculated measures A formula editor is provided to create calculations. Standard arithmetic operators and logical operands can be included in a formula workspace using drag-and-drop. More than 100 metrics are included in the preconfigured InfoCubes. Support for multiple measures with a set of dimensions Multiple measures can be included with a set of dimensions. Multiple designers Multiple designers BW does not provide any special support for multi-designer environments. However, developers can use the R/3 Basis system to provide change management, check-out/in and multi-user locking facilities for the repository. Support for versioning Version management is provided by CTO.

Advanced analytical power Summary

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BW supports a useful collection of ready-to-use functions that can be used for general business analysis. More than 100 metrics are included in the preconfigured InfoCubes. End users rely on Excel functions for forecasting, and write-back is not supported for ‘what-if’ applications. There is limited support from SAP for the extraction of data from BW for use in third-party analytical tools. Third-party tool integration BW integrates with Microsoft Excel. However, there is no integration with other third-party analysis tools. Defining specialised models Ranking and sorting Ranking and sorting functions are available including top/last n and top/last n%. Sequence functions are also provided for 80/20 analysis, including cumulative sum, tertiles, quartiles, classification and dual classification. Mathematical methods There is support for standard arithmetic operator functions and more advanced methods, such as trigonometric functions. Financial functions Financial functions are provided, including complex currency translations (the euro, exchange rate types for bank buying/selling rates and EMU legal directives), ABC analysis and internal business volume elimination.

Statistical models Support is provided for a number of statistical functions, including percentage share, ratio, correlation, percentage difference and variance. Trend analysis Average over period sum/count functions can be used for establishing trends. Otherwise, BW relies on Excel for trend analysis functions. Simple regression BW relies on the linear regression functions provided by Excel. Time-series forecasting Simple time-related comparisons and trends are supported via Excel. However, there is no support for advanced time-series forecasting methods. User-definable extensions Simple functions can be defined locally in a model using the formula editor or the Excel macro functions. However, a procedural language is not supported. Write back for ‘what-if’ analysis Write-back to models is not supported. Incorporating non-numerical data SAP BW is designed for the analysis of numerical data only. Data mining There is no support for data mining.

Web support Summary

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Business Explorer offers a web interface, but only for browsing the metadata catalogue to see what reports are available. Ad hoc query and analysis is via Analyzer and requires a copy of Excel (with a BW add-on) to be installed locally on the client machine. Effectively, there is no OLAP support via the Web. However, if required, it can be provided by tools from some of SAP’s partners. End-user functionality via the Web Functionality of web access to explore models The web interface only allows users to browse through the metadata catalogue to select reports. OLAP functions are not available via this web interface. Supports both registered and unregistered web access All SAP clients must be registered named-users.

Range of users supported by the web interface The web interface is limited to browsing through a catalogue of predefined reports. Creating models via the Web Modelling capabilities via the Web are not supported. Distributing via the Internet and the Web Apart from e-mailing a static report via the Internet, there are no special publishing and distribution facilities provided by BW.

Management Summary

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BW provides a central and easy to use administration tool to supervise and control the operation of the warehouse. Strong facilities are provided for managing data loads and updates and monitoring processing in the warehouse environment. To get the best from BW, administrators should have a good knowledge of R/3 and third-party tools, particularly if additional SAP and non-SAP data sources are used. Security is based on standard R/3 authorisations. Management of models Separate management interface The Administrator Workbench provides a graphical overview of InfoObjects and corresponding source systems, InfoSources and InfoCubes. These objects can be easily managed using drag-and-drop functions. Security of models Access rights (authorisations) can be defined for queries, models or individual InfoObjects. Security can be modelled freely for all elements in a model, right down to field values. Query monitoring The ‘monitor’ facility provides detailed statistics on the frequency of query execution and usage of summary levels. BW supports its own statistics InfoCube for analysing and reporting on the collected data. Management of data How persistent data is stored (not scored) Data is stored persistently in relational database (in a star schema). The database can reside in the BW Server or a separate database server.

Scheduling of loads/updates SAP BW’s Scheduler facility is based on R/3’s scheduling system. It provides a graphical interface that is used to define extract and load schedules according to time and date criteria (hourly, daily, weekly, monthly or other periods). Full or delta loads/updates are supported. Event-driven scheduling It is possible to schedule a data load or an update to the data warehouse based on external events (for example, a specific transaction in R/3). Failed loads/updates The ‘monitor’ facility supervises the load and staging processes. It provides detailed statistics on current and completed load jobs and notifies the administrator of exceptions such as failures. An ‘assistant’ feature helps with the analysis of failed and incorrect data loads/updates. Distribution of stored data Data can be stored in separate BW Servers or remote database servers. No data is stored on the client. Sparsity (only for persistent models) Sparse data handling is the responsibility of the relational database, and is not an issue in BW. Methods for managing size Typically, the size of InfoCubes runs into tens of gigabytes, rather than hundreds. However, the maximum size is limited only by the RDBMS. BW administrators can decide on the number of aggregate tables created to control the size of the database. In-memory caching options R/3’s extensive memory management and caching functions are available for fine-tuning performance. Informing the user when stored data was last uploaded All reports are time-stamped for each new data load. Management of users Multiple users of models with write facilities Write-back to InfoCubes is not supported. However, certain SAP applications that work with BW do allow write-back to the source operational systems. User security profiles User security is based on individual and group authorisation profiles. BW uses the same authorisation schema used in R/3. Query governance SAP BW does not support query governance.

Restricting queries to specified times It is not possible to restrict usage patterns and queries to specific times of the day. Management of metadata Controlling visibility of the ‘roadmap’ SAP authorisation checks can be used to restrict access to any functions or objects in the SAP BW environment. User authorisations are summarised in the form of profiles. Different authorisations are required for working with the Administrator Workbench and the Business Explorer.

Adaptability Summary

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Dimensions and measures can easily be added to models. All these definitions are stored and maintained in a single metadata repository, allowing for easy re-use within multiple models. If SAP data is being used, structural changes to the data source are automatically synchronised within BW. How- ever, if customised ABAP programs are used for accessing non-SAP data, the maintenance overheads can rise considerably. Change in business requirements Adding new dimensions to a model New dimensions (characteristics) can be added to a model by selecting InfoObjects from the InfoObject catalogue. Re-use of dimension definition InfoObject definitions can be re-used across multiple InfoCubes. Adding new measures to a model New measures (key figures) can be added to models, either as a predefined measure or a local calculation. However, there is no support for tracking the changes made to the base model. Re-use of calculated measure definition Measure definitions are stored in the InfoObject catalogue and can be re- used across multiple InfoCubes. Additionally, key figure templates can also be applied to InfoCubes. Changing the architecture to reflect business needs There is no support for changing the architecture to a full MOLAP mode. Desktop and mobile architectures are not supported.

Changes to data sources Keeping the data source and model schema synchronised Metadata from R/3 systems is automatically transferred to BW. The metadata repository is updated every time there is a change in the underlying data source. Automatic updating of members in a dimension If InfoObjects have changed in the source system, the changed properties need to be transferred in BW using the appropriate maintenance dialogue. Dimension members are automatically updated during the update process. Metadata Synchronising model and model metadata InfoCubes are automatically synchronised with the metadata repository. However, descriptive metadata assigned to InfoCubes must be maintained manually. Impact analysis There is no direct support for anticipating the consequences of changes in data sources to models and reports. Metadata audit trail (technical and end users) The metadata repository provides a history of the metadata. Access to upstream metadata Metadata from a R/3 system can be imported into BW. Access to metadata from data warehousing tools is via BAPIs. SAP has four certified partners in this space: ETI, Informatica, Prism and TSI.

Performance tunability Summary

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BW resides on its own dedicated server and is separate from the OLTP system and other source systems. Scalability is achieved using multipass SQL, distributed processing and SMP technology. The caching mechanisms within the tool have also been carefully designed to maintain performance. Overall, getting the best performance from BW can be expensive, although justifiable. ROLAP Multipass SQL BW automatically generates multipass SQL. Options for SQL processing Data processing is optimised between the OLAP Processor and the BW database.

Speeding up end-user data access Once an InfoCube has been queried, it caches the data in memory. The cached data can be stored persistently for subsequent access rather than (re)querying the InfoCube. Aggregate navigator The Data Manager maintains aggregations to speed up queries. An aggregate optimiser determines the best fit aggregate tables that satisfy the query. Administrators can also add or remove summary levels according to usage patterns. MOLAP BW is not a MOLAP tool. Support for multiple users A single BW Server can support around 200 concurrent users. Multiple application servers can be added as the number of users grows. Processing Use of native SQL to speed up data extraction BW supports native SQL generation for accessing its relational database. Distribution of processing Multiple BW Servers can be connected. However, there is no support for intelligently balancing processing across the servers. SMP support SAP BW supports SMP parallelism.

Customisation Summary

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SAP BW is positioned as a ready-to-go data warehouse. Customers can use SAP’s proprietary ABAP fourth generation programming language for application development. However, ABAP coders are a much valued species and are available only at high prices. Accessing R/3 data for decision support using customised ABAP programs also has the danger of high maintenance overheads. The SAP BAPIs can be accessed by third-party development tools. Customisation Option of a restricted interface There are no facilities for providing restricted interfaces. Ease of producing EIS-style reports There is no direct support for producing EIS-style reports. This can only be achieved using third-party development tools.

Applications Simple web applications There is no direct support for developing web applications. Development environment R/3 customers can use SAP’s proprietary ABAP/4 fourth generation programming language. SAP provides ABAP Workbench with the SAP R/3 system, which is a development platform for client-server applications. It includes a repository, editor, dictionary, function builder, screen/menu paint- ers and tools for testing and debugging R/3 applications. ABAP can be used to communicate with both the application server layer and the client. All BW objects are accessible to the ABAP Workbench. Use of third-party development tools BAPIs can be accessed from development environments such as Visual Basic, Visual J++ and Visual Age.

Deployment

Platforms Client SAP BW Business Explorer runs on Windows 95 and Windows NT. A local copy of Microsoft Excel is required on the client to run Analyzer. Server SAP BW Servers and database servers run on Windows NT and all the major versions of Unix.

Data access SAP BW is primarily designed to work with SAP R/3 data or it can easily access data in other BW systems. SAP can provide customised routines to enable customers to access data in R/2 systems and from content providers such as Dun and Bradstreet. There is a flat file load interface for feeding in flat files. Data from other non-SAP sources is loaded using BAPIs. This can be achieved either by users writing applications or through the use of SAP’s partners that have been certified, such as ETI, Informatica, Prism and TSI. The first three of these are evaluated in Ovum Evaluates: Data Warehousing Tools.

Standards SAP BW supports SAP’s proprietary BAPIs to feed data into BW. For accessing data in BW, SAP provides a subset of Microsoft’s OLE DB for OLAP protocol for accessing data. This is used by Microsoft Excel when running as Business Analyzer.

Published benchmarks SAP BW does not have any published OLAP benchmarks.

Price structure Pricing for SAP BW is based on named users. The entry level cost for BW Server is DM250,000 (approximately $144,200) for 250 users. Existing SAP users can upgrade to SAP BW for DM1,000 ($575).

Summary

At a glance ...... 2 Terminology of the vendor ...... 3 Ovum’s verdict...... 4 Product overview ...... 5 Future enhancements ...... 13

Commercial background

Company background ...... 14 Distribution ...... 15

Product evaluation

End-user functionality ...... 16 Building the business model...... 18 Advanced analytical power ...... 19 Web support ...... 21 Management ...... 22 Adaptability ...... 24 Performance tunability...... 25 Customisation ...... 27

Deployment

Platforms ...... 28 Data access ...... 28 Standards ...... 28 Published benchmarks ...... 28 Price structure ...... 28 At a glance

Developer Seagate Software Information Management Group, Scotts Valley, CA, USA Versions evaluated Seagate Holos version 7.0 Key facts • A client-server application development environment for building OLAP applications • Runs on Windows NT, Unix and VMS servers; clients run on Windows 3.1, Windows 95, Windows NT and Macintosh • Part of a comprehensive suite of business intelligence applications being assembled by Seagate Software Strengths • A functionally rich OLAP tool with strong support for custom application development • Supports a range of OLAP architectures, and provides flexible data storage and access options • Supports an extensive set of advanced analytical functions Points to watch • Little ‘out-of-the-box’ functionality • Can be complex to set-up and manage – considerable thought has to go into the model and application design process • End users rely entirely on IS to build models and OLAP applications Ratings

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End user functionality

Building the business model

Advanced analytical power

Web support

Management

Adaptability

Performance tunability

Customisation Terminology of the vendor Agent Used by Holos to perform time-consuming, complex or repetitive tasks in the background, as batch programs. Holos desktop A custom end-user interface built using the Holos development tools. Usu- ally called a Holos application. Model A collective term for a combination of structures that store data and rules which define calculations to be performed on the data. Open OLAP An integration strategy that allows Holos to incorporate data stored in third-party multidimensional databases into Holos structures. Report A common way of presenting information to the user of a Holos application. A report is a view from a particular perspective into a multidimensional model. It can also contain other types of information such as graphs, images and OLE objects and controls. Rules Holos rules define relationships between dimension members and calculated measures. An example of a rule could be a calculation such as variance. Rules are generally held in a ruletable. Structures Define dimensions and their relationships and include information on how the underlying data is stored and retrieved. Holos supports four types of structure: memory-based, disk-based, relational and ‘Open OLAP’. A structure corresponds to Ovum’s definition of a business model. Ovum’s verdict

What we think Seagate Holos is aimed primarily at application developers. Holos’s powerful 4GL is purpose-designed for building OLAP applications and provides a broad range of development options. The graphical development tools offer some strong features for building sophisticated OLAP applications for specialist analytical requirements. One of Holos’s greatest strengths is its breadth of functionality. The tool supports a wide range of OLAP architectures, and provides one of the strongest sets of advanced statistical analysis and forecasting functions in the OLAP market. Scalable performance is also achieved through the use of optimised multi-cubes and distributed and parallel processing. However, under the covers, Holos is a complex OLAP product, and its learning curve is steeper than most other OLAP products. The compound multi-cube architecture can be complex to set up and there are limited graphical management facilities to maintain the system. The Holos Web Gateway is Seagate Soft- ware’s first attempt at web access. Although it provides a functional web interface, its HTML interface is far from elegant. Holos is targeted at the high-end of the OLAP market, which consists of large corporates seeking to deploy OLAP throughout the enterprise. Any purchase decision therefore requires a strong commitment to the Holos development philosophy, not to mention a significant entry-level investment. The main challenge for Seagate Software is to straddle the requirements of proprietary language-based development, and the provision of ‘ready-to-use’ tools that provide greater freedom for end users and remove the reliance on developers and IS to deliver OLAP applications.

When to use Holos is suitable if you: • are willing to make a strategic commitment to Holos for decision support • require highly customised OLAP applications • have in-house development skills to exploit • want applications with advanced statistical analysis and forecasting • require flexible models built from disparate data sources. It is less suitable if you: • are looking for an ‘out-of-the-box’ OLAP solution that is quick and easy to implement • have users that require flexible, ad hoc business modelling • do not wish to commit yourself to a significant development effort • do not want IS involvement in supporting OLAP applications. Product overview

Components The main components of Holos 7.0 are: • Holos development environment • Holos Agents • Seagate Worksheet • Holos Web Gateway. Figure 1 shows the primary functions of Holos components and whether they run on the client or the server. Holos is a client-server application development tool for building OLAP applications. The tool is based on a powerful 4GL (called Holos Language), which is purpose-built for developing business intelligence applications; application components are created as Holos Language scripts. Holos is a flexible hybrid OLAP product that supports relational and multidimensional data storage options, with an interesting overlaying option. OLAP processing and code development is usually completed on the server. Holos extensively uses agents to automate a number of processing tasks. At the client end, Holos relies mainly on the development of custom applications to access data and perform OLAP analysis. An ‘out-of-the-box’ spreadsheet-style interface is provided. Seagate Crystal Reports and Seagate Info support read-only access to Holos. Holos can also be accessed by ODBC-compliant applications. The Holos Web Gateway provides access to the Holos server via a web browser; it supports HTML and Java tools. Holos development environment The Holos development environment provides a number of graphical development tools to define models, interfaces and other elements of a Holos application. The main interface for development is the Application Manager. This is a graphical tool for creating and maintaining Holos applications aimed at developers. It brings together the various elements (objects) of a Holos application. It acts as a ‘shell’ for other Holos application development components and provides support for multi-developer environments. Holos application objects are defined using one or more of the graphical Holos design tools, the most important of which are listed below.

Figure 1 Component details

Data storage Application OLAP analysis Web access development

Client Holos development Holos development Holos clients Java Worksheet environment environment Seagate Worksheet

Server Holos development Holos development Holos Agents Holos Web Gateway environment environment Data Manager This provides a GUI point-and-click interface for creating Holos data structures. Structures define dimensions and their relationships. They also include information on how the underlying data is stored and retrieved. A Holos ‘model’ is a combination of a structure and a collection of business rules that define further relationships between dimensions and additional calculated measures. The Data Manager is also used to define the loading of data into the structures from relational databases, flat files and other supported data sources. Hierarchy Manager Used to define simple parent/child relationships between dimension members. It is also used to define additional drill hierarchies using a graphical editor. Data Filter Used to define selection criteria for use with Holos reports, in the Worksheet front-end tool or as standalone processes. Report Designer A tool for designing multidimensional reports, and enabling OLAP functions to be defined within them. Desktop Designer Used to design custom application interfaces and desktops. It integrates reports and application components in a single user interface. Developers can also use the tool to tailor their own development workspaces. Dialogue Designer Used to create Visual Basic-style client dialogues for use in applications. Seagate Worksheet An end-user tool that presents a spreadsheet-style interface for ad hoc analysis of models. The Worksheet is designed for power users that require flexible ad hoc OLAP capabilities. The Worksheet interfaces directly with models stored on the server. It can access all the analytical and data manipulation functions provided by Holos. A Java version is also available. Holos Web Gateway The Holos Web Gateway uses CGI to connect with a web server to provide web access to Holos applications and data. The Gateway provides HTML and Java interfaces: • the HTML interface provides access to predefined Holos reports; a transformer facility converts Holos Language code into HTML and presents the report to the web browser as a series of ‘dynamic’ HTML pages. Users can perform standard OLAP functions such as drill-down using HTML tags • the Java implementation provides a web interface for the Seagate Worksheet. It uses Java applets and a Corba architecture to download a structure and provide more interactive OLAP functions, including slice- and-dice. Charting and graphing functions are available via a Java applet. Holos Agents An important part of Holos is the use of Holos Agents to automate batch processing tasks that are hidden from end users. An Agent toolkit provides developers with functions to create, manage and use Agents. The Agent Dialogue System provides users with a set of dialogues for setting up and modifying Agents. Holos supports a number of predefined Agents, for OLAP analysis, data mining and data loading.

Architectural options The Holos development environment supports a wide range of application development styles and implementation architectures. This flexibility is possible due to the number of data storage and processing options that can be configured by developers when designing OLAP applications. Compound multi-cube OLAP Before explaining the different architectural options supported by Holos, it is important to first understand the compound multi-cube architecture that underpins the tool and OLAP applications developed using it. Hybrid storage options Holos is a hybrid OLAP tool, supporting multidimensional, in-memory or relational storage options. A Holos business model (structure) can consist of different types of structures. This means that ‘virtual’ structures can be created by referencing one or more physical structures. The structures can be any of four physical kinds with an interesting over-laying option: • disk-based structures; these are multidimensional data files stored persistently on the Holos server. There are two forms of disk-based structure – shared and hashed structures – that provide optimised storage for dense and sparse data sets respectively • relational structures; these map to data stored in RDBMS tables. In this case, the structure of the cube is simply a mapping layer that holds information about how requested data should be retrieved • memory-based structures are in the form of ‘micro-cubes’ created on-the- fly whenever data is needed. They are typically used for relatively small amounts of data. Memory-based structures are created and held on the Holos server and viewed from the client • open OLAP structures, which are sourced from third-party multidimensional databases such as Essbase and Microsoft SQL Server OLAP Services. A strong feature of the architecture is the ability to create compound structures under the control of the Holos language at runtime in the application, perhaps in response to end-user actions. Racking and stacking Different types of structures can be joined using racking and stacking, as shown in Figure 2. Figure 2 Structure joining

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Racking occurs when multiple cubes are joined in parallel along a ‘backbone’ dimension. For example, three cubes containing sales data dimensioned by region, product and line item can each hold data for different months. These three cubes can be ‘racked’ together by introducing a time dimension to act as a backbone – each of the monthly structures would contribute the data for one field in the time dimension. The resulting compound cube behaves like a standard cube dimensioned by region, product, line item and time, but would contain no data of its own; instead it would provide pointers to data in the base cubes. Stacking is a less obvious form of joining. It connects two identically dimensioned structures in a series – in this case, data is always read preferentially from the first structure. When data is read from the resulting compound cube, Holos first interrogates the top cube of the stack. If the data is found, it is returned: if not, the bottom cube is interrogated. In contrast, when data is written to the compound cube, it is only ever written to the top cube. Full mid-tier architecture This is the ‘natural’ architectural configuration for Holos where data retrieval, manipulation and formatting are done on the server, with the client interface handling user requests and presentation issues. The full mid-tier architecture stores models and data in a mid-tier server. OLAP calculations and processing are done on the server. Holos disk structures represent the MDDB, although an open OLAP structure could reference an Essbase or Microsoft SQL Server OLAP Services multidimensional database. Typically, the MDDB is built using multiple structures (multi- cubes) that can be linked together to create a ‘virtual’ structure. The client interface can be an application created using the Holos development tools, the Seagate Worksheet, an Excel add-in or a web browser (if the Holos Web Gateway is also running on the server). Light mid-tier architecture Holos’s ROLAP functionality supports a lighter mid-tier architecture through its support for relational structures. These structures map to data stored in relational tables on a database server, and provide information about how requested data should be retrieved. SQL queries are executed at runtime and data is cached on the server as a (hashed) multidimensional structure for further analysis. There is the same range of client interfaces as with the full mid-tier architecture. Desktop architecture A cut-down, standalone version of Holos is provided to support a two-tier desktop architecture. This can be configured to work directly against an RDBMS and carry out processing on the client machine. Mobile architecture The standalone version allows Holos applications to be run on a laptop computer. A structure can be downloaded locally for offline analysis.

Using Holos Holos is primarily an application development environment. As such, it clearly defines roles and tools for developers and end users: • the development environment and design tools provided are geared towards experienced application developers with strong programming skills. Typically, developers create the business model and the application interface, including OLAP functions and access rights • end users access models assigned to them via a custom OLAP interface, or by using the Seagate Worksheet interface. Principal development concepts It is easier to understand the Holos approach to OLAP application development if three important concepts – structures, rules and models – are clarified. •A structure is both a storage type and a definition of a set of dimension and hierarchies. The structure is, in essence, a metadata layer that maps dimensions, dimensions members and aggregation hierarchies on to a physical storage format. Structures can be implemented in a number of different ways. Different structures can be linked within a compound structure to appear as a single structure. • Holos rules define relationships between fields (dimension members and measures). A rule may, for example, define a calculated measure such as variance. A rule may also define the derivation rules for a hierarchical relationship between members. For easier management, a set of rules can be held together in a ruletable. •A model is a collective term for a combination of structures (which store data) and rules (which define calculations to be performed on that data). Models are defined by declaring their constituent objects (structures and rules). Holos provides automatic validation of models to ensure rules are correctly applied. It also ensures that rules are always implemented in the correct order (so that any dependencies between calculations are handled correctly). A model can have a number of structures and ruletables, and a structure can be used with different models. Developing a custom OLAP application A Holos application is defined as a series of interconnected ‘objects’, which are linked together using a hierarchical browser facility in the Application Manager. This is shown in Figure 3. The hierarchical browser uses a dataflow metaphor to represent the links between application items. Each block in the browser represents some functionality within the application. The first ‘object’ within the application will typically be a ‘data load’ module that has been defined in Data Manager. Within Data Manager, this ‘data load’ object will itself be made up of a number of steps that define a data source and a Holos data structure. Developers use the other Holos graphical tools, such as the Desktop De- signer, Report Designer and Dialogue Designer, to define the various components of an application, but the Holos language remains an important part of the development philosophy. All the graphical tools generate Holos language scripts, which can be further customised or run independently. Figure 3 Hierarchical browser

Designing a Holos report Structures and models are used to build a report in the Report Designer. They can also be used directly within Holos language programs. Structures are also used in the Data Filter module, which is used to define selection criteria for use in reports. A filter is built up as a hierarchical set of procedures defined using dialogues and pick lists. Analysing a model using Seagate Worksheet As shown in Figure 4, Seagate Worksheet provides a spreadsheet-style, multidimensional analysis environment. The Worksheet can be used by end users and developers to build prototype models and reports. New rules can be easily added from within the worksheet to enhance a model. All the analytical functions provided by Holos are available directly from the worksheet interface via drop-down lists. Figure 4 Analysis environment

A worksheet view can also be saved to provide the basis of a report definition, which is tailored in the Report Designer tool. Future enhancements Seagate Software plans a number of enhancements to Holos with two major releases. • The ‘Tasman’ release, due in early 1999, will provide a number of significant enhancements to Holos’s web offerings and its development environment. • The ‘Magellan’ release is scheduled for the second half of 1999 and will focus on making Holos structures more scalable. It will also provide greater management capabilities, specifically a toolkit for implementing application and user security and a GUI tool for administering multiple OLAP servers. Seagate Software is also endeavouring to integrate its OLAP products more closely to provide a business intelligence suite. The next version of Seagate Info, code-named Polaris and scheduled for release in December 1998, will allow Holos applications to be run directly from its interface. Commercial background

Company background History and commercial The original developer of Holos was Holistic Systems, a British company formed in 1987. In June 1996, Seagate Technology purchased Holistic Sys- tems for $84 million as part of its strategy to build up a $1 billion software arm. Seagate Technology is perhaps best known as a manufacturer of disk drives, but has been building up its software business. It acquired Crystal Services in 1994, which developed the now ubiquitous Crystal Reports. This was followed by Holistic Systems and several network storage management software products. Together, these companies now form Seagate Software, a wholly-owned subsidiary of Seagate Technology. The business intelligence technologies have now been brought under the wing of Seagate Software’s IMG. Seagate Technology is a publicly held company with revenues of $6.8 billion. Seagate Software’s revenues for fiscal 1998 grew 35% to $293 million. Seagate Software is based in Scotts Valley, California, US, and employs 1,800 people. It also has representation in 40 countries worldwide. Character and direction Seagate Software is combining the technologies of its business intelligence and its network and storage management units under a framework called ‘Enterprise Information Management’ (EIM). The EIM initiative aims to provide enterprise customers with end-to-end information delivery, analysis and availability. Hence, Seagate wants its customers to think of its back-up and other management software products in the same light as its OLAP, query, data mining and similar products. The plan involves some integration of its product range, specifically the reporting and OLAP analysis capabilities of Seagate Reports, Seagate Info, with the development capabilities of Holos. Seagate Software is attacking both the low and the high ends of the business intelligence market. One of the challenges facing the company is to accommodate the different channel approaches for its OLAP products: • high volume and VAR-oriented, for its low-end reporting and OLAP analysis tools • direct sales and strong customer focus, for large corporates deploying OLAP on an enterprise scale.

Customer support Support Help-desk support is provided through all local offices via the telephone or the Web. Training A range of Holos courses are provided for developers and end users. Develop- ers may need some consulting support in addition to training to establish first links between Holos structures and the underlying database. Computer- based training is also available. Consultancy services The worldwide professional services division is growing rapidly. Services provided can be basic (installation or start-up) or can scale up to address enterprise issues such as requirements analysis and application design. Seagate also has referral partnerships with management consultants.

Distribution North America Seagate Software Information Management Group 840 Cambie Street Vancouver British Columbia V6B 4J2 Canada Tel: +1 604 681 34 35 Fax: +1 604 681 29 34 Europe, Middle East and Africa Seagate Software The Broadwalk 54 The Broadway Ealing London W5 5JN UK Tel: +44 181 566 2330 Fax: +44 181 231 0600 Asia-Pacific Seagate Software IMG Australia Level 9, 42 Alfred Street Milsons Point New South Wales Australia Tel: +61 2 9955 4088 Fax: +61 2 9955 7682

Seagate Software also has Asia-Pacific offices in Singapore and Japan covering local Asia-Pacific territories.

http://www.seagatesoftware.com E-mail: [email protected] Product evaluation

End-user functionality Summary

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Core OLAP analysis is well supported via the Seagate Worksheet or custom application interfaces. The Worksheet is well suited to power users wanting to take full advantage of Holos’s advanced forecasting and modelling capabilities from a ‘no-nonsense’ spreadsheet-like interface. However, Holos is a tool for IS developers building applications for diverse user requirements. Holos supports report distribution via the Web or by integrating with reporting tools such as Seagate Info. Holos data can also be exported to Lotus Notes databases as a shared resource, for distribution to group working environments. Report subscription services are not provided. Finding and understanding the model Finding and loading a multidimensional model Access to specific models is generally defined by the developer of the application and is entirely dependent on the user’s workspace definition. Metadata for end users Most of the metadata about models in Holos is aimed at developers only. Annotation by the end user A ‘notes’ function allows users to attach descriptions to dimensions and cells in a model. A flag can be displayed if a note is attached. Using the model Basic OLAP functionality A range of OLAP functionality (drill-down, slice-and-dice and pivot) can easily be built into a custom application using the Holos development tools. Seagate Worksheet provides a spreadsheet-like interface, from which core OLAP functions are also accessible. Changing the position of members in a dimension level A range of filtering options is available for ordering the position of values in a dimension level. Visualising the drill down hierarchies A dimension ‘Field Picker’ selector provides a visualisation of the drill-down hierarchies, and the current position in it. Drilling down to detailed data Developers can give end users the option of drilling through to transaction- level data. This must be explicitly defined when the original structure is created – Holos saves the SQL statement and column/dimension mapping information in a script. Range of front-end user tools The Holos Open Client Interface makes Holos data available to any ODBC- compliant desktop application. Custom applications developed using Visual Basic can also access data from the Holos server. An Excel add-in is also provided. Seagate Crystal Reports and Seagate Info can access reporting data from Holos, though the interface is read-only. Visualising the results Holos supports a comprehensive gallery of standard business charts and graphs. Multiple charts and data can be displayed on a report page. Double- clicking on a chart item will allow drill-down. Holos provides its own graphing capabilities. It also integrates a third-party graphing engine (licensed from 3-D) to offer a wider range of data visualisation options. Saving and sharing results Designing a report Holos provides a Report Designer tool which allows point-and-click creation of reports in a graphical, production-style environment. Developers and end users can include OLE, sound and video clip objects in reports. Publishing a report Holos reports can be distributed using external tools such as Seagate Info. Reports can also be published and distributed in HTML format using the Web Gateway. Integration with Lotus Notes allows Holos reports to be sent to a Notes database (as a shared object) for distribution into group working environments. Targeted distribution via e-mail Holos reports can be e-mailed to multiple users from within the Holos client interface. Reports can be sent as static report views or ‘viewpoints’ that direct recipients to specific parts of an application. Holos integrates with Microsoft Mail (MAPI), Lotus cc:Mail (VIM), Unix Mail and VMS Mail e-mail systems. There is no direct support for the creation of dynamic e-mail lists; this requires Holos language scripts to be written and interfaced through the supported mail APIs. Subscribing to reports Typically, Holos reports are dynamic and are executed at runtime. There is no support provided for subscribing to reports. Building the business model Summary

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In Holos, business models are built exclusively by IS or developers. These users are well supported by the Holos language and a number of graphical tools for defining structures, hierarchies and adding calculations to models. The combination of structures, models and rules provides tremendous flexibility for designing a business model, and quite complex business models can be built by overlaying different types of structures. But a single view of the various types of structures and dimensions available would ease the process considerably. Basic design Design interface The Holos tools provide an easy-to-use point-and-click interface for creating structures, dimensional hierarchies and calculations. However, there is no single view of the various structures and dimensions that are available for use in applications. Visualising the data source When building the structure, the database, flat file or source schema can be viewed on-screen, and a sample of data displayed. Universally available mapping layer There is no direct support for providing end users with a universal mapping layer. Prompts for metadata Developers and end users are not automatically prompted to provide additional metadata during the model design, application development or report design process. Optional metadata can be included. The metadata can be as detailed as required, and can be stored with the object concerned or linked to a database table. Building the dimensions Selecting columns for the dimensions Columns can be chosen selectively via point-and-click by using the SQL Select Builder tool. Selecting the members shown in a dimension level Dimension members are chosen using the graphical SQL Select Builder tool. Defining a dimension hierarchy The Hierarchy Manager provides a visual interface for defining dimension hierarchies and drill-down paths. Multiple hierarchies may be specified within dimensions based on summary consolidations or average consolidations. Holos also supports unbalanced hierarchies. Time dimension Holos provides a utility to simplify the process of creating time dimensions. Standard time periods such as years, quarters, months and days can be defined. These can be customised to reflect user-defined time periods. Stand- ard operators for time analysis, such as lead and lag operations, are also provided. Annotating the dimensions Dimension names can have two forms: a symbolic name for use within the Holos language; and a long name that provides a fuller and more meaningful description to end users in worksheets and custom applications. Default level of a dimension hierarchy Models can be designed to default to a specified dimension level when it is opened or saved in an application. Defining the measures Calculated measures The Holos language provides arithmetic, conditional and looping constructs, and many intrinsic functions for the manipulation and calculation of multidimensional data. The Worksheet provides its own functions and operators, including sum, avg, min, max, random, variance, exponential and square root. Support for multiple measures with a set of dimensions Multiple measures can be assigned to a set of dimensions. Measures can also be arranged in a hierarchy. Multiple designers Multiple designers Holos supports a built-in file control system that provides standard check- out/check-in facilities. Support for versioning Holos supports its own version control system, and can also link to external versioning and change management systems.

Advanced analytical power Summary

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Holos provides an extensible set of advanced analytical functions for statistical analysis, trend analysis and time series forecasting. Write-back is also supported for ‘what-if’ analysis. End users have the flexibility to apply a number of specialised pre-built functions to model data directly from a custom application or the worksheet interface. The Holos language also supports advanced data mining capabilities, though the integration of this technology requires considerable programming effort. Holos does not help analysts to interpret results of analyses. Third party tool integration An Excel add-in is provided. However, there is no integration with specialist third-party analysis tools. Defining specialised models Ranking and sorting Holos can rank values in either ascending or descending order (top/bottom-n selections). It also supports Pareto analysis for more sophisticated classification. Mathematical methods Holos supports advanced mathematical techniques, such as simultaneous equations, linear and quadratic functions, algebra and nominal optimisation. Financial functions Holos provides a number of advanced financial consolidation routines, including discounted cash flow, internal rate of return, net present value and depreciation. Statistical models The Holos language supports an extensive set of statistical modelling functions including moving averages, smoothing, range, standard deviation and variance. The more powerful statistical functions assume an understanding of statistics if the most is to be made from the results. Trend analysis Holos provides a number of sophisticated curve fitting techniques for analysing trends. These include exponential, geometric, linear, quadratic, modified exponential, modified hyperbolic, polynomial quadratic, rational and semilog. Simple regression Holos provides a range of regression functions for forecasting, including: multiple linear regression, univariate and multivariate regression. Time series forecasting Holos provides a comprehensive range of time-series forecasting functions, including Holt-Winters method, Box Jenkins and Fourier analysis, additive and multiplicative methods, single and double exponential smoothing. User-definable extensions The Holos external function interface can be used to access procedural analytical functions created using external tools; for example, SAS. The Holos language can also be used to extend the analytical capability of applications. Write back for ‘what-if’ analysis Disk-based structures provide single-user write-back access for ‘what-if’ analysis. Incorporating non-numerical data Holos does not provide support for the analysis of non-numerical data. Data mining The Holos language provides advanced data mining capabilities through the use of pattern matching, neural networks, cluster analysis and Chaid analysis. The integration of this technology requires considerable programming, although a wizard is provided to help generate the initial code. Pattern matching is also available as an Agent process. Other analytical functionality Holos supports a range of specialised analytical functions, including risk analysis (Monte Carlo), impact analysis and target seeking.

Web support Summary

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Holos supports both HTML and Java-based web interfaces. The HTML implementation is far from elegant, but does provide web users with a simple and effective means of accessing and navigating through reports, albeit in a restricted manner. The Java-implementation provides a more flexible interface for OLAP analysis. The web tools are aimed at end users; there is no support for designing models or developing applications. End-user functionality via the Web Functionality of web access to explore models The Web Gateway interface delivers standard OLAP capability via a series of dynamic HTML pages. This allows a degree of interaction with reports, but users are restricted by the limitations of HTML. Graphs are implemented as Java applets. The Java Worksheet provides a much slicker interface for OLAP analysis. The Worksheet supports drag-and-drop operations for slice-and-dice and filters for changing views. Supports both registered and unregistered web access The Web Gateway can support both registered and unregistered (guest) users. Range of users supported by the web interface The Web Gateway is suitable for those users that require access to predefined reports in a restricted way. The Java Worksheet is suitable for power users that require direct access to models for ad hoc OLAP analysis. Creating models via the Web Editing the mapping layer It is not possible to edit the model-data source mapping layer using any of the web tools. Building and editing models It is not possible to create new models via the Web. Distributing via the Internet and the Web Generate HTML and Java Holos can convert applications within Holos language code to HTML format using a transformer facility. However, there is no support for generating Java code. Corporately organised distribution via the Internet There is no support for dynamic or targeted distribution of reports and models via the Internet. Static HTML reports can be e-mailed via the Internet. Include URLs in a report It is possible to include multiple URLs in reports. URLs can reference other Holos reports or applications. Distribution of web server processing There is no support to balance load-processing across separate application servers. The Holos Web Broker can spread processing across many processors inside a single multi-processor application server.

Management Summary

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Holos lacks a separate graphical management console for administering Holos models, data and end users. A command-line interface is provided to define scheduling and user security. The security of models relies heavily on the underlying operating system or database, though stricter access can be programmed. It will be up to developers to define and maintain these controls. Management of models Separate management interface Application Manager is a general interface that is used to manage all aspects of the application development environment, including models. Security of models In general, Holos relies on the server operating system (Windows NT, Unix or Open VMS) and the security in the target database to ensure access controls. All Holos models can be defined as read-only or hidden from users. Tighter controls can be built using the Holos language. For example, alias definitions of structures can be used to restrict user access to parts of the model. Query monitoring Holos provides query and application monitoring facilities. Statistics can be collected and analysed on usage of models, reports and other system components. Management of data How persistent data is stored (not scored) Holos is a hybrid OLAP tool that supports both relational database and multidimensional database storage options. Scheduling of loads/updates Data loading can be scheduled by attaching a loading script to an Agent that defines the process for updating the models and applications with fresh data. There is no point-and-click support for defining these schedules. Event-driven scheduling Agents can be defined that automatically watch for events such as an update to the database or the creation of a new file, and subsequently execute a schedule. Failed loads/updates Holos automatically generates a comprehensive log of all load processes. Holos language scripts determine how failed uploads and updates are handled on a per-application basis. Agents can be linked to scripts to notify administrators of failed loads and updates via e-mail. Distribution of stored data Stored data can be distributed across multiple servers for storage. For example, it is possible to store a multi-year time series as a set of ‘annual’ structures, each of a different type, and each stored on separate servers. Holos provides facilities to calculate structures held in different servers. Sparsity (only for persistent models) Developers can specify sparse data processing algorithms according to the type of structure and its degree of sparsity. Sparse disk structures are indexed using a hash algorithm to provide optimised handling of sparse data sets. Methods for managing size Holos’s smart consolidation facility can be used to calculate some values on demand only. Developers can decide which values to precalculate, and which to store for each model. In-memory caching options Various aspects of the server cache, such as bucket size, can be configured to optimise performance. Informing the user when stored data was last uploaded A log is created each time data is loaded into a structure. The information in the log can easily be displayed in client applications. However, there is no support for accessing upstream load process metadata from the data warehouse. Management of users Multiple users of models with write facilities Multi-user locking is automatic in relational structures. A toolkit is provided to help manage multiple update users on a disk-based structure. However, simultaneous user locks must be programmed in the Holos language. User security profiles User security profiles are defined using the Holos language. Profiles can be assigned to individual users or groups of users. Query governance In the relational environment, the size of data blocks returned from the database can be controlled. Restricting queries to specified times There is no support provided for restricting queries according to time. Management of metadata Controlling visibility of the ‘road map’ Developers can define the complete user environment, or set up application development groups that restrict access to specific Holos components.

Adaptability Summary

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Within Holos, it is easy to adapt a model to support changing business requirements; all model design operations produce Holos language scripts that can be stored and re-used. Holos can also incorporate different types of structures in a model and easily adapt from ROLAP to MOLAP modes and vice versa. However, there is no direct support to ensure that data sources are automatically synchronised with Holos structures and applications. Nor does Holos provide any support for impact analysis. Change in business requirements Adding new dimensions to a model Adding dimensions to models is straightforward, but adding them to structures requires updating the data in the structure and the objects dependent on that structure. A tool to track these dependencies is available. Re-use of dimension definition All dimension definitions can be saved as Holos language scripts and re-used by application developers. Adding new measures to a model New measures can easily be added into any Holos model or application. Re-use of calculated measure definition Measure definitions are re-usable. Changing the architecture to reflect business needs Holos’s architecture is flexible in allowing for both ROLAP and MOLAP types of data storage. When query times lag against the broader less summarised ROLAP engine, a subset of the data can be loaded into a pre-aggregated multidimensional database to obtain speedy response against a more focused set of information. The racking and stacking architecture lets users swap cube formats, or replace a physical cube with a ‘virtual’ cube without having to modify the application. Changes to data sources Keeping the data source and model schema synchronised There is no direct support to ensure that the database schema is synchronised with the application. However, developers could feasibly build Holos language scripts to check on this before an application is run. Automatic updating of members in a dimension Updates to dimensions can be triggered through the use of agents. Metadata Synchronising model and model metadata Apart from dimension and measure information, there is little model metadata to synchronise in Holos. Impact analysis There is no support for impact analysis. Metadata audit trail (technical and end users) Holos does not provide any direct support for an audit trail. However, information stored in scripts while creating and modifying the structures can easily be logged to an external file for analysis. Access to upstream metadata There is no integration with externally generated metadata.

Performance tunability Summary

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Holos provides strong tunabaility features for both MOLAP and ROLAP operation. For ROLAP mode, Holos supports the generation of multipass SQL and native SQL access to all the major relational databases. For MOLAP configurations, multidimensional structures can be loaded incrementally. The loading and precalucation of data can also be distributed across multiple processors simultaneously using SMP technology. ROLAP Multipass SQL Holos automatically generates multipass SQL. Options for SQL processing The processing of SQL can be carried out either on the server or the database. Speeding up end-user data access Data access can be speeded up by caching queries on the server in an optimised form. Users can access the cache for matching queries. The cache can be defined to constantly refresh itself, though end users are not automatically informed of its currency. Aggregate navigator The relational structure provides a caching mechanism that stores aggregates on the server. This cache can be stored and reloaded as required. MOLAP Trading off time/size and performance Large logical structures can be incrementally loaded. The underlying physical structures can be independently loaded or refreshed. Once the load is complete, the multi-cubes can be ‘snapped’ back into the main compound cube. Scripts can be developed to re-calculate only those values that have changed during a refresh. Support for multiple users Disk-based (MDDB) structures provide single-user write-back access only. Applications that require multiple write access to an RDBMS are supported through the Holos language programs. Stacked cubes can also be used to provide a ‘pseudo’ multi-user write-back environment. For ROLAP architectures, the number of users that can be supported simultaneously depends on the complexity of the queries and capacity of the data warehouse to cope with the number of temporary tables generated when the SQL is processed. Processing Use of native SQL to speed up data extraction Holos uses native SQL access to all the major RDBMSs. ODBC drivers are supported on certain Unix platforms. Distribution of processing The loading and pre-calculation of data can be spread across many processors; either inside a single multi-processor system or across a loosely clus- tered network of machines. SMP support Holos makes full use of SMP parallelism. Other performance tunability features All the structure types have associated tuning mechanisms and can be modified at runtime. Holos can alter the sparsity algorithms used to calculate and consolidate data dynamically. This means that a structure can be sparse at calculation time, and defined dense at runtime so that different access methods can be employed.

Customisation Summary

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Holos is a powerful 4GL application development tool. The Holos language is purpose-designed for building business intelligence applications, and is supported by an integrated and easy-to-use set of graphical development tools. The range of development options should satisfy most development needs, from simple EIS reporting systems to advanced analytical applications. Customisation Option of using a restricted interface The Worksheet cannot be configured to provide a restricted interface. How- ever, the Desktop Designer tool provides a point-and-click method for customising the functionality of Holos applications, desktops and workspaces. Ease of producing EIS-style reports EIS-style reporting applications can easily be built using the graphical development tools. Applications Simple web applications A toolkit is provided to develop applications that run through a web browser. A transformer utility is provided to convert Holos language scripts into HTML, and present them to web users as a series of dynamic HTML pages. Web applications can access all the functions of the Holos server, but do not support as many features as Holos client applications. Development environment Holos provides a flexible and productive development environment. Easy-to- use GUI tools allow most development work to be done via point-and-click. A full programming environment for the Holos language is provided for writing custom procedures and linking them into applications. Use of third-party development tools Holos does not integrate with external development tools. Other customisation features Holos has strong support for localisation; a single OLAP application can be written to support different language interfaces including German, French, Italian, Spanish, Portuguese and Japanese character sets. Holos can also act as an OLE2 client. Deployment

Platforms Client Holos clients run on Windows 3.1, Windows 95, Windows NT and Macintosh. The Seagate Worksheet runs on Windows 95 and Windows NT and Java- based web browsers. Server The Holos server runs on Windows NT, DEC VMS and the following Unix flavours: HP-UX, AIX, Sequent, IRIX (Silicon Graphics and SGI), SunOS, Digital, AT&T, Pyramid, ICL DRS/NX and SGI Siemens Nixdorf.

Data access Holos provides native access to the following RDBMSs: Oracle, Informix, Sybase, Red Brick, Teradata, Rdb, Ingres, IBM DB2/6000 and HiRDB. Holos also supports ODBC (Windows NT and Unix) and can access data from third-party multidimensional databases (Essbase and Microsoft SQL Server OLAP Services). Holos can also access transactional data from SAP and Oracle ERP applications and can load data directly from Lotus Notes databases and flat file systems. Links to external information providers, such as online news services, are also supported.

Standards Holos supports Microsoft’s OLE DB for OLAP and Hyperion Solutions’ Essbase API. A third interface – the OLAP Council’s MDAPI 2.0 specification – is also under consideration. The Holos Open Client Interface provides integration with ODBC-compliant applications.

Published benchmarks Holos does not provide any published OLAP benchmarks.

Price structure Because of the fixed-price element of the host-based server, costs per user start a little higher than other OLAP tools, but can be substantially lower for large numbers of users. The entry point for Holos is around $80,000. This enables five concurrent server connections, ten licensed desktop users and one application developer. The price includes training for the developer and end users. Sterling Software – Eureka:Suite

Summary

At a glance ...... 2 Terminology of the vendor ...... 3 Ovum’s verdict ...... 4 Product overview ...... 6 Future enhancements ...... 18

Commercial background

Company background ...... 19 Distribution...... 21

Product evaluation

End-user functionality ...... 22 Building the business model ...... 24 Advanced analytical power ...... 26 Web support ...... 28 Management ...... 29 Adaptability ...... 32 Performance tunability ...... 33 Customisation ...... 35

Deployment

Platforms ...... 37 Data access ...... 37 Standards ...... 37 Published benchmarks ...... 37 Price structure ...... 37 Evaluation: Sterling Software – Eureka:Suite Ovum Evaluates: OLAP

At a glance

Developer Sterling Software (Business Intelligence Division), Eden Prairie, Minnesota, USA

Versions evaluated Eureka:Suite comprising of Eureka:Strategy 5.7.8, Eureka:Analyst 4.5, Eureka:Intelligence 1.1, Eureka:Reporter 6.1.3 and Eureka:Portal 2.0

Key points • A suite of tools that integrates ROLAP, multidimensional and query and reporting systems through an Internet-based portal • Servers run on Unix and Windows NT; clients run on Windows 3.1, 95, 98 and NT workstation. Web access is also provided • Eureka:Suite integrates Information Advantage’s DecisionSuite ROLAP tools and IQ Software’s SmartServer and Vision OLAP tools – both of which were acquired by Sterling Software in August 1999

Strengths • A comprehensive suite of business intelligence tools and services that are easily accessible through a web-based portal interface • Integrates a highly scalable ROLAP system – query processing is automatically optimised between the server and the RDBMS • Provides flexible scheduling, report sharing and messaging facilities that are matched by few OLAP tools

Points to watch • The ROLAP server runs exclusively on Unix • The ROLAP server can only be accessed by Eureka clients, which are not the strongest tools for highly specialised analysis • Still considerable scope for integration between the back-end systems – management of a Eureka system can be complex

Ratings

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End user functionality

Building the business model

Advanced analytical power

Web support

Management

Adaptability

Performance tunability

Customisation

Terminology of the vendor

Calculations A general term for any numeric fact that is included in a report. There are three types of calculations: • volumetric – stored values from the database fact tables • calculated facts – are not stored but derived by the ROLAP engine • custom calculations – combinations of existing facts, operators, constants and dimension constraints that the user defines. Categories Database views that define the tables to be used for reports. Categories are initially defined in the metadata tables; each metadata category identifies the dimensions and attributes that are available. End users create reports, calculations and filters based on metadata category definitions. Facts A generic term for data included in reports. Facts can be data items stored in database tables or calculations derived from stored data items and formulae. Facts are defined in the metadata tables and viewed in reports. Filters Allow users to limit the detail and amount of data in a report. Filters are re- usable components stored in the metadata tables. Hot Objects Sterling’s term for hypertext support, which makes Eureka reports act like web pages, with hot spots and drill-down capabilities. Metadata tables Metadata tables are relational database tables that map a data warehouse structure to a business model and contains information about the dimensions, attributes, drill paths and calculations that are used in reports. Report Reports consist of report documents that act as web pages. Report objects are the primary source of information contained within report documents.

Ovum’s verdict

What we think Before being bought by Sterling Software in 1999, Information Advantage was one of the first OLAP vendors to introduce Internet portal concepts into the business intelligence world. Eureka:Suite is its first attempt to integrate two radically different product lines – DecisionSuite and IQ SmartServer and IQ Vision, to offer a comprehensive business intelligence suite and, more significantly, step away from its strict ROLAP stance. Eureka’s portal approach has the potential to bring OLAP to a broader audience. Users will either be delighted by the ease with which they can access business intelligence data, or overwhelmed by the choice of tools and breadth of information available – although users can easily personalise the interface for specific information. However, the core strength of the product remains its ROLAP capabilities (the focus of this evaluation), in particular its ability to analyse large volumes of information with and high numbers of attributes. Scalability is underpinned by a well designed server-based architecture, including an object request broker and a proven ROLAP engine that maximises the use of RDBMS technology while addressing the limitations of SQL. The product’s flexible report scheduling, sharing and distribution options are matched by few other ROLAP tools. However, the ROLAP server is ‘closed’ in that it is not accessible as a data provider to complementary third-party business intelligence tools. Users are limited to Sterling’s own client offerings, which, although well integrated and easy to use, are not necessarily ‘best-of-breed’. Sterling has committed to supporting Microsoft’s OLE DB for OLAP standard, but has not yet announced a date. Under the portal interface, Eureka:Suite represents a collection of separate business intelligence systems. There is still considerable scope for further consolidation and integration and users should not underestimate the management burden. The implementation of the ROLAP system can be complex and any purchase decision usually involves a wider data warehousing consideration. Customers without a data warehousing strategy will almost always need to buy-in some consulting and migration assistance. Large-scale rollouts can take between six and 18 months to complete. When to use Eureka:Suite is suitable if you: • require access to corporate data stored in large, finely-tuned data warehouses • are already committed to a large-scale data warehouse strategy, or are preparing for one • want to analyse large amounts of customer- or product-centric data with large numbers of attributes • want to provide a broad range of users with personalised access to business intelligence and unstructured corporate data • have a requirement to easily distribute and share information and reports across the enterprise

• have a strong commitment to Unix – the ROLAP server runs exclusively in this environment. • It is less suitable if you: • want to develop highly specialised OLAP applications that require very complex analysis • want an out-of-the-box OLAP solution • are willing to accept the effort and cost needed to implement Sterling’s ROLAP tools • want to use ‘best-of-breed’ tools to access and analyse OLAP data • need a flexible business model – the ROLAP business model is tied closely to the structure of the data warehouse.

Product overview

Components ‘Eureka’ is the colourful brandname for a suite of tools that integrates ROLAP, multidimensional OLAP, query and reporting systems – technologies that were originally developed by Information Advantage and IQ Software. The five main components of the Eureka:Suite are: Eureka:Strategy version 5.7.8 – for server-based ROLAP analysis Eureka:Analyst version 4.5 – for multidimensional analysis at the desktop Eureka:Intelligence version 1.1 – for web-based, integrated query reporting and analysis Eureka:Reporter version 6.1.3 – for server-based production reporting. All of these components publish their results to, and can be accessed through, the Eureka:Portal (version 2.0), a web-based portal that provides a personalised, single entry point to a broad range of business intelligence and corporate data. Figure 1 shows the primary functions of the components and whether they run on the client or the server. Eureka:Suite is one of the largest business intelligence suites on the market. But for the specific purposes of this evaluation, we focus on the ROLAP and multidimensional OLAP capabilities provided by Eureka:Strategy and Eureka:Analyst. However, we do acknowledge (where appropriate) the functionality provided by the other components.

Eureka:Portal Eureka:Portal is a web browser interface that allows users to access OLAP reports and other corporate information. It applies the same principles used by consumer Internet portals (such as MyYahoo!) to provide a personalised single point of access to business intelligence and other corporate data via URL links.

Figure 1 Component details

Query and reporting OLAP design and analysis Web access

Client Eureka:Analyst Eureka:Portal

Server Eureka:Reporter Eureka:Strategy Eureka:Intelligence

Eureka:Portal is built on two components: • Content Server, which maintains a repository of ‘objects’ created with Eureka components and third-party systems. Users can publish objects into the server, navigate through content using a search taxonomy, and subscribe to ‘channels’ for automatic report distribution • Content Administrator, which is a web-based environment for administering the Content Server. It includes graphical interfaces for setting up and managing user profiles and setting security, and tools for monitoring the usage of repository objects and tuning performance. The Eureka:Portal can be purchased out-of-the-box as a standalone portal platform for the integration of unstructured tools through a personalised interface. However, its benefits are fully realised when teamed with other Eureka components. The portal integrates all the client Viewer interfaces for the Eureka tools. Similarly, all the Eureka tools can also publish information directly to the portal.

Eureka:Strategy Eureka:Strategy is a Unix-based ROLAP server that processes client requests against large data warehouses – typically to evaluate dimension attributes for customer segmentation and inventory management applications. The server carries out a significant amount of data processing (joins, aggregations and calculations). Eureka:Strategy uses an intermediary metadata layer to dynamically generate SQL for a query, and delivers formatted content back to the presentation tier. The metadata layer provides a business-oriented map of the underlying database table structures, which automatically synchronises applications with changes in the RDBMS. This information is stored in a series of metadata tables, usually in the data warehouse. The metadata can also map data stored in more than one RDBMS. Eureka:Strategy includes a number of client components: Designer An end-user interface for defining ROLAP queries and new reports. Reports can be enhanced by creating custom calculations directly from the Analysis interface. A range of visualisation techniques are also provided. Viewers Provide a visual interface for analysing ROLAP models generated by the ROLAP engine. Two types of ROLAP Viewers are available – for client-server and a combination of HTML and Java: • a client-server interface for casual users – it allows users to tailor reports built with Analysis, or simply view predefined reports delivered by the ROLAP server as part of a schedule or agent process • a web interface – enables reports to be accessed and analysed from a web browser. It is closely integrated with the ROLAP server (via CGI), with reports dynamically generated in HTML.

Administrator A client-server tool aimed at model designers, DBAs and systems administrators in the ROLAP environment. Interfaces are provide for: • creating, validating and maintaining the metadata tables • administering and managing the ROLAP environment.

Eureka:Analyst Eureka:Analyst is a multidimensional analysis tool targeted at analysts with calculation-intensive analytical needs – typically financial forecasting. The tool is based on IQ Software’s Vision software – a proprietary OLAP client that loads multidimensional cubes from MDDB servers and holds them in memory on the desktop for offline analysis. It links directly to Applix’s TM1 MDDB engine and OLE DB for OLAP-compliant data sources. Eureka:Analyst can be configured for server-based and local desktop OLAP capabilities: • a standard mid-tier server architecture, where Applix’s TM1 Server or Microsoft SQL Server 7.0 OLAP Services MDDB act as data providers. Users can access cubes directly from these servers (via the TM1 API or OLE DB for OLAP) • a desktop-based OLAP architecture, which processes multidimensional cubes that have been downloaded from the server. Only base-level data is downloaded, compressed and stored in memory on the client resulting in a small, but rigid cube structure. Because data is held locally on the client this allows for on-the-fly calculations and aggregations to be performed offline. A key feature of Eureka:Analyst is its ability to create calculations on-the-fly and write them back to multidimensional cubes. It uses TM1’s Excel add-in component, called Perspectives, to provide this functionality. Analyst also includes tools to generate TM1 cubes and store them in the TM1 Server. The tool supports write-back capabilities to TM1 cubes; but it can only access predefined Microsoft OLAP Services cubes. Detailed evaluations of Applix TM1 and Microsoft SQL 7.0 OLAP Services are included as separate reports in Ovum Evaluates: OLAP.

Eureka:Intelligence Eureka:Intelligence is a Java-based tool that provides web-based integrated query, analysis and reporting (WIQAR) functionality. It allows users to slice- and-dice and drill into live data via graphical, interactive views including tabular reports and charts. The product also lets users create multidimensional OLAP reports and provides report scheduling facilities. Eureka:Intelligence includes a client application and a server. The WIQAR server is comprised of several main components: i-cache i-cache stores multidimensional data for OLAP processing. The cache holds the information in a non-sparse form and can be accessed by multiple users. The Connection Manager facility handles user and server connectivity.

Query and reporting engines These are for querying from various data sources using JDBC and/or ODBC and generating banded report documents. Scheduling tool The scheduling tool allows users to schedule live reports to be refreshed periodically. For example, historical ‘snapshots’ of data can be scheduled to periodically add a report and time-series grouping of like reports. OLAP engine The OLAP engine is for analysing data and calculating measures. The engine currently supports cached cubes created by Eureka:Intelligence. Enterprise Java Bean API The Enterprise Java Bean API is for custom development and integration of third-party OLAP engines.

Eureka:Reporter Eureka:Reporter is a server-based reporting tool best used for creating, scheduling and distributing batch reports from ODBC-compliant data sources. The Eureka:Reporter server acts as a repository for storing reports and report definition templates. Eureka:Reporter integrates SQL query and reporting capabilities. The query engine performs all SQL queries, condition filters, calculations and aggregations. It runs directly against relational databases or third-party operational datastores. The query capability also ‘doubles’ as a cube builder, moving relational datasets into dimensional structures for OLAP analysis. An integrated report writer uses the output from the query engine to create ‘active’ report documents. These reports act like web pages, and incorporate ‘hot objects’ that create drillable spots or hyperlinks to additional data sources. In an OLAP context, hot spots can be used to drill to a different level of detail by making a new request for data. Eureka:Reporter has three client components: Report Viewer An end-user interface for accessing SQL reports. It includes a DDE interface and a command line interface for submitting queries to the Report Server and viewing reports. Report Viewers are available for client-server, ActiveX and HTML. Report Designer Provides interactive tools to create queries, design reports, charts and crosstabs. In addition, the Report Designer also includes the functionality contained in the Report Viewer. There are two main interfaces for creating reports: QuickQuery – for creating ad hoc queries and formatting the results using simple grouping, sorting and totalling functions FreeForm – for more elaborate formatting and the inclusion of ‘hot’ objects (that link to other documents). Report Designer accesses data via a ‘Knowledge Base Manager’ – a metadata repository that provides a business view of the database, the objects in them (such as tables and columns) and the relationships (joins) between these objects. Sterling provides out-of-the-box solutions that create and populate metadata models for SAP, PeopleSoft, Baan and JBA transaction models.

Report Administrator A client-server management tool for managing report metadata. It includes a metadata management facility to allow DBAs to configure, define and maintain access to multiple data sources. Report Administrator includes all functionality found in the Designer and Viewer interfaces. Architectural options

Full mid-tier architecture A mid-tier architecture is supported by using Eureka:Analyst. In this configuration, Eureka:Analyst effectively acts as an OLE DB for OLAP ‘consumer’ for TM1 Server and Microsoft OLAP Services (or other OLE DB for OLAP compliant MDDBs).

Light mid-tier architecture This is the ‘natural’ configuration for Eureka:Strategy’s ROLAP architecture. It uses a ROLAP engine that sits on a mid-tier Unix server. ROLAP clients run under Windows. A web server can be added to the architecture to provide web access. The Eureka:Strategy ROLAP server is the central hub of the system that processes client requests against a data warehouse. The ROLAP server is optimised for variations of star, snowflake and federation and constellation database schemata and multiple table aggregation and partitioning strategies. The ROLAP server processes queries and temporarily caches them on the server at runtime; there is no loading of data into a persistent MDDB store. The server is based on an object request broker (ORB) architecture, where messages are passed between the different service objects, such as those responsible for receiving client requests, connecting to the RDBMS or formatting reports. An important feature of the architecture is scalability. Depending on the nature of the query, data processing is carried out on the server, the relational database or a combination of both. Eureka:Strategy optimises how processing is split between the database and the server. For example, it can perform simple aggregations or data filters within the database; more complex procedures that are inefficient to perform in SQL, particularly calculations, will be done on the server.

Desktop and mobile architecture Eureka:Strategy is a server-based tool and does not support a two-tier desktop architecture; all processing is done on the server. A desktop OLAP configuration is supported using Eureka:Analyst, which allow users to download small multidimensional cubes (or slices of cubes) from a MDDB server into memory on a client machine for local, offline analysis. Using Eureka:Suite

Bringing the portal to business intelligence Eureka:Suite is one of the first OLAP products to apply the search, personalisation, visualisation, navigation and subscription principles of Internet portals to the business intelligence world.

Eureka:Portal is designed primarily for two audiences: • those that wish to use a portal to provide a single point of access to the Eureka business intelligence tools • those that want to use Eureka for a much broader purpose than just business intelligence – for example, building a generic portal or extending a corporate intranet. Eureka:Portal provides many of the core functions expected from a web- based portal – though its main thrust is not as a generic portal. Search and categorisation The portal’s Library component provides a centrally administered corporate taxonomy for business intelligence data. The taxonomy enables users to search for reports and other content using keyword searches (powered by a Verity search engine) and by navigating through a folder structure organised by logical category or subject. Channels The portal uses ‘Channels’ to organise the delivery of OLAP reports and corporate information based on user-defined categories or subject-based folders. Users can subscribe to workgroup or public channels to browse information on a specific subject, and choose to link to an attached report ‘object’ for more detail and/or further analysis. Broadcasting agents The portal also provides advanced ‘Messenger’ and ‘Newspage’ agents that can recognise predefined events and deliver information to users through a range of channels, including a Newspage, as shown in Figure 2. This is an electronic newspaper that lists and displays reports information headlines from subscribed channels, with links to associated report objects for further analysis. Users can define agents to automatically run reports and other script files at a pre-determined date and time, or when a trigger event occurs in the database. Personalisation Eureka:Portal supports extended user profiles to allow users to personalise access and delivery of relevant information. The profiles allow content administrators to regulate the delivery of appropriate information to each user or workgroup. For example, administrators can set profile parameters that ensure each user or workgroup receives reports that are most relevant to their job requirements and match their security levels. Users can further personalise Eureka:Portal by choosing topics of primary interest to them, as well as determine how frequently this information is updated for them. The Newspage interface, shown in Figure 2, allows users to subscribe to information that can not only track headlines on topics of interest, but also access personal Internet information sources and provide integration with personal e-mail and calendering systems. Collaboration Users can also engage in collaboration using the portal’s ‘Discussion Boards’, which provide a threaded forum for group discussions. The Discussion Boards are formatted in the style of popular Internet message boards, and are searchable and available for any report object serviced by the portal. Users can also add comments and commentary to report objects to support collaborative group working environments.

Figure 2 Broadcasting information using a Newspage

Setting up a ROLAP environment At the heart of Eureka:Suite is a ROLAP system – based on the Eureka:Strategy (formerly Information Advantage’s DecisionSuite ROLAP tools). It provides a suite of tools for setting up the ROLAP environment and distinguishes clear responsibilities for model designers, end users and administrators. The metadata tables used to map the data warehouse structure to business dimensions are defined by experienced DBAs using the ROLAP Administrator interface, as shown in Figure 3. These users are expected to have a good understanding of the data warehouse and SQL syntax.

Figure 3 Defining drill-down hierarchies

Reports can be defined by DBAs, but can also be created and viewed by business end users using the Analysis client. Experienced power users can use this interface to enhance models by including their own custom measures and filters. The ROLAP Viewer interface provides a simple interface for ‘information consumers’ that only require easy viewing access to reports scheduled by the Eureka:Strategy Server. Administrators are provided with separate ROLAP Administrator interfaces for managing end users. It provides a number of graphical tools that enable managers to configure user security profiles and govern database queries according to time and the size of results sets returned from the RDBMS. The ROLAP interface also provides the management interface, allowing distribution schedules to be built and developing agents that ‘push’ results directly to end users via alerts, e-mails or report attachments.

Building and using a business model using the ROLAP tools Building and using an OLAP application using the Eureka:Strategy ROLAP tools is typically a three-phase process.

Creating the metadata model The first step is to map an existing data warehouse structure in metadata tables so that it is made visible to Eureka:Strategy. The mapping defines the logical elements of a business model, such as dimensions, measures and hierarchies. Eureka:Strategy references the metadata tables to build the SQL statements that it submits to the source database. Metadata tables are defined by an experienced DBA using the ROLAP Administrator tools, which provide a spreadsheet-style tabular view of the various metadata tables. Alternatively, the tables can be defined with an external editor. A graphical wizard provides a prompted interface to create and populate the metadata. Initially, DBAs need to set up approximately ten metadata tables that map the dimensions, attributes and facts, drill-down hierarchies and time periods held in the data warehouse. Figure 3 shows the graphical interface for defining drill-down hierarchies. Add calculations and filters Designers (or power users) can elaborate the metadata by adding calculations and filters. Calculations are an important element of DecisionSuite models, and two types can be defined using a one-off formula or a calculation template: • simple report calculations, such as averages and totals for a group of cells, can be added to a report via the report calculations dialogue • custom calculations are calculated measures that are stored on the server and are available for use in all reports. Market share, for example, might be defined as a calculation.

Calculation templates are skeleton definitions of calculations that use variables rather than actual dimensions. A calculation template can be re- used as the basis for different calculated measures; the user simply selects the appropriate dimension values to be used. Eureka:Strategy includes a number of standard calculation templates, but users can define their own calculation templates or build calculated facts from scratch using the Calculation Builder facility. Filters define usable selections or groupings of dimension members for inclusion in reports. They are typically used to enhance the model without the user having to access the metadata directly. A filter, for example, can define a new group of dimension members based on an attribute value, such as all items whose product code starts with ‘88’. A filter is either a static or dynamic constraint on the data: • a static filter may define a specific number of dates or a group of items from a dimension: for example, all products beginning with ‘Diet’ • a dynamic constraint will change as the database changes: for example, it may select data for the last six months, or all sales of more than $500. Filters are themselves re-usable objects within Eureka:Strategy. They are defined through a set of dialogues, using an expression builder if required. All filters are stored in the metadata tables and can be re-used by anyone with the correct access rights.

Creating a report The Eureka:Strategy development philosophy is focused on the concept of ‘reports’ that users create by selecting dimensions from the metadata tables. Reports are defined for or by users, sent to other users, scheduled by agents or published through the Web. Each report is based on a report template that defines its layout, content and properties. Report templates are created in the Template Editor. This is shown in Figure 4. ROLAP designers can readily create templates, but ROLAP users can modify them by changing the layout of dimensions or including different dimension members. Typically, templates will be created for standard reporting requirements in an organisation, such as a market share summary or product ranking.

Building and analysing multidimensional cubes using Eureka:Analyst Eureka:Analyst allows users to build multidimensional cubes from relational data sources on-the-fly, and analyse them locally on the desktop in an ad hoc manner. The data for analysis can be sourced directly from a third-party MDDB server – currently TM1 Server and Microsoft SQL Server 7.0 OLAP Services are supported – or from a dataset returned from a simple SQL query.

Figure 4 Template Editor

Users can define new TM1 cubes from datasets returned from Eureka:Reporter. A TM1 Cube Creation Wizard, shown in Figure 5, is provided to select measures, dimensions, create filters and so on. This facility is intended to support the ad hoc creation of small and simple cubes for local desktop analysis. Cube creation is therefore provided as an adjunct to the core ROLAP and reporting environments, rather than to support complex multidimensional modelling requirements. As users create the TM1 cube, they simultaneously create a ‘design document’ that is referenced every time the cube needs to be updated with fresh data. TM1’s Perspectives interface is an Excel add-in tool that is used to access and analyse the cube in a spreadsheet-like matrix. Standard OLAP functions such as slice-and-dice, drill-down and traffic lighting are provided.

Sharing and distributing reports A key feature of Eureka:Suite is its ability to share and distribute reports to a large user community. Users can create a set of saved analysis views like an interactive presentation or ‘briefing books’ to be shared with other users via the Eureka:Portal. Users can also automatically receive cubes via e-mail from other users.

Figure 5 TM1 Using the Cube Creation Wizard to build a model

Distributing reports Support for the sharing of information between large numbers of users is an important element of the Eureka:Suite architecture. A number of features within the product promote the easy sharing of reports. For example, the user interface for all the Eureka:Strategy client tools is based on the notebook metaphor of a ‘portfolio’. A portfolio is made up of a number of tabbed pages. The first page is always the ‘alert page’ and lists any alerts received, with a short description of each one. An alert might notify a user that a scheduled report has been completed, or it might have an attached report sent by another user. Other pages in a user’s portfolio are used to organise reports in an efficient manner; they can be set up according to each user’s preferred way of working. A portfolio can include folders shared by a workgroup. For example, a message icon is provided on a standard toolbar across all the client tool interfaces to allow end users and administrators to easily define alert messages, attaching, if required, one or more reports. When the report has run, an alert message appears in the portfolio of all the recipient users. This is shown in Figure 6. As well as sending reports to other users, developers and users can create Agents to run reports. An Agent runs one or more reports and can deliver alerts to one or more users when the report is completed. Agents can be scheduled to run at set times or can be fired off by a specific trigger event, such as the loading of the data warehouse.

Figure 6 An Alert message

Future enhancements

Eureka:Suite is the first phase of integration between Information Advantage’s DecisionSuite ROLAP tools, and IQ Software’s multidimensional OLAP and reporting products (SmartServer and Vision). Further integration and enhancements are planned for future releases.

Portal enhancements Eureka:Portal’s search engine will be enhanced and integration with third- party search technology will be provided. A staging area will be added to the Content Server to support a ‘submission- and-review’ process for publishing content. This will include the provision of content expiration and version control facilities. Various functional enhancements to the web interfaces are planned using Java applets and ActiveX controls. ROLAP enhancements Sterling plans a number of enhancements to the ROLAP system including the provision of new web-based ROLAP Viewers and Designers that provide full OLAP analysis and design functionality via the Web. Eureka:Strategy will also be enhanced to support larger SQL statements and advanced statistical and visualisation functionality by leveraging RDBMS extensions such as RISQL and via integration with SPSS. A Windows NT version of Eureka:Strategy and support for OLE DB for OLAP as a data provider are planned for the long term – although Sterling has not announced any concrete dates for availability as yet. Scalability and performance A number of enhancements are planned in this area, including: • intelligent caching of large query results and metadata – the caching will also implement security so the results can be shared across workgroups • SQL optimisation – future releases of Eureka:Strategy will support the ‘packaging’ of SQL statements as objects for easy modification and re-use. This will allow a DBA to build a custom ‘SQL Adapter’ that generates optimised SQL for a specific ‘class’ of query • load balancing and failover support – based on industry standard distributed network processing technologies. Development environment Sterling aims to provide an application development environment. It is in the process of re-structuring the API of its business intelligence tools to support CORBA. This is expected to facilitate integration and the development of custom applications from both customers and VARs. Analytic applications Sterling is developing a number of relationships with CRM and ERP vendors to develop and market analytic applications for the customer relationship management (CRM), enterprise performance management and healthcare markets. Several applications have already been announced, with more expected in 2000.

Commercial background

Company background

History and commercial In September 1999, Sterling Software acquired Information Advantage for $168 million. Information Advantage is now part of Sterling’s Business Intelligence Group, based in Minneapolis, Minnesota. Information Advantage was formed in 1990, following IBM’s purchase of Metaphor Computer Systems, the EIS/DSS vendor. The Metaphor product group was absorbed into IBM, but the consulting arm set up a new company, which became Information Advantage. The company developed its first product, a Unix-based decision support engine called Axsys, as a by-product of its experience in implementing large DSS systems in the retail industry. Axsys eventually evolved into DecisionSuite ROLAP Server. In December 1997, Information Advantage completed its IPO, followed in September 1998 by the acquisition of IQ Software, a US enterprise query and reporting tool vendor, for $65 million. Although the two companies had radically different product lines and sales models, Information Advantage quickly worked towards synergy. In March 1999, it announced a strategy for integrating DecisionSuite and IQ Software’s SmartServer and Vision tools under Eureka:Suite. These tools were bought under Sterling’s Business Intelligence Group based in Minneapolis and London. Sterling Software’s headquarters are in Dallas, Texas and it is one of the 20 largest ISVs in the world. Revenues for fiscal 1999 increased 12% to $807 million. Net income also increased 36% to $143.7 million. In addition to business intelligence, Sterling provides software and services for the application development, information management, systems management and federal systems markets. It has an installed base of more than 20,000 customer sites and 3,700 employees worldwide. Sterling has a long history of acquisition – more than 35 companies have been bought since it was founded in 1981. Proposed acquisition by CA As we went to press, CA International announced its intention to acquire Sterling Software. The proposed deal will be worth around $4 billion in a stock-for-stock exchange. The deal has been approved unanimously by the boards of directors of both Sterling Software and CA and now awaits review by the US authorities. No details have been announced as to the impact on Sterling’s business intelligence tools.

Character and direction Information Advantage was the first business intelligence vendor to jump on the portal bandwagon, although the impact of its new Eureka products has as yet been minimal. Seeking additional financial resources to achieve its goal of becoming a leading business intelligence player, Information Advantage agreed to be acquired by Sterling Software. Sterling obviously saw an opportunity to acquire a complementary set of technologies and boost its position in the business intelligence market by offering a larger suite of tools. Similarly, the

injection of R&D resources from Sterling will allow Information Advantage to pursue new avenues. But in some respects it is sad to see one of the two remaining independent ROLAP players get subsumed into a company with a ‘growth by acquisition’ strategy; Information Advantage represents Sterling’s 35th acquisition, and the only remaining ‘independent’ ROLAP vendor is now MicroStrategy. The Eureka:Suite is not only an attempt to consolidate two complementary, non-overlapping product lines – it also opens up new opportunities in the applications service providers (ASP) market – that is, to provide a single point of entry into business intelligence and enterprise applications available to ASP customers via a web-based portal. Sterling is targeting Eureka:Suite at the high-end market; early adopters include 3M, Acxiom, Andersen Consulting, Federal Express, Goodyear, MCC Behavioural Care and PeopleSoft. The ROLAP technology (Eureka:Strategy) is best suited for analysing large data warehouses, and is therefore particularly strong in the retail, consumer packaged goods, telecommunications and insurance sectors. Sterling has partnered with application software vendors to develop packaged analytical solutions. The company has formed a CRM business unit to develop and market customer analysis solutions. It has partnerships with Acxiom, DynaMark, Prime Response, Profit Management Group and SPSS in the CRM space. It has also aligned with the major ERP vendors. For example, PeopleSoft has integrated Eureka:Strategy’s ROLAP capabilities into its Enterprise Performance Management (EPM) application suite. Customer support

Support Multilingual, telephone hotline support is available through support centres based in Atlanta (USA), London (England) and Sydney (Australia). On-site support arrangements are also available. Some support is available via the Web.

Training A number of public and on-site training courses are provided for all Eureka components. These include a one-day introductory course for casual end users, a two-day course for analyst-type users and a four-day technical course for IS developers and DBAs. Computer-based training is also available. Sterling also offers an Online University.

Consultancy services ROLAP implementations usually involve a wider data warehousing consideration, and will require significant consulting. Sterling’s Professional Services division provides a broad range of services for business intelligence. Two service organisations are available as part of the company’s Business Assessment and Solution Planning Service: • business consulting – for strategic planning & business intelligence opportunity and solution assessment and project & change management • implementation consulting – consultants have expertise in data warehousing, information portal development, implementing enterprise reporting and analysis systems for specific vertical sectors.

Distribution North America Sterling Software Business Intelligence Division 7905 Golden Triangle Drive, Suite 109 Eden Prairie MN 55344 USA

Tel: +1 612 833 3700 Fax: +1 612 833 3701

Europe Sterling Software International Business Intelligence Division Sterling Court Eastworth Road Chertsey Surrey, KT16 8DF UK

Tel: + 44 1932 587 000 Fax: +44 1932 587 242

Asia-Pacific Sterling Software Business Intelligence Division Level 21, 201 Miller Street North Sydney NSW 2096 Australia

Tel: +61 (0) 2 9959 2282 Fax: +61 (0) 2 9959 2257

http://www.sterling.com/eureka/ E-mail: [email protected]

Product evaluation

End-user functionality

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Eureka:Suite is not a product that can be used out-of-the-box. But it is not too difficult to master and is flexible enough to cater for a range of users. The portal interface provides a consistent and manageable interface to business intelligence data, allowing users to easily navigate to higher levels of functionality as required. Excellent support is provided for dissemination of reports and collaborative group working. The ROLAP clients support an extremely intuitive notebook-style interface for advanced analysis and reporting functions. ROLAP users can also benefit from the portal’s intuitive publish-and-subscribe model and its strong personalisation capabilities. However, one feature we would like to see is an approval process that requires a manager’s ‘sign-off’ before any reports are published into the system.

Finding and understanding the model Finding and loading a multidimensional model The highest-level interface for accessing business intelligence reports is the Eureka:Portal. Users can easily browse report libraries by navigating through a folder structure, organised by subject. Internet search capabilities are provided; the index enables users to search for objects in the repository using keywords, including name, description, headline, content type, author, date and filters. The ROLAP interface for accessing and interacting with models is organised around a ‘portfolio’ containing tabbed pages. Users can have as many tabs as they wish, although generally there are three – alert information from agents, personal information for users’ own models and reports, and workgroup information (shared models and reports). Metadata for end users A description of the ROLAP model in terms of its elements (dimensions and calculations), its author and when it was last updated, is stored in the metadata tables. The descriptions are readily accessible to end users of reports. Eureka:Analyst, however, does not provide any end-user metadata. Annotation by the end users Users with write access to Eureka:Strategy’s metadata model may annotate reports.

Using the model Basic OLAP functionality Eureka:Strategy clients provide a friendly notebook-style interface. Core OLAP functions such as drilling, slice-and-dice and pivot are easily accessible via point-and-click. Three drill modes are available – up, down or custom

(skipping over one or multiple levels) – and each drill results in data retrieval from the database. Drilling can occur on any dimensional element, regardless of its positioning on the report (including multiple levels of nesting within rows, columns or sections) and derived facts; but can only drill on items for which fact values are extracted directly from the database or defined within the metadata. Eureka:Analyst provides a similar range of OLAP functions, but through a spreadsheet-like interface. Changing the position of members in a dimension level Eureka:Strategy users can change the location of dimension members (including rows, columns or blocks of data) in a report using drag-and-drop. Eureka:Analyst uses the TM1 dimension and measure hierarchy functions. It is possible to add calculated members on-the-fly, but users cannot change hierarchies or reposition members. Visualising the drill-down hierarchies ROLAP users are provided with a pop-up ‘map’ to show the levels of hierarchies available for a dimension and identify the current level with a check mark. Users can also jump to specific levels in the dimensional hierarchy. Drilling-down to detailed data Users can drill-down to access detailed transactional data directly from the report interface. Eureka:Strategy does not differentiate between aggregated and detailed data; the same user interfaces are used and the same processing is performed. The data does not have to undergo special preparation to be accessible at detail level. Eureka:Analyst also supports ‘drill-through’ capabilities to source relational database. Range of front-end user tools Eureka:Portal provides access to a number of front-end tools for client-server and web-based reporting and analysis. These include all the Eureka Viewers, including a custom-built OLAP client for analysing TM1 and Microsoft OLAP Services multidimensional cubes. Visualising the results Report content can be visualised in multiple variations; a default mode is provided to automate visualisation upon initial access of the report. Users can easily select and chart data from within reports. The charting tools support a range of business graphs, and a wizard facility is provided. Users can simultaneously display multiple tables and charts in a report. But it is not possible to drill-down or rotate dimensions from within a chart. Integration with MapInfo is provided for visualising data in maps.

Saving and sharing results Designing a report Report documents can be created dynamically from any ODBC-compliant data source and can include user-defined calculations. A visual object-based publishing tool is provided to define and arrange report object for presenting data in tabular, free-form, chart, cross-tab and newspaper-style documents.

Eureka:Reporter also has the ability to define ‘hot objects’, which simulate web hotlinks and look and act like web pages. Users can link from one document to another, reach a deeper level of detail by drilling through the underlying data or page through a briefing book of related documents. Reports can easily be defined from scratch or using templates. It is possible to embed images, video, sound or OLE objects in reports. Publishing a report The Report Caster component automatically publishes and distributes reports to end users based on either an individual or workgroup basis; public and dynamically defined distribution lists are supported. Narrow casting functions, which limit publication to specific users based on their personal or workgroup exceptions, are also supported. Reports can be published directly to the Eureka:Portal for access and distribution to other users. Users can choose whether the content can be viewed by every user, specific groups or just by the publisher. Analysts can create a set of saved analysis views (briefing books) to be shared with other users. Targeted distribution via e-mail Reports can be distributed via e-mail from the client tool interface. Eureka:Portal’s Messenger feature can also be used to schedule and automatically deliver reports stored in the repository via e-mail – or any e- mail addressable device or channel (including digital phones, pagers and fax machines). Eureka:Strategy uses the Unix mail system to distribute reports – address lists set up within Unix mail may be used, but these cannot be generated dynamically. Users can send and receive compressed cubes on their desktop machines via e-mail. Eureka:Analyst users can receive cubes via e-mail, and download them onto the desktop in memory for disconnected analysis. Subscribing to reports Users can easily subscribe to ‘channels’ to control which reports they receive. Newspage is a particularly useful tool to specify the scheduled delivery of reports and other information pertinent to a user’s daily tasks. Building the business model

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A Eureka report is just one perspective on the business model. Much of the work is done beforehand when defining a business-oriented map of the underlying database table structure (metadata tables). This allows developers to build a logical business model to simplify end-user construction of reports. The business model is flexible, and the use of filters and calculations allow for considerable adaptability. A wizard-driven interface guides designers through the process of describing complex drilling hierarchies and aggregation table information. However, a diagrammatic editor would ease the task of setting up and managing the metadata tables. Eureka:Analyst also provides modelling capabilities to build TM1 cubes ‘on- the-fly’ for desktop analysis. These models created are considerably smaller and less flexible than the ROLAP cubes, and are not the primary focus of this evaluation section.

Basic design Design interface Eureka:Strategy’s ROLAP Administrator provides a graphical interface for mapping the data warehouse structure onto metadata. This interface displays the metadata in spreadsheet-style tables. The ROLAP Administrator is adequate for this task, but it would be better if there was an overview of the main elements, rather than just a series of tables. It would also help if it included dialogues and pick lists to help with the maintenance of the metadata. The wizard provides dialogues and pick lists during the metadata creation. Reports (sets of dimensions, calculations and filters) represent the business model. The design interfaces for both metadata and reports share the same general style of interface. Visualising the data source ROLAP designers can see a sample of data from a selected relational table. However, they cannot view the overall database schema. Universally available mapping layer Metadata tables can be defined to map dimensions, measures and hierarchies to specific parts of the data warehouse. Categories provide end users with a restricted view of the metadata tables. Prompts for metadata ROLAP designers are not automatically prompted to add additional metadata when creating the metadata tables or defining reports.

Building the dimensions Selecting columns for the dimensions Columns for dimensions can be selected using point-and-click. A wizard facility is provided to speed up the mapping process. Selecting the members shown in a dimension level Filters can be used to select dimension members. Filters are created by point- and-click. There are three types of filter: dynamic, static and level. The differences are related to the type of SQL generated. Defining a dimension hierarchy Developers can easily define drill-down hierarchies using point-and-click. Multiple and split drill-down hierarchies may be defined. Unbalanced hierarchies are also supported. Developers can also define attribute traversal paths for traversing on attributes (not dependent on a dimensional hierarchy). Time dimension Time dimensions must be defined according to standard or custom time periods in the business model. Multiple time dimensions are supported, and filters can be used to define non-standard time periods, such as fiscal year and lunar months.

Annotating the dimensions Dimensions in the model can be assigned long and short name descriptions by designers, which can subsequently be viewed in a report by end users. End users cannot edit these dimension descriptions. Default level of a dimension hierarchy Designers can define a default level for a dimension hierarchy when opening a report.

Defining the measures Calculated measures Designers and end users can add new calculated measures to the business model at any time, either using a calculator-type interface or a calculation template. A scripting language is available for defining complex calculations. A library of mathematical, logical and relational operators is provided. Support for multiple measures with a set of dimensions Multiple measures can be stored with a set of dimensions. The measures can also be arranged in a hierarchy.

Multiple designers Multiple designers The tools do not provide any special support for multiple designers. Support for versioning There is no direct support for versioning control.

Other ‘building the business model’ features Eureka:Strategy links to Platinum’s ERWIN data modelling software, which is able to create metadata tables. Advanced analytical power

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The ROLAP tools provide a number of specialised functions geared towards customer/product-centric analysis. Calculation templates could feasibly be used to add more powerful analytical capabilities with regard to dimensions or attributes, but these need to be built into the model during the design phase. Users that wish to perform advanced statistical analysis and trending/ forecasting will need to licence third-party tools. Eureka:Analyst relies entirely on the analytical functions provided by Applix’s TMI and Microsoft OLAP Services, including write-back capabilities. Standard Excel functions are available.

Third-party tool integration The ROLAP tools do not provide any direct integration with specialised third-party analysis tools. Nor does it provide an Excel add-in. However, the tools can be used to export subsets of the RDBMS database in a format suitable for import into such tools. Statistical packages and datamining tools can also be accessed via the Eureka:Portal, but as separate systems.

Defining specialised models Ranking and sorting Support is provided for simple definitions of top and bottom order ranking. Mathematical methods Support is provided for logarithmic, trigonometric, exponential and factorial functions. Financial functions Other than the standard functions provided by Excel (for Eureka:Analyst users only), financial functions are not supported. Statistical models Support is provided for a number of simple statistical functions including: moving averages and rolling sums, share and cumulative totals. Trend analysis There are simple functions available for analysing trends based on year-on- year percentage change. Simple regression DecisionSuite offers no support for forecasting. It relies entirely on exporting data to Excel or external statistics packages for this function. Time series forecasting There is no support for advanced time series forecasting algorithms.

User-definable extensions The tool provides the ability to define advanced, custom calculations on-the- fly with regard to any dimension or attribute. These calculations can be saved back to the MDDB, so that other users can use them. A scripting language can also be used to create ‘add-ins’ that integrate with third-party products (such as SPSS) to access advanced analytical functions. However, this requires programming.

Write back for ‘what if?’ analysis ‘What if?’ or budgeting applications that need write access to the database require special handling. In most circumstances, the data warehouse tables will be read-only, so a separate set of tables will need to be created that support write-access. These will then need to be integrated with warehouse data via a custom-built application. Eureka:Analyst supports write-back, through its integration with Applix’s TM1 OLAP tool. Users can directly write-back to TM1 cubes for budgeting and forecasting type applications.

Incorporating non-numerical data Eureka:Strategy supports character string functions for comparing textual data. The results of counts for sub-strings and word patterns can be included in analyses. For example, the calculation builder allows analysts to create procedural ‘if then else’ type functions that compare text strings held as metrics. The calculation could return a text string for display or a number, which may be summed or counted, for example.

Data mining Eureka:Suite does not provide any support for data mining.

Other analytical functionality A number of functions are provided that are geared towards customer and product analysis that are aimed primarily at retail and sales & marketing analysis applications. These include market share, average inventory, rate of sales, BDI (brand development index) and SDI (share development index). Web support

Summary

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All the Eureka tools are becoming increasingly web-enabled. But rather than simply transferring client-server functionality onto a web browser, the product is based on Internet portal standards to provide access to a business intelligence data. While this approach has its merits, it places restrictions on the level of ROLAP functionality that can be effectively deployed through the portal. The two ROLAP Viewers provide strong web access to view predefined reports or analyse models respectively. However, web users cannot define new reports or add new filters or calculations to the report definitions. Reports can be easily published and distributed to a wide range of users over the Web using Internet technology, including hyperlinking and e-mail. Additionally, Eureka:Intelligence provides web users with a flexible and sophisticated query, reporting and analysis functions. There is no web support for Eureka:Analyst or the ROLAP Designer tools – though this is planned for the future.

End-user functionality via the Web Functionality of web access to explore models The web-based ROLAP Viewer provides the same level of OLAP functions and reporting as the client-server desktop tools. However, it is not possible to add new filters or calculations to models. Web users can register reports with Internet-based search engines, granting access to reports via hyperlinks. It is also possible to include sound and video objects in web reports. Eureka:Analyst does not support web access.

Supports both registered and unregistered web access Web users must be pre-registered. Range of users supported by the web interface The web-based viewers are well suited for general business users that require easy access to predefined or scheduled reports. Users wishing to define new reports or implement their own calculations are not supported. Eureka:Intelligence provides integrated query, reporting and analysis functionality through a Java client interface.

Creating models via the Web Editing the mapping layer It is not possible to edit the metadata tables via the web browser. Building and editing models It is not possible to create new model or report definitions.

Distributing via the Internet and the Web Generate HTML and Java All HTML generated by the web viewers is dynamic and is built up from templates and rules built into the user profile. No conversion is required, as reports are held in a neutral format and automatically converted to HTML on-the-fly when requested by a web client. Corporately organised distribution via the Internet Users can publish reports to Eureka:Portal for distribution over the Internet. Report casting facilities can also be used to dynamically distribute reports via e-mail over the Internet. Include URLs in a report Users can include multiple URLs in reports. The URLs can reference other reports.

Distribution of web server processing There is no integration with middleware to support distributed processing across multiple web servers. Management

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The management of Eureka:Suite can be complex – all the main components operate as separate systems, each with its own administration schemes and interfaces. However, user management is facilitated by links to LDAP and other directories for synchronisation and validation purposes. DBAs are provided with a number of tools and facilities to manage the ROLAP environment. The security of reports relies entirely on the Unix and RDBMS security. Agents are used for scheduling the distribution of report

objects and report updates and can be based on time, date or event triggers. As expected from a ROLAP tool, there is strong support for query monitoring and governance, and produces detailed usage statistics.

Management of models Separate management interface Several interfaces are provided to manage the back-end servers. For ROLAP administration, Eureka:Strategy provides two graphical interfaces that are similar in design: one is used for maintaining the metadata tables; the other is for administering application components, report objects and end users. Eureka:Analyst relies on the management capabilities that are provided as part of the Applix TM1 product. Security of models The security of ROLAP models is governed from a multi-level security model based on Unix, metadata and the RDBMS security systems. All models have associated properties that govern read/modify access. There are three levels of security provided by Eureka:Analyst: • data in the cube (database security) • access to the cube (Eureka:Portal security)

• inside the cube (based on native Applix TM1 security). Query monitoring Eureka:Strategy generates an audit log for each query and report generated, including the author, the time it was run and the tables it accessed. Administrators can also bring up the SQL generated, and re-run the query for audit trail or debugging purposes. They can also view and cancel background jobs. Eureka:Analyst does not provide any special facilities provided for monitoring OLAP queries.

Management of data How persistent data is stored (not scored Eureka:Strategy processes data directly from the RDBMS and creates multidimensional models at runtime, which are cached on the server. However, once a report has been defined, the data can be stored persistently on the ROLAP server or any other application server, and can be periodically refreshed for current data. Eureka:Analyst stores multidimensional cubes locally on the desktop, or on the Applix TM1 server. Scheduling of loads/updates The loading of data into the data warehouse is outside the scope of the Eureka:Suite. Once it has been loaded and stored as part of a report definition, a scheduler facility can be used to automate the refresh of reports. Scheduling can be based on times, dates or events. Users can apply a refresh schedule to a group of reports. Desktop cubes can also be refreshed by re- querying the data source.

Event driven scheduling Event triggers can be specified for updating existing reports or scheduling new reports. Triggers can be based on events such as an update to the data warehouse or events external to the OLAP environment. Failed loads/updates An agent may be set up to look for the completion of a report update and then alert users. All agent tasks are persistent, and are therefore automatically re-submitted if the update fails. Distribution of stored data Data is stored persistently in the database or cached on the ROLAP server (as part of a report). When a query is executed, the data is temporarily cached on the server at runtime; there is no caching on the client. Sparsity (only for persistent models) Eureka:Strategy uses two analytic ‘workspaces’ to efficiently process dense and sparse data returned from the RDBMS. The ROLAP server dynamically routes data to the optimal workspace based on its sparsity percentage of data returned from the database. For sparse data models, Eureka:Strategy automatically uses multidimensional b-tree, while for dense data models, data is returned as a multidimensional array. A fact filter examines data values before they are extracted from the database if the fact is stored – only those values that meet the fact condition criteria (for example, report only items with sales >$100,000) are retrieved and processed. Fact filters are particularly useful for ‘sparse data’ situations (large dimensions with many attributes). Methods for managing size Managing size is not an issue for ROLAP environments. The size of the server cache is subject to size restrictions based on query governance definitions. For desktop cubes, Eureka:Analyst compresses and loads only the base data into memory. Because the data is in memory, summaries and calculations are performed on-the-fly. The size of desktop cubes relies on native Applix TM1 or Microsoft OLAP Services constraints. In-memory caching options In-memory caching is not supported. Informing the user when stored data was last uploaded Each report is time-stamped with information about when the data was last updated – but this information is not automatically displayed.

Management of users Multiple users of models with write facilities Eureka:Suite is designed to permit simultaneous read-only access. User security profiles Role-based security can be applied across the entire system including all objects in the repository, business rules in metadata and data.

For ROLAP environments, security is implemented through the ROLAP Administrator. The ROLAP server uses a flexible security model to connect to the RDBMS, with anything from a one-to-one user to connection relationship, to all users sharing the same connection. User profiles grant access to parts of the ROLAP application environment and metadata. Profiles can be assigned on an individual or workgroup basis. The profiles are also closely linked to categories, which define user access to parts of the data warehouse and available calculations and filters. User profiling can be integrated with LDAP (lightweight directory access protocol), Novell Directory Services, Exchange and other directory services. This allows the Eureka:Portal to synchronise and validate against multiple directory services simultaneously. Query governance Administrators can define the maximum number of concurrent processes used by the ROLAP Engine at any given time – extra requests are queued automatically for processing as database connections become available. They can also control the maximum number of rows returned from the database on a user profile basis, and specify the maximum time an OLAP query is allowed to process in the database. Restricting queries to specified times There is no support for restricting queries to specific times of the day.

Management of metadata Controlling visibility of the ‘road map’ The category definition controls access to the metadata that a user can access. This definition determines the model metadata, calculations and filters that can be included in a report for a particular user or groups of users. Adaptability

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Eureka:Strategy’s metadata layer allows for an adaptable business model. New dimensions and measures can easily be defined and re-used across models. All additions are automatically time-stamped. Model metadata can be referenced to ensure that reports are kept synchronised at all times, but there are no facilities for keeping data sources and models in line. Users can access both ROLAP and multidimensional OLAP systems. However, the cubes generated by the multidimensional system lack most of the adaptability features covered in this section.

Change in business requirements Adding new dimensions to a model Eureka:Strategy allows new dimensions to be easily added to the metadata tables and subsequently used in reports. Each addition is time-stamped, but there is no direct support for change management.

Models generated by the Eureka:Analyst are less flexible and need to be completely re-built to support any structural changes or additions. Re-use of dimension definition New dimension definitions are stored in the metadata tables and can be re- used across multiple models depending on the access rights assigned to them. Adding new measures to a model New measures can be added to ROLAP models at any time by users, provided they have the necessary access rights. Folders exist within the model in which to save the calculation definitions, and Unix-style security is applied to them. Re-use of calculated measure definition When an end user creates a new calculated measure, the specification is stored in metadata tables and is available for use by other users with the appropriate access rights. Eureka:Analyst allows custom calculations to be saved back to the MDDB for re-use by other users. Changing the architecture to reflect business needs Eureka:Suite supports both a ROLAP system (Eureka:Strategy) and a multidimensional OLAP (Eureka:Analyst) mode of analysis. However, there is no integration between these two environments.

Changes to data sources Keeping the data source and model schema synchronised Users are not automatically informed of changes in the data warehouse when a report is opened. Automatic updating of members in a dimension As the data warehouse is the only source of information for Eureka:Strategy, new members are automatically available. There is no support to lock a level to prevent new members being automatically updated.

Metadata Synchronising model and model metadata Eureka:Strategy provides a ‘validation’ feature to ensure that metadata categories are synchronised with the metadata tables each time a report is created. Impact analysis The tools do not provide support for impact analysis. Metadata audit trail (technical and end users) There are no metadata audit trail facilities. Access to upstream metadata Eureka:Strategy integrates with Informatica’s Metadata Exchange architecture – enabling developers to view extraction and transformation metadata about the columns in the data warehouse that provide the data for the model. Sterling has also joined Ardent Software’s (now taken over by Informix) MetaConnect Co-operative for metadata integration.

Performance tunability

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Eureka:Strategy utilises the strengths of relational database technology, while ensuring that processing is optimised between the ROLAP server and the database using the most effective method and location. The tool also provides a number of performance-tuning services to minimise access times, such as multipass SQL, native SQL access (to Unix databases only) and SMP parallelism. The architecture also enables high user and job concurrency for OLAP applications. The performance of Eureka:Analyst is entirely dependent on the capabilities of Applix’s TM1 tool – Sterling does not provide any additional performance tuning features.

ROLAP Multipass SQL Eureka:Strategy automatically generates multipass SQL statements. Options for SQL processing An important feature of Eureka:Strategy is its ability to intelligently balance SQL processing between the ROLAP server and the database. Processing options include: join processing, which eliminates full outer joins of large tables aggregation processing, to support advanced totalling across multiple dimensions calculation processing, which eliminates the use of temporary database tables and provides support for non-SQL calculations. Speeding up end-user data access The ROLAP server’s data cache is volatile, and can be accessed to maximise data access times. Aggregate navigator Eureka:Strategy automatically accesses the highest level aggregate tables in the database to fulfil a ROLAP request and minimise response time. It calculates the Cartesian cross-product of dimensional data models, which then produces aggregate-level priority information.

MOLAP Trading off load time/size and performance Eureka:Analyst is a multidimensional OLAP system based on TM1. It compresses and loads only the base data into memory on the client machine. All aggregations and summaries are performed on-the-fly as requested. Data and subsequent calculations are stored in a very efficient manner for enhanced performance.

Support for multiple users Sterling claims that the ROLAP architecture can support high user concurrency without degrading performance. It has many customer sites with more than 1,000 concurrent users running reports against large data warehouses. Eureka:Strategy also provides scalable query governing, which manages application and database resources so that neither an individual nor a group of users can collectively decrease system performance as the number of requests increase. Eureka:Analyst is primarily designed for single-user access on the desktop, rather than a shared, multi-user cube environment.

Processing Use of native SQL to speed up data extraction Eureka:Strategy uses native SQL interfaces to connect to all the major RDBMSs. It also uses ODBC for Unix to connect to Red Brick, Teradata and HP-Intelligent Warehouse data warehouses. Distribution of processing A client request is automatically routed to the least utilised ROLAP server for processing. There is no automatic load balancing between these servers, because each functions independently. It is, however, possible to balance processing loads between the database and ROLAP servers. SMP support Eureka:Strategy takes full advantage of SMP technology. Customisation

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Eureka:Suite provides limited support for application development. Add-ins and a procedural scripting language are available to customise applications and reports. Generally, complex application development relies on the tool’s API, and the use of external development tools via DLLs.

Customisation Option of using a restricted interface Various aspects of the client tools’ interface can be modified to provide restricted or extended views and functionality. Ease of producing EIS-style reports Eureka:Strategy’s Administrator interface provides an add-in facility to define pre- and post-process operations in reports. Typically, these are calls to an external procedure, such as a Windows application or a Unix shell script, that are used to customise the execution or results of a report, or add new capabilities such as EIS displays.

Applications Simple web applications A web gateway API is provided for the development of simple EIS interfaces in HTML or JavaScript. Additionally, Eureka:Portal exposes an XML-based API that can be used by ISVs or VARs for custom development. Development environment There is no visual development environment. However, Eureka:Strategy provides a scripting language for defining procedures for interaction with external systems or data. The scripting language – a cross between Visual Basic and Unix shell scripts – uses the standard Unix ‘vi’ editor. Use of third-party development tools Eureka:Strategy’s client DLLs can be called by development tools such as Visual Basic, PowerBuilder and Visual C++.

Other customisation features Localisation Language support for English, French and German is available for all Eureka:Suite components.

Deployment

Platforms Client Eureka:Suite’s client components run on Windows 95, Windows 98 and Windows NT. Web access is supported via Netscape, Microsoft and Mosaic web browsers. The Report Viewers also provide support for Unix. Server Eureka:Strategy runs exclusively on Unix: HP-UX, AIX, NCR MP-RAS, SGI Irix, Sequent Dynix, Sun Solaris, DG-UX, DEC Unix, Siemens Reliant and Unisys SVR4. Eureka:Analyst supports Windows NT and Unix. Eureka:Portal runs on Windows NT and Unix (Sun Solaris, HP-UX and AIX). Data access Eureka:Strategy provides native access to Unix-based RDBMSs only. Databases supported include Oracle, DB2, Sybase, Informix, Tandem and MDI. ODBC for Unix database drivers are also supported to provide access to Teradata, HP-Intelligent Warehouse and Red Brick and other non-Unix sources. Eureka:Analyst can access multidimensional data held in Applix TM1 and Microsoft SQL Server 7.0 OLAP Services MDDB servers. Eureka:Reporter runs against all the major RDBMSs. It also includes access to transactional/ERP databases and SPSS databases. Eureka:Portal can connect to any JDBC-accessible database. Connection is required only for the portal repository and any data source can be linked (via URLs) as ‘content’ to the portal. Standards Eureka:Suite has its own proprietary client and server APIs. The Viewers support standard HTML, Java and JavaScript. Published benchmarks Sterling has not published any OLAP benchmarks for its Eureka products. Price structure Pricing for Eureka:Suite varies, depending on the type and number of client and server components licensed, and the level of functionality that is required. This ranges from: • $100 per user, for a simple portal implementation with basic query and report viewing capabilities • up to $1,000 per user, for advanced OLAP design, analysis and reporting. The pricing structure for the ROLAP system is aimed primarily at large- scale enterprise deployments. Entry-level pricing usually starts at $75,000.

WhiteLight Analytic Application Server

Summary

At a glance ...... 2 Terminology of the vendor ...... 3 Ovum’s verdict ...... 4 Product overview ...... 6 Future enhancements ...... 16

Commercial background

Company background ...... 17 Distribution ...... 19

Product evaluation

End-user functionality ...... 20 Building the business model ...... 22 Advanced analytical power ...... 24 Web support ...... 26 Management ...... 27 Adaptability ...... 29 Performance tunability ...... 31 Customisation ...... 32

Deployment

Platforms ...... 34 Data access ...... 34 Standards ...... 34 Published benchmarks ...... 34 Price structure ...... 34 Evaluation: WhiteLight Systems – WhiteLight Analytic Application Server Ovum Evaluates: OLAP

At a glance

Developer WhiteLight Systems, CA, USA

Versions evaluated WhiteLight Analytic Application Server, version 2.0

Key facts • A ROLAP-oriented tool that provides a multidimensional cache for OLAP calculations and includes a component-based development environment • Server runs on Windows NT and Solaris; clients run on Windows 95, Windows 98 and Windows NT • The WhiteLight OLAP products are designed to support ‘Integrated Decision Processing’ – where modelling, analysis and integration functions are hosted on an analytic application server

Strengths • Advanced predictive modelling techniques for financially-oriented ‘what- if?’ analyses • Sophisticated metadata exploration and model auditing tools

• Component-based development environment provides fast and easy deployment of custom analytical applications

Points to watch • Requires a clean data source – WhiteLight does not provide any scheduled ETL functionality of its own

• No ready-made web client for ad hoc analysis – web access relies on building custom clients using the set of ACE components provided • Advanced OLAP reporting and distribution relies on integration with third-party tools.

Ratings

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End user functionality

Building the business model

Advanced analytical power

Web support

Management

Adaptability

Performance tunability

Customisation

Terminology of the vendor

Data map A set of properties for elements in a measure dimension that tells WhiteLight where values in a database are found. For example, it specifies the database, table and columns to retrieve values from. Elements Elements are subordinate members in a dimension hierarchy. There are two types of elements: qualifiers, which represent objects such as customers, and measures, which represent quantitative values such as sales. Infospace A set of dimensions and elements that represent a set of useful cells in the model for a particular query. Typically, users create an infospace in a worksheet to provide an initial view onto meaningful data. Model A multidimensional business model that users create to represent corporate data for a specific aspect of their business. Physically, a model is a file stored in the repository and has one or more worksheets associated with it. Rules Used to derive the values for cells in a model. Rules are attached to elements in the model and can be a formula, a data map or a UEV. Schema The WhiteLight schema specifies the databases, tables, columns and joins to be used by models. UEV User-entered value. A rule created by a user in a worksheet cell that is not generated from the source database or calculated as the result of a formula. UEVs are commonly used to change the values in a model for ‘what-if?’ analysis. Worksheet A spreadsheet-like view of model data presented in tabular form. Worksheets are used for interactive OLAP analysis and reporting. Multiple worksheets can be associated with a model to provide different views of data held in the model.

Ovum’s verdict

What we think WhiteLight is best described as a ROLAP-oriented MDDB tool that provides a strong front-end to large data warehouses. The tool scales like a ROLAP tool, but also benefits from a shared multidimensional cache for enhanced performance. This hybrid architecture easily lends itself to large multi-user deployment. The provision of a component-based development environment and OLE DB for OLAP support also opens up the product to third-party development and integration. What sets WhiteLight apart from a straightforward OLAP server is the provision of a middle analytic application layer that hosts modelling, analysis and data integration features based on a set of CORBA services. WhiteLight currently has a lead over competitors with respect to its modelling capabilities. The tools support the creation of flexible business models that contain highly granular business rules and calculations that can behave differently, depending on the context they are used in. A unique feature is the metadata exploration capabilities, which allow users to gain a better understanding of a model’s underlying business logic. Another key strength is its predictive modelling capabilities – models are highly adaptable to change and can be tested under different scenarios. The product is therefore well suited to dynamic environments in which high volatility tends to be the order of the day, where the underlying customer base can quickly change, and whose analysts are comfortable with this complexity and uncertainty. WhiteLight is targeted at ‘complex ROLAP’ applications and is particularly well equipped to deal with the requirements of sophisticated financial analysis, particularly credit risk management and profitability analysis. Clearly, the product is most at home in the financial sector, and is most likely to expand its sales into that particular territory. But WhiteLight’s consultancy partners have the expertise required to develop modelling components relevant to some strictly non-financial problem domains, such as database marketing, brand management and customer churn analysis. WhiteLight does not offer an out-of-the box web client. Instead, it provides re- usable components to develop custom web-based analytic interfaces. Although this shortens application deployment cycles, web users are restricted by the level of functionality built into the components – the range is limited, but expanding. WhiteLight does not provide a scheduled ETL capability. This means that the WhiteLight server is either pulling data off single operational sources at runtime (with associated overheads and lack of integration) or accessing a pre-integrated data warehouse. If WhiteLight is to be a long-term player in the OLAP market, it will need an ETL integration strategy. We also have reservations about the market opportunities for WhiteLight’s products. The WhiteLight tools are mainly targeted at specialised analysts within large corporations – a relatively small group. Although the analytic applications market is set to grow significantly, the number of people requiring access to more complex analytics will not grow proportionately.

When to use WhiteLight is suitable if you: • want to support financially-oriented applications – particularly credit risk management and profitability analysis • want to build large, complex business models, and be able to understand and adapt them easily • require ROLAP-like scalability with the performance benefits of a MDDB for complex OLAP query and calculations • wish to deploy custom analytical applications across the enterprise using reusable components and models, and with minimal IS involvement • need to provide business scenario analysis applications that diagnose and predict the risks and opportunities using advanced predictive modelling techniques. It is less suitable if you: • are still rolling out data warehouse and datamart solutions • want an out-of-the-box data warehousing solution – WhiteLight does not provide any tools for managing data loads and updates • require only general business analysis – the modelling facilities are geared towards complex financial analysis of large datasets • are looking for web access that provides the same level of functionality as the client-server system • want to support mobile users or have a need to distribute business models via the Web

• require advanced graphical OLAP and reporting functions without having to use third-party tools.

Product overview

Components WhiteLight Analytic Application Server 2.0 consists of the following components: • WhiteLight Analytic Application Server • WhiteLight Workbench • WhiteLight Excel Add-In • Application component environment (ACE). Figure 1 shows the primary functions of the components and how they relate to client-server systems.

WhiteLight Analytic Application Server The WhiteLight Analytic Application Server functions primarily as a traditional OLAP server. But its also has a strong relationship to standard application servers – such as those provided by Sun, Netscape and Oracle. OLAP server Seen from an OLAP perspective, it is a mid-tier server that provides an OLAP engine, a metadata management tool and a multidimensional cache. It consists of three main components: • a ROLAP calculation engine, which generates and performs SQL queries. It also performs the necessary OLAP calculations in memory, and delivers query results to the client. Database performance is optimised by intelligently distributing query processing between WhiteLight and the data warehouse • MultiCache, a multidimensional results cache for frequently-requested data. As multiple users connect, MultiCache shares results from common queries and complex OLAP calculations to improve overall throughput • an object manager, for managing the repository. The repository stores metadata, database schema information and security information. The server has a multi-threaded architecture – it shares all server resources, such as caches, connections, query processors and queues. It supports native and ODBC database connectivity, CORBA, ActiveX and OLE DB for OLAP (as a data provider).

Figure 1 Component functions

OLAP design and analysis Application development Web access

Client WhiteLight Workbench ACE-developed analytic WhiteLight Excel Add-in web clients

Server WhiteLight Analytic Application Application component Server environment (ACE)

Application server The server also provides structured application-level functionality on top of a basic set of CORBA-based services – similar to an application server. However, in the case of WhiteLight, it also manages the execution of complex structured business rules alongside the manipulation of different objects (numbers, text and images) within a multidimensional framework. This functionality forms the basis of WhiteLight’s ‘integrated decision processing’ approach (see Using WhiteLight section).

WhiteLight Workbench WhiteLight Workbench is the client-side tool for: • business modelling and OLAP analysis • administering the WhiteLight Analytic Application Server. It consists of a number of tools for end users and DBAs. End-user tools For WhiteLight end users (analysts), the following tools are provided: • Modeller’s Workbench – a graphical tool for building analytical models. The modelling functionality includes ActiveRules, which enables analysts to model complex business problems using simple drag-and-drop methods. • Cell Explorer – a metadata exploration tool for viewing an individual cell’s value, address, parents and children

• Model Audit – an interface for creating a textual description of the elements and logic of a business model • Worksheet Tool – a graphical interface for creating and analysing worksheets for a multidimensional model. Worksheets provide a spreadsheet-like view of the model and allow users to navigate through models and perform OLAP functions • Worksheet Filter – provides filtering, sorting and ranking commands to narrow the data displayed in a worksheet • Charting Tool – enables analysts to visualise multidimensional information in a number of graphical formats

• Query Builder – enables advanced users to create query scripts that are submitted to the server. Queries are composed in HQL (Hypercube Query Language), a proprietary SQL-like multidimensional query language. Administration tools For WhiteLight administrators, the following tools are provided: • Server Console – an interface for monitoring client connections and setting cache parameters on the WhiteLight Analytic Application Server • Schema Explorer – a graphical DBA tool used to import database schema into WhiteLight Analytic Application Server and manage joins • Library Explorer – an interface for managing a repository of business models • Cell-level Security – a drag-and-drop interface for defining model security

• Users and Groups Explorer – a graphical interface used to manage end users and define access privileges • SQL Audit – a DBA tool used to monitor SQL queries generated by WhiteLight Analyst clients and executed by the WhiteLight Analytic Application Server to the data warehouse.

WhiteLight Excel Add-In This is a .dll add-in that allows users to interactively analyse WhiteLight business models and data, and generate reports using Microsoft Excel 97 and Microsoft Excel 2000 interfaces. The add-in is an OEM version of OLAP@Work technology.

Application component environment The WhiteLight Analytic Application Server also includes an Application component environment (ACE) for building web-based analytic applications. ACE consists of two major parts: • ACE Basics – a set of prebuilt application components written as JavaBeans or Active X components for connecting to, selecting, querying, analysing and presenting data. All components are re-usable and reside on the client machine • ACE Component Development Kit (CDK) – a toolkit for developing customised components using the JavaBeans component model. It enables developers to generate new components by either extending off-the-shelf ones or by creating Java components. Components created with the CDK can then be re-used in any analytic application in the same way as ACE Basics components. Analytic applications are assembled by dragging-and-dropping components using standard development or HTML-based web authoring tools (such as Microsoft FrontPage). The resulting applications are accessible via a Java- enabled browser (including Microsoft Internet Explorer and Netscape Navigator) or standalone desktop application. Developers can also use components developed by third-party vendors, such as KLGroup, RogueWave, FormulaOne and ThreeDGraphics, to provide more specialised functionality. WhiteLight provides ACE component ‘packs’ to support specific applications such as budgeting. Architectural options

Full mid-tier architecture WhiteLight does not support a full mid-tier architecture. A multidimensional cache is used for storing frequently-requested data for OLAP calculations. However, it does not constitute a full MDDB store.

Light mid-tier architecture WhiteLight is a ROLAP-oriented tool and supports a light mid-tier architecture. It stores data in a relational format (typically, in a data warehouse) and uses the mid-tier ROLAP server to handle and generate query requests from clients.

The WhiteLight Analytic Application Server makes it possible to build analytic logic in a middle tier by hosting modelling, analysis and integration functions. Additionally, the mid-tier server supports a multidimensional cache that is neither a MDDB, nor intended to replace a data warehouse. Rather, the MultiCache augments the processing power of the ROLAP engine by managing frequently requested data returned from complex data. WhiteLight integrates with relational data warehouses via ODBC. It can be used against a variety of database schema, including denormalised, star, snowflake and normalised. It also integrates with web-based data sources, such as HTML, XML and other MIME-based data types.

Desktop and mobile architecture There is no direct support for desktop or mobile architectures. However, because the WhiteLight server is an OLE DB for OLAP provider, these types of architectures can be supported using third-party OLAP tools that support the OLE DB for OLAP standard. Using WhiteLight Analytic

Integrated decision processing WhiteLight’s Analytic Application Server is specifically designed to provide core functionality for supporting ‘integrated decision processing’ (IDP). IDP assumes that decisions come at the end of a series of complex and interactive processes, comprising some distinct steps. For example, objectives are set by management, relevant data is identified and collected, the business problem is modelled, various outcome scenarios are evaluated by modifying the input data and a decision is finally taken and acted upon. This action may very well affect the objectives that were set at the outset – so the ‘decision loop’ becomes complete. WhiteLight aims to automate the latter stages by supplying an analytic server that provides flexible data access and application integration, business modelling, scenario (variable ‘what-if?’) analysis, business process automation and workflow features to produce a complete decision-processing system. A key part of IDP applications is user collaboration and workflow – where the output of one user’s analysis becomes the input to the next user. To support this requirement, WhiteLight provides strong multi-user write-back functions that can be used in situations where it is important to be able to dynamically update databases – typically, financial planning, approval and budgeting applications. All this functionality is encapsulated in a WhiteLight Analytic application.

Division of roles WhiteLight has a number of components that are employed by four different types of user: • administrators – install the WhiteLight Server and WhiteLight Workbench clients. They use the WhiteLight Workbench client for server- related tasks, including server configuration, cache management and creating and maintaining WhiteLight schema for bringing in source data. They are also responsible for setting user, group and model security

• analysts – use the WhiteLight Workbench analysis tools to build and analyse models, as shown in Figure 2). They build business models on top of data access components by dragging-and-dropping re-usable model components These users can also use the graphical ACE to build their own custom analytic applications and interfaces, by assembling a number of predefined OLAP components • developers – use the ACE to construct analytic applications that can be rolled out across the enterprise. They also construct specialised analytic components (using the CDK) that can be stored in a library and re-used across multiple applications • business users – are ‘information consumers’ that use the WhiteLight Workbench’s worksheets, charts and related features to view model information for a variety of business perspectives. Alternatively, the WhiteLight Analytic Application Server provides them with components to build and deploy their own analytic interfaces. However, this division of roles is merely a guideline; WhiteLight’s administrative and model-building roles can be performed by one person or several, depending on the skill level of the user(s).

Figure 2 WhiteLight Workbench analysis interface

MultiCache The most unusual feature of WhiteLight is how it exploits a multidimensional cache (called MultiCache) to perform complex OLAP calculations. MultiCache is RAM-based, and is designed to provide interactive response to queries. For example, a typical usage scenario might be when the detail data is managed by the RDBMS and retrieved quickly. High-level aggregates are stored on disk and managed by MultiCache, resulting in very quick response times. Finally, the mid-level aggregates are calculated on-the-fly. The cache is ‘self-tuning’ – as more mid-level aggregates are requested and then cached, MultiCache gets faster. MultiCache retains the most-requested information, while removing less frequently-requested data. MultiCache operates in the same way as an MDDB, and can either be preloaded for predictable performance or populated dynamically as data is requested. Multiple users can share a single instance of a model’s cached values in memory. However, scalability will be limited by the amount of RAM available.

Metadata exploration A valuable feature of WhiteLight is its support for guided metadata exploration and its model validation. It provides end users with a number of graphical tools for verifying calculation accuracy inside models. Cell Explorer WhiteLight’s Cell Explorer tool allows end users to graphically navigate metadata from complex and highly-derived calculations in the model, by examining rules, relationships and calculations of data from its point of origin throughout its database history. It is designed for analyst users that need to understand the underlying business rules and logic used to calculate data and the sources of the data. As Figure 3 shows, Cell Explorer shows the current cell address in the centre of the window, the children of the cell on the left and the parents of the cell on the right. Selecting a different dimension in the cell will change the children and parents to those in the selected dimension. Selecting a child or parent will navigate through the model, making that member part of the centre cell. This enables users to rapidly explore the model. The lower part of the window contains rules that are used to calculate the cell, as well as a textual description of the currently-selected element.

Figure 3 Cell Explorer

Formula bar The worksheet formula bar quickly shows how cells are calculated. End users can see exactly how data values in a business model are derived on a cell-by- cell basis. Model Audit A built-in feature that documents existing models and is used to track changes to models. Users can create and view a complete textual description of any model using the Model Audit interface. The Model Audit window, as shown in Figure 4, displays dimensions, hierarchies, rules, infospaces and database structures that comprise a model. It also provides details on model ownership and currency information (such as when it was created or last modified). WhiteLight provides facilities for the verification of calculation accuracy, by automatically performing consistency checks for cell errors and potential rule conflicts.

Figure 4 Model Audit window

ActiveRules WhiteLight’s patented ActiveRules technology allows it to build context- sensitive models, which include complex business calculations that behave differently depending on the context of their use. ActiveRules consists of two components – ‘rules’ and the ‘rules compiler’. WhiteLight models consist of business ‘rules’, which determine how information is derived for cells in the model. These rules are automatically processed by a dynamic compiler facility to create a runtime model. WhiteLight rules are ‘active’, because they are instantiated objects that know about one another in a semantic network. For example, the way ‘gross margin’ is calculated is different from how the percentage variance is calculated, so these elements have different rules. However, ‘gross margin’ could be calculated differently for products and customers depending on the context of the analysis. The intelligence needed to determine how to calculate rules for any given cell in the model is automatically managed by the ‘rules compiler’, which performs a semantic analysis of the rules to automatically determine rule applicability and ordering.

ActiveRules provides a repository for re-usable business models; rapid model development is possible by assembling mathematical modelling components in a graphical drag-and-drop environment, rather than having to write complex programs.

Building web-based analytic applications The focus of WhiteLight Analytic Application Server is to provide an easy-to- use environment for building custom analytic applications by following a simple three-step process: • data integration – data access components are used to identify input and output data sources. External data access objects include C++, Java, .dlls, DCOM, EJB, CORBA objects, HTML and XML. A server extension development kit is provided to build custom interfaces to data sources • business modelling – business models are constructed using WhiteLight’s graphical ActiveRules modelling environment • application assembly – customised web-based analytical applications are created by dragging-and-dropping presentation components into standard HTML documents. The resulting applications are accessible via any Java-enabled browser or standalone desktop application. ACE WhiteLight’s application component environment (ACE) provides a graphical, component-based environment for building custom web-based analytic applications, by dragging-and-dropping application components into web pages using standard Java and HTML authoring tools, as shown in Figure 5.

Figure 5 Developing analytic applications with ACE

Application builders are provided with a core set of Java and Active X application components (called ACE Basics), which are used to underpin analytic functionality in applications. Ten ACE Basic components are currently provided (although further components continue to be rolled out by WhiteLight). These are split into: • non-visual components – Co-ordinator (for data connectivity and query processing), Cube, Cube-Query and Probe • presentation and analysis components – such as Graph, Grid, Selector, Library Explorer and Forms, which support a range of OLAP analysis filtering, sorting, ranking and navigation functions. For specialised requirements, an ACE Component Development Kit (CDK) is provided to create new components using the JavaBeans component model. The CDK enables developers to create new components by extending off-the- shelf or custom Java components with ACE methods. Components created with the ACE CDK can then be re-used in any analytic application in a similar way to the ACE Basics components. The ACE CDK is also compatible with a variety of Java interactive development environments. Developers can use components developed by third-party vendors such as KLGroup, RogueWave, FormulaOne and ThreeDGraphics. These include components with advanced functionality, such as geographical mapping (GIS) or application-specific analysis. ACE components can be dragged-and-dropped into a web page using standard web page layout tools. Application developers do not need to program linkages between components, neither do they need to know CORBA, as WhiteLight provides the necessary class libraries. ACE analytic applications are accessible via any JDK 1.1-compliant web browser.

Future enhancements

WhiteLight Systems plans a number of new features that will start to appear in the first quarter of 2000, including: • support for server-side ACE components – these components currently run on the client machine as Java applets. The aim is to provide a thinner (HTML) client, which will enable access from a corporate intranet, extranet or the Internet • a layer of XML integration and communication facilities – allowing navigation into XML-based document repositories • relevance ranking on documents – including support for keyword searches. A number of new ACE components will also be delivered throughout 2000. These include Drill Through, Cell Explorer, Calculated Write-Back, Personalise, Animator (which graphically depicts the changes of data values over a time period) and GIS. Integration with third-party OLAP servers (via OLE DB for OLAP) and Microsoft Repository (for common metadata access) are also planned for the longer term. WhiteLight also plans to deliver several new applications for the financial sector and additional vertical industries such as: • electricity/power trading and risk analysis

• insurance underwriting applications – for example, chain ladder analysis • e-commerce – such as clickstream analysis • network analysis – for ISPs or companies engaging in the provision of network or intranet services.

Commercial background

Company background

History and commercial WhiteLight Systems is a US start-up founded in 1992. The company initially focused on providing object-oriented software tools based on NextStep. In 1994, WhiteLight Systems partnered with Sybase on the joint ‘Highgate’ OLAP project. In December 1997, version 1.0 of WhiteLight was formally released. WhiteLight is marketed as an independent OLAP product and is also offered as an OEM extension to Sybase’s Warehouse Studio under the brandname Power Dimensions. WhiteLight Systems has since been promoting an adaptive server and component architecture, which relies on middleware to bond a set of multiple data sources within an application. It is now advocating a concept known as ‘integrated decision processing’ (IDP) – effectively a strategy for handling the complex issues involved in taking business decisions, backed up by version 2.0 of its Analytic Application Server software and related technologies. WhiteLight Systems is a privately-held company. It has received $22 million in funding from Warburg Pincus, Sybase, General Electric and several other venture capital companies – Sybase still has a 2% stake in the company. WhiteLight Systems does not disclose financial information, but claims to be profitable. Ovum estimates its revenues to be around $12 million. WhiteLight Systems employs approximately 90 people worldwide, and is currently expanding into Europe. It has its corporate headquarters in Palo Alto, California and a regional office in New York. The company’s European operations are based in Bracknell, UK.

Character and direction Compared to some of the larger, established OLAP vendors, WhiteLight Systems is a relative newcomer to the business intelligence market. The company is heavily backed by venture capital and its youthful management exudes a high degree of confidence. The company continues to grow rapidly and to expand further in new geographic areas. We expect that it has had its last venture capital round of funding and will soon go public. WhiteLight is doing some pioneering work in business intelligence and is defining a new classification of decision support software. While many vendors are focusing solely on OLAP to support data analysis, WhiteLight proposes a new model of decision support called ‘integrated decision processing’ (IDP). The WhiteLight Analytic Application Server is designed specifically to support IDP applications; automating the entire process of complex decision-making, from accessing and integrating information to business modelling and testing business decisions. WhiteLight is sold directly in North America and Europe, and increasingly through VAR and OEM relationships (notably with Sybase) worldwide. WhiteLight Systems has aimed its OLAP tools at the financial sector, which accounts for more than 50% of its revenues. It is quickly carving out a niche for itself in this market by providing an extensible set of tools that it can sell into financially-oriented businesses, as well as large organisations with significant sophisticated financial analysis requirements. The financial focus of the company is highly evident from its customer base. WhiteLight has

about 50 customers worldwide, and most of these are large institutions drawn predominately from the banking, insurance and financial sectors. For example, Barclays Bank in the UK uses WhiteLight to analyse the impact of credit risk in order to minimise loan default rates. Other major customers include the Bank of America, Citibank, GE Financial Assurance Holdings, Internal Revenue Service and UBS. However, the architecture of the Analytic Application Server is equally well equipped to support other application areas. WhiteLight notes database marketing, category management, customer churn management and brand management as emerging application areas. Key verticals in this area include telecommunications (Bell Atlantic) and consumer packaged goods (Unilever and Birdseye). WhiteLight operates an application partner programme, involving systems integrators, consultants and application companies that develop packaged analytic applications or specialised solutions for large enterprises. Key partners are: • Pinpoint Solutions – which has already released a property and casualty actuarial analysis system called ProfitCube • Harte Hanks – which develops high-end database marketing applications based on WhiteLight technology • Algorithmix – which has OEM’d WhiteLight Analytic Application Server to develop credit and risk management solutions.

WhiteLight also has technology and marketing partnerships with Ardent, Brio Technology, Informix, Microsoft, Oracle, Seagate Software, Sun and Sybase. Customer support

Support The Technical Support Services group offers telephone hotline, e-mail and web support in North America and Europe. Support is also available in other territories through Sybase, VARs and business partners.

Training Public and on-site training is provided for all WhiteLight modules, including courses on modelling fundamentals, model building and systems administration. A ‘train the trainer’ programme is also available.

Consultancy services WhiteLight Systems provides a range of generic consulting services for product integration, deployment and application design via its Solutions Centre organisation. The Financial Solutions Group (FSG) provides a range of professional services aimed specifically at the financial services industry. Central to the FSG is the Enterprise Risk Architecture, a set of application templates and financial models for profitability analysis and risk management applications – Portfolio Management and Risk-adjusted Profitability. Around 20% of its revenues comes from consulting, but WhiteLight expects the services side of its business to grow substantially.

WhiteLight also has a number of consultancy partners, including AnswerThink Consulting Group, Archer Decision Sciences, Dimensional Systems and First Derivatives. Distribution Head office WhiteLight Systems (corporate headquarters) Suite 100, 2191 East Bayshore Road Palo Alto, CA 94303 USA Tel: +1 650 843 3000 Fax: +1 650 843 3910

Europe WhiteLight Systems Unit 4, Bracknell Beeches Old Bracknell Lane Bracknell Berkshire, RG12 7BW UK Tel: +44 1344 310070 Fax: +44 1344 310071

E-mail: [email protected] http://www.whitelight.com

Product evaluation

End-user functionality

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WhiteLight’s Workbench tool provides a functional and easy-to-use spreadsheet-like interface for accessing and analysing models. Business users can easily apply their own business rules and calculations directly from the spreadsheet. Excellent graphical metadata tools are provided for viewing the underlying business logic of large, complex models. Additionally, the WhiteLight Server acts as a data provider to any OLE DB for OLAP clients. Custom web-based analytical front-ends can also be built using the ACE components. But there is limited native support for graphical presentation and OLAP reporting. Advanced data visualisation and report distribution is also provided via integration with these third-party tools. Generally, the Workbench is the best interface to use for complex analysis functions.

Finding and understanding the model Finding and loading a multidimensional model The Library Explorer provides a graphical interface for viewing and working with models. Models can be organised according to users or groups. End users can search for models by owner name. They can also search for specific worksheets associated with a model. Metadata for end users Within the worksheet, a formula bar quickly shows how cells are calculated, while the Cell Explorer allows users to view data sources and uses of information. A model property sheet can also be viewed, which shows the model’s owner, the date it was created and last modified, as well as a textual description of the model. Annotation by end users The Audit tool allows end users to create their own textual specifications for models. However, it is not possible to annotate dimensions directly.

Using the model Basic OLAP functionality The worksheet provides a graphical Excel-like spreadsheet interface for analysing models. Standard OLAP functions, such as pivot, drill-down and drill-across are available directly from the spreadsheet via point-and-click. Changing the position of members in a dimension level Dimension members can be moved in a dimension level using drag-and-drop.

Visualising the drill-down hierarchies A graphical display of the dimension hierarchies is provided by the Workbench tool. Drilling down to detailed data End users cannot drill-down to transactional-level detail inside the WhiteLight server. It uses a CORBA extension, which executes SQL with parameters for this capability. Range of front-end user tools The WhiteLight Server supports a number of interfaces to enable client tool access and allow packaged applications to leverage the server. Through OLE DB for OLAP, the WhiteLight Server can integrate with Excel (97 and 2000), and any OLAP tools compliant with OLE DB for OLAP as a consumer, including Brio, Business Objects, Cognos, Knosys, Portola, Seagate, Hummingbird and OLAP@Work. However, only part of the WhiteLight Server’s functionality can be exposed in this way. An Excel add-in is also provided. WhiteLight provides C++, Java and COM-based APIs for customised client interfaces. Additionally, the ACE development tools can be used to create custom web clients for specialised analysis. Visualising the results Results data is displayed in a tabular spreadsheet-like format. WhiteLight’s wizard-based charting tool supports a range of two- and three-dimensional charts that are linked to worksheets. Chart types include series and plot graphs.

Saving and sharing results Designing a report WhiteLight provides basic formatting and layout options, including support for nested cross-tab reports. MIME content (that is, sound, images, video and text) can be included in reports. However, WhiteLight relies on third-party tools for advanced report construction. Publishing a report WhiteLight allows users to publish models in the repository for shared development and analysis. Scheduling and distribution functionality relies entirely on third-party tools. Targeted distribution via e-mail There is no direct support provided for e-mailing worksheets from within WhiteLight. Subscribing to reports WhiteLight does not support any report subscription services.

Building the business model

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One of WhiteLight’s greatest strengths is its support for all aspects of the model-building process. Graphical and easy-to-use modelling tools are provided to map data sources to multidimensional data schemas. Analysts can use the schema to develop ‘base’ models that can easily be extended, by applying complex and highly granular business rules and calculations. The initial construction of ‘base’ model components is carried out up-front (without end-user intervention). WhiteLight’s ActiveRules feature provides a repository for re-usable components and a high-level interface for building models that avoids the need for complex programming tasks. Unlike traditional ‘black box’ approaches to business modelling, the tools provide a range of audit and integrity features. Multiple designers are well supported by a version-controlled repository. A bonus is the tight integration with Sybase’s PowerDesigner data modelling tools.

Basic design Design interface WhiteLight uses point-and-click functions for almost all aspects of the model- building process. Models are created by dragging-and-dropping re-usable model components (such as dimensions and measures) in a graphical environment. Visualising the data source The Schema Explorer can be used to view the underlying database schema, but there is no support for data sampling. Universally available mapping layer There is no ‘universally’ available mapping layer. Instead, the ‘base model’ provides the initial mapping to disengage data analysis from underlying relational database concepts. Prompts for metadata Model designers are not explicitly prompted to provide contextual metadata. However, fields to capture such metadata are prominently displayed in the design interfaces.

Building the dimensions Selecting columns for the dimensions Columns can be mapped to dimensions using point-and-click. Selecting the members shown in a dimension level Members in a dimension level can be selected using point-and-click only. Similarly, users can create or redefine parts of a model, by introducing new collections of members via drag-and-drop.

Defining a dimension hierarchy Hierarchies can be built and modified graphically in the Hierarchy Definition window. WhiteLight automatically implements ‘self-joins’ to support irregular (ragged) hierarchies, where some branches of the hierarchy are shorter than others. Time dimension When designers create a model, WhiteLight automatically creates a time dimension. Standard time units (such as business quarter and fiscal year) are supported. Model designers can create their own custom units, including dynamic ‘year-to-date’ time dimensions. Annotating the dimensions Support is provided for defining short and long names for dimension members. Default level of a dimension hierarchy Default levels can be defined on a per-model basis.

Defining the measures Calculated measures Formula-based rules are used to calculate derived values of cells in a model. They are created in the workbench via a point-and-click interface. WhiteLight supports two types of formula: aggregate (sum, ‘avg’, ‘min’, ‘max’ and count) and custom (created using mathematical, logical and comparison operators and operand functions). Custom formulas are expressed in a similar way to common spreadsheet formulas, with a simple calculator-type interface or by manual typing. Support for multiple measures with a set of dimensions Multiple measures can be associated with a set of dimensions in a model.

Multiple designers Multiple designers Other than standard model-locking mechanisms, there is no special support for multi-designer environments. Support for versioning Multiple copies of models can be saved in a repository. This enables different analytic applications to be managed on the same server, each utilising different iterations of the same base-level model. However, this is not true version control.

Other ‘building the business model’ features WhiteLight interfaces with Sybase’s PowerDesigner modelling tool. It is possible in PowerDesigner to visualise the database and automatically create a WhiteLight schema and a base model.

Advanced analytical power

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WhiteLight is targeted at ‘complex ROLAP’ applications. It therefore offers greater support for complex analytics than most ROLAP tools in its class. It supports a range of financial, statistical, time-series and other mathematical modelling techniques that are geared towards financial analysis. WhiteLight’s strong support for write-back supports a variety of predictive modelling and ‘what-if?’ analysis methodologies for planning, forecasting and simulating the effects of decisions. Users can vary scenarios through modifications to values, structures or formulas in the model. External analytical components, such as proprietary analytical libraries, can also be integrated into business models – by ‘wrappering’ them as WhiteLight-specific components

Third-party tool integration Integration with Excel is provided. External analytics, such as advanced statistics and datamining, can be integrated as drag-and-drop model components. However, there is no direct integration provided with spreadsheet tools or third-party statistical analysis packages (such as SPSS analytical libraries).

Defining specialised models Ranking and sorting Standard ranking (top, bottom and rank number) and sorting functions are supported on cell values, as well as dimension members and attributes. Mathematical methods WhiteLight supports standard arithmetic functions, such as exponential, logarithms, polynomials, absolute values, multinomial, power, sign and round-up/down. Financial functions A range of financial functions is provided, including internal rate of return (IRR), net present value (NPV) and future value (FV). In addition, the Portfolio Management and Risk-adjusted Profitability modules also provide specialised functions for measuring risk and profitability, including exposure, expected loss, risk-adjusted capital requirements, risk-adjusted return and Sharpe ratio. Statistical models WhiteLight supports more than 25 statistical functions and methods, such as correlation, co-variance, standard deviation, geometric and percentile. Trend analysis Trend analysis in WhiteLight is supported by modifying historic performance data customised with simple growth factors. The ‘forecast’ function can be used to return a value along a linear trend. Non-linear predictive models are also supported.

Simple regression WhiteLight supports simple linear regression functions, such as slope, intercept and forecast. Multiple regression techniques are also supported. Time-series forecasting WhiteLight provides support for lead and lags, enabling simple time-series analysis functions. However, it does not support any advanced de facto time- series forecasting algorithms.

User-definable extensions The analytical capabilities of the WhiteLight Server can be extended by defining and/or adding external analytical components (such as risk management engines or specialised statistics) via the CORBA extensions. These analytical components are re-usable and can easily be applied to business models.

Write back for ‘what-if?’ analysis Predictive models can be constructed that require ‘what-if?’ analysis. They will generally be based on historical data, but with adjustments made to address future scenarios. WhiteLight supports multiple types of ‘what-if?’ analysis and allows any model component (formulas, rules, cell values or dimension/member structures) to be changed on-the-fly. Write-back for ‘what-if?’ analysis is supported in two ways: • allowing end users to enter values (called UEVs) directly into a model’s worksheet. UEVs are not actually stored in the worksheet, but are available to any worksheet that uses the model • allowing end users to enter UEVs that write back to the source database where values were originally derived.

In addition to changing cell values, users can also define new business rules to apply to models and change hierarchies to analyse the effect of alternative roll-up calculations. ‘What-if?’ scenarios can be saved and published.

Incorporating non-numerical data A range of data types can be included in analyses. WhiteLight supports non- numeric data types, such as binary, Boolean, text, documents and other MIME-based functions. Support is provided for basic date, float and string manipulation. An example would be date manipulation in an insurance application or handling URL ‘strings’ that refer to documents and processes outside the WhiteLight environment.

Datamining There is no direct support for datamining. However, datamining analytics (such as cluster analysis) can be integrated into applications as model components.

Other analytical functionality A key analytical feature of the WhiteLight Analytic Application Server is its ability to associate a single cell with more than a single value; for example, it is possible to store a ‘vector’ of numbers within a single cell. This is particularly useful for testing a number of different scenarios. Support is provided for stochastic simulation models, including Monte Carlo modelling.

Web support

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There is no standard web client provided – web access is only provided through custom web-interfaces that need to be built using Java-based components provided by WhiteLight’s ACE development environment. Web functionality is restricted by the range of ACE components available – these components offer fairly rigid functionality and are limited by the peculiarities of HTML and Java. WhiteLight’s modelling environment and metadata tools are not accessible via the Web. Generally, business users will be satisfied by the web tools, but analysts with more complex analytical requirements will be better served by the client-server Workbench tool.

End-user functionality via the Web Functionality of web access to explore models The level of functionality is restricted by the ACE components available. Standard OLAP analysis and presentation components are provided for drill- down, pivoting, filtering, sorting and ranking. However, more sophisticated functionality either needs to be built from scratch (using the ACE SDK) or sourced from component partners. WhiteLight’s metadata exploration (Cell Explorer) capabilities are not available via the Web – though this is a planned component. Supports registered and unregistered web access All WhiteLight users need to be registered to access the system. Currently, there is no support for dynamic subscription. Range of users supported by the web interface Sufficiently high-level web interfaces can be built to cater for the needs of general business users. Similarly, a higher degree of sophistication can be introduced for more complex analysis needs. However, business analysts with specialised and complex analytic requirements will be better served by the client-server tools.

Creating models via the Web Editing the mapping layer It is not possible to edit the WhiteLight Schema via the Web. Building and editing models It is not possible to define new, or edit existing, models via the Web.

Distributing via the Internet and the Web Generate HTML and Java The tool can generate HTML, but not Java.

Corporately organised distribution via the Internet There is no direct support for distributing reports via the Web. The distribution of models is achieved through the construction of an analytical application (using the Java-based ACE components) that provides distribution concepts, such as push (e-mail), pull (on-demand), offline scheduling and ‘briefing books’. Include URLs in a report URLs can be included in WhiteLight reports.

Distribution of web server processing WhiteLight does not provide any facilities for distributing the web server processing across multiple web servers. Management

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Most administrative functions can be accessed through a clear graphical console. WhiteLight provides a comprehensive multi-layered security model for data and end users. Security also benefits from WhiteLight’s ActiveRules technology. As with most ROLAP tools, there is strong support for query monitoring. However, scheduling relies entirely on the use of third-party tools. WhiteLight would benefit from native control of data load and update processes to improve its overall manageability.

Management of models Separate management interface The Server Console provides the main graphical administration interface for monitoring client connections, managing model and end-user security, and setting cache parameters. Security of models The server supports database, report-level, cell-based and network-based security. Read-write access to models (down to cell level) and the underlying database can be granted to users and groups of users on a per-model basis. Security ‘domains’ specify which cells can be seen or written back to. Model owners and systems administrators can both assign security. Security controls also benefit from WhiteLight’s ActiveRules technology, and are automatically updated when a change to the business model is made. Query monitoring All OLAP queries are recorded in the WhiteLight server log and can be used to generate usage statistics. The log file includes information about the SQL generated, the associated cache entries, the author, and the date and time the query was executed. However, administrators cannot edit the SQL generated by the WhiteLight server directly.

Management of data How persistent data is stored (not scored) The data used for analysis is typically stored in a relational database, and can thus be managed using standard database utilities such as backup, import and export. WhiteLight does not store persistent versions of models in an RDBMS; the WhiteLight repository, which stores models, metadata and reports is proprietary. Calculation results can also be cached persistently on the WhiteLight server (in the MultiCache). The advantage of using cached data is that it speeds up query times. Scheduling of loads or updates The WhiteLight server exercises no control over the loading or updating of data into the data warehouse or any other source databases. However, third- party tools can be used to upload the MultiCache after updates to the data warehouse and to refresh reports. Event-driven scheduling Event-driven scheduling is not directly supported. Failed loads or updates WhiteLight has no control over the loading or updating of data into the data warehouse, so there are no facilities for reporting failed loads or updates. Distribution of stored data A partitioned data feature allows data to be divided amongst multiple tables, databases or WhiteLight Servers. Data can also be cached on clients, and a mixture of caching options is supported. Sparsity (only for persistent models) Sparsity handling is not a major issue for ROLAP-oriented products such as WhiteLight. It is typically handled by the RDBMS, where the majority of processing occurs. Methods for managing size The WhiteLight Server places no limits on database size or dimensionality. However, the size of the MultiCache is restricted to 2Gb. Administrators cannot remove specific cache entries, but they can limit the overall size of the cache and number of entries. In-memory caching options Administrators can determine how best to optimise the allocation of cache memory resources; for example, specifying a maximum number of cache entries. Informing the user when stored data was last uploaded Each WhiteLight model has a timestamp that shows when it was last refreshed (typically, when the data warehouse is updated). This information is easily accessible by end users.

Management of users Multiple users of models with write facilities WhiteLight supports multi-user write-back to the data warehouse by using supported interface standards. WhiteLight relies on the underlying RDBMS for transactional integrity for all updates, by ensuring that all cells on a single update are batched. If a cell value was to fail, all cells would be rolled back and an error message generated for all users. The MultiCache is ‘locked’ for all other users when a write-back is being made. User security profiles User security is performed graphically using the ‘users and groups’ interface. Three levels of access can be specified – end user, analyst and administrator – and each has increasing levels of services and capabilities. A graphical interface is provided for assigning users to groups and defining model access privileges. Query governance Query governance is enforced at the database level and relies mainly on the facilities provided by the source RDBMS for query control. WhiteLight can also restrict certain calculations by using rule ‘domains’ that restrict the maximum number of rows a query can return to the server. Restricting queries to specified times It is not possible to restrict queries to specified times.

Management of metadata Controlling visibility of the ‘roadmap’ There are no special features to control the visibility of metadata. Adaptability

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WhiteLight’s strong focus on supporting predictive modelling and scenario testing requirements means that models need to be highly adaptable to change. In WhiteLight, adaptability is generally a case of being able to add new dimensions and measures, and re-use the definitions. However, adaptability is also significantly enhanced by ActiveRules and referential integrity – which prevent users from invalidating models when changes are made. The metadata browsing capabilities also boost adaptability and ease the maintenance of models. But there are no facilities to track changes. Also, there are no facilities provided for keeping data sources and models in synch with metadata.

Change in business requirements Adding new dimensions to a model New dimensions and members can easily be added to a model. The definitions can exist in the database or be defined locally in the model. Existing dimensions can also be renamed. Changes to models are propagated immediately and are supported by referential integrity – this restricts the deletion of members referred to elsewhere in the model. But there are no change management facilities to track the changes. Re-use of dimension definition Through the use of a base model, dimensions can be re-used. Adding new measures to a model New measures can easily be defined and added to models using point-and- click. Referential integrity is guaranteed for all measures and formulas in the model. Again, there are no change management facilities. Re-use of calculated measure definition Measure definitions (including rules, calculations and metrics) can be saved, stored as components and re-used across different models and applications. Changing the architecture to reflect business needs Depending on how WhiteLight is configured, it can operate in pure ROLAP mode or it can exploit the multidimensional cache to gain the performance benefits of a MOLAP environment.

Changes to data sources Keeping the data source and model schema synchronised WhiteLight does not automatically synchronise with changes in the data warehouse. The administrator needs to invoke the process at a suitable point in time. If databases, tables or columns are removed from the data warehouse, models that reference them are tagged as obsolete and users will be notified with an error message. Automatic updating of members in a dimension When refreshing a WhiteLight Schema, WhiteLight adds any new columns and rows that belong to tables in the schema.

Metadata Synchronising model and model metadata There is no automatic support for keeping WhiteLight Schema and other metadata descriptions synchronised with models. When items are removed from WhiteLight Schema, models that refer to them cannot be accessed. Impact analysis There is no direct support for analysing the impact of a change across related models. However, WhiteLight’s concept of ‘referential integrity’ restricts the deletion of dimension elements that are referred to elsewhere in the model. Metadata audit trail (technical and end users) A metadata audit that shows the history of the metadata is not supported.

Access to upstream metadata There is no direct access to upstream metadata (expect with Sybase’s PowerDesigner tool). In October 1998, WhiteLight joined the Ardent Software MetaConnect Co-operative, an integration programme designed to share data warehouse metadata. Integration with Ardent extends the guided metadata exploration capabilities provided by Cell Explorer to examine ETL rules for data derived in WhiteLight models, and allows users to navigate back to its source database origin. Performance tunability

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WhiteLight scales well – like a classic ROLAP product. Significant performance benefits can be gained from fine-tuning the multidimensional cache for complex OLAP calculations – avoiding the need to revisit data sources to satisfy queries. The system also supports distributed processing; the WhiteLight Server analyses both queries and the performance of the RDBMS to determine where best analytical processing should occur. By default, WhiteLight will calculate as much as possible in the RDBMS, but complex, multi-step calculations and advanced functions are typically brought up to the server. Other performance-enhancing features include multipass SQL and a multi-threaded SMP architecture. These factors combine to enable scaleability – potentially, up to a thousand users per server – without degrading performance.

ROLAP Multipass SQL WhiteLight supports the automatic generation of multipass SQL. Options for SQL processing Query processing can be optimally distributed between the WhiteLight Server and the RDBMS – depending upon the type of analysis and available resources. Around 30 SQL optimisation routines are provided to balance the processing. The system also minimises the generated SQL complexity to ensure compatibility with the RDBMS’s optimiser. Speeding up end-user data access The MultiCache can be used to cache some query results for faster performance, which allows the need to re-visit data sources to satisfy queries. In response to a new query, the calculation engine will check the cache and use pertinent cached values before it performs additional data retrieval or calculations. The MultiCache is ‘self-tuning’: the more it is used, the faster it becomes (provided there is substantial re-use of data between subsequent queries). Aggregate navigator An aggregate navigator is built into WhiteLight’s query optimiser tool. The aggregate navigator employs database row counts that direct queries to the smallest table that can resolve the query. WhiteLight automatically finds the ‘next best fit’ if there is no aggregate table with the exact level of aggregation available.

MOLAP Trading off load time/size and performance Multidimensional caching (MultiCache) provides fast query response for frequently-requested information, by caching complex calculations in the WhiteLight Server The MultiCache is not a true MDDB. Rather, it can be considered an in- memory cache that can be preloaded and/or loaded dynamically based on user queries.

Support for multiple users The maximum number of users that can be logged-in to the WhiteLight Server peaks at around one thousand. In terms of performance, the main issue is the time taken to resolve queries. If the system is configured to use the multidimensional cache as much as possible, then performance is significantly enhanced.

Processing Use of native SQL to speed up data extraction WhiteLight uses ODBC for connectivity to most RDBMSs. Native access is only provided for Sybase. Distribution of processing WhiteLight supports partitioned databases across multiple servers; query processing can be distributed across the appropriate partitions. SMP support WhiteLight Server supports multi-threaded SMP. Customisation

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The ACE development tools simplify the process of creating and deploying a custom web-based analytical interface. It simplifies the application development by the provision of re-usable application components written in Java, which are capable of automatically finding and interacting with each other. Applications can therefore be quickly assembled by dragging-and- dropping pre-defined analytic components on a web page using standard web layout tools. The approach is best suited for business analysts, rather than complex application development – the tools are object-based rather than object- oriented. For more specialised applications, a development kit is provided for creating new components.

Customisation Option of a restricted interface WhiteLight Analyst provides three types of interfaces that offer decreasing levels of functionality for administrators, analysts and casual end users. NetPublish also supports restricted interfaces. Ease of producing EIS-style reports WhiteLight provides a wizard-based briefing book construction toolkit for publishing EIS-type interfaces. Alternatively, briefing books can be built via drag-and-drop using web-authoring tools (supported by ACE).

Applications Simple web applications ACE provides a suite of Java-based components that can be dragged-and- dropped onto any web page using standard authoring tools. These components are also available from JavaScript programming interfaces. Development environment ACE supports a visual drag-and-drop environment for the development of web-based analytic applications. A pre-built library of re-usable components is provided. For specialised requirements, ACE also includes a Component Development Kit (CDK), which allows developers or third parties to create new components and add them to the ACE environment. Use of third-party development tools There is no integration with third-party client-server development environments such as Visual Basic or PowerBuilder. However, ACE developers can use any web-authoring tool that supports Java and HTML, such as Microsoft FrontPage, Visual Interdev, NetObjects Fusion, and certified Java development environments such as Microsoft Visual J, Sybase Power J, Inprise Jbuilder and Symantec Visual Café.

Deployment

Platforms Client WhiteLight Workbench runs on Windows 95, Windows 98 and Windows NT workstations. ACE assembled applications can operate in any JDK 1.1- compliant web browser – including Microsoft Internet Explorer and Netscape Navigator. Server The WhiteLight Analytic Application Server runs on Windows NT and Solaris. Data access The WhiteLight Server can access Sybase (Adaptive Server/IQ), Oracle, Informix ODS, Microsoft SQL Server, IBM DB2, Red Brick Warehouse, NCR Teradata Microsoft SQL Server and other ODBC-accessible RDBMSs. In addition to RDBMS support, the WhiteLight Server can access data from spreadsheets, ERP applications, legacy applications, realtime data feeds and web-based data sources, such as HTML and XML. Integration is through a combination of OLE DB for OLAP, CORBA and MIME standards. Standards The WhiteLight Server supports Microsoft’s OLE DB for OLAP API as a data provider. Published benchmarks WhiteLight does not have any published OLAP benchmarks. Price structure WhiteLight Analytic Application Server configurations, with a five-user licence, start at around $70,000 – the WhiteLight Workbench-client and ACE-client components are included in the cost. The Excel client add-in costs around $200 per client. The Portfolio Management and Adjusted Profitability modules are priced at $100,000 each, and include implementation services.