sign in sign up
<<
Home , Business intelligence

Portland State University PDXScholar

Engineering and Technology Faculty Publications and Presentations Engineering and

10-8-2018 Unified Intelligence cosystem:E A Approach to Address Business Intelligence Challenges

Bhavana Ramesh Portland State University

Akash Ramakrishna Portland State University

Follow this and additional works at: https://pdxscholar.library.pdx.edu/etm_fac

Part of the Engineering Commons Let us know how access to this document benefits ou.y

Citation Details B. Ramesh and A. Ramakrishna, "Unified Business Intelligence cosystem:E A Project Management Approach to Address Business Intelligence Challenges," 2018 Portland International Conference on Management of Engineering and Technology (PICMET), Honolulu, HI, 2018, pp. 1-10.

This Article is brought to you for free and open access. It has been accepted for inclusion in Engineering and Technology Management Faculty Publications and Presentations by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [email protected]. 2018 Proceedings of PICMET '18: Technology Management for Interconnected World Unified Business Intelligence Ecosystem: A Project Management Approach to Address Business Intelligence Challenges

Bhavana Ramesh, Akash Ramakrishna ETM Department, Portland State University, Portland, USA

Abstract--The purpose of this paper is to explore a concept between having a Business Intelligence strategic plan and the that improves the success rate of Business Intelligence projects ability to create a -informed decision making culture [2]. by looking at it as an ecosystem rather than standalone project. Most of the companies have realized the importance of This paper discusses the importance on Business Intelligence (BI) grounding their decisions on data driven facts yet companies initiatives and today’s challenges in implementing them. The struggle to realize the potential of to its full capacity. concentration is on using proven practices of project It is surprising to see that more than 70% of the and management to reduce the failure rates of the Business intelligence projects fail [3]. Data driven firms continue to face Intelligence projects by evaluating currently existing Information increased difficulties implementing data driven strategies. This Technology (IT) project process, Business Intelligence research paper examines the over-all nature of the Business frameworks and a Project Vulnerability process in order to adopt some of the best project management practices that works Intelligence projects and its implementation challenges and for Business Intelligence projects. The paper studies currently with the help of project management practices and governance existing processes and introduces a new concept which discipline. This paper tries to bridge the gap in analytics henceforth will be termed as ‘Unified Business Intelligence strategy formulation and the slippages that occur in execution Ecosystem’ framework (UBIE). This research is based on through the use of suggested framework to build more various project management frameworks, those are proven to comprehensive and integrated plan for business. It is increase the efficiency of projects, and far-ranging sources of anticipated that the use of this framework can build a more research on nature of Business Intelligence projects, personal comprehensive and integrated plan for Business Intelligence observations and real-world Business Intelligence consulting initiatives, and will yield better success rates in terms of an experience. ’s analytical success. Suggested approach will at a Research limitations/implications: Business Intelligence is a minimum, set a strong baseline to the project managers constantly developing field. Partial standardization and extensive managing the Business Intelligence projects. This paper will availability and access to Business Intelligence resources has also evaluate the benefits and the challenges that created many definitions and buzz words in the industry. The face in implementing analytical solutions. assumptions described in this research might seem arguable given the reader’s experience in this field. A. Business Intelligence definition Originality/value: The paper presents a different perspective The difference between Business Intelligence (BI) and to view and manage Business Intelligence projects. It encourages (BA) is very fluid in the current industry. to interpret Business Intelligence projects as an ecosystem by The diversity of opinion reflects the fluidity of how we combining technical, business and management aspect rather understand the defining language of the field. It also than separate, standalone entity. The aim of this research paper demonstrates that in business Intelligence term can mean is to achieve Business Intelligence success by customizing Project Management (PM) practices to fit the unique need of Business different thing to different people depending on their business Intelligence projects. focus and perspective [4]. However, in general business analytics is pursued as a function of business Intelligence Acronyms: making it a wider umbrella for hosting analytics, systems and Business Intelligence – BI infrastructure. In this paper, business intelligence and business Project Management – PM analytics are used interchangeably as both of these fields offer Data Science- DS similar implementation and adoption challenges to the Information technology - IT organizations. Unified Business Intelligence Ecosystem - UBIE B. Business Intelligence trends Responsibility, Accountability, Consulted, Informed - RACI Business Intelligence can be defined in different ways. The I. INTRODUCTION Data-Warehousing Institute has defined business intelligence as “the tools, technologies and processes required to turn data A Business Intelligence Strategy is a roadmap that enables into information and information into knowledge and plans that to their performance and seek out optimize business actions” [5]. The use of business intelligence competitive advantages and truly "listen to their customers" is becoming universal within organizations at every level. using and [1]. Recent Business According to a study by Gartner, the world's leading research Intelligence survey showed significant positive correlation and advisory company, the technology category of “analytics

978-1-890843-37-3 ©2018 PICMET and Business Intelligence” is the top priority of chief TABLE 1. SUMMARY OF ORGANIZATIONAL AND ANALYTICS CHALLENGES, ATRE & PYRAMID information officers and comprises a $12.2B [6]. It is SL Organizational Challenges by Analytics Challenges by Pyramid seen as a higher priority than such categories as mobile Atre Group [16] Analytics [15] technology, cloud computing, and technology Failure to recognize BI projects [7]. In 2011 Business intelligence and analytics exceeded as cross-organizational business Lack of adoption of BI initiatives 1 initiatives, and to understand due to the lack of right skills, tools other competitiveness plans as mobility solutions (ranked 2nd that as such they differ from and application at74%), cloud computing (ranked 4th at 60%), and social typical standalone solutions. networking (ranked 8th at 55%) [8]. IDC, a premier global Unengaged business sponsors Hard to standardize on a BI provider of and advisory services reported 2 (or sponsors who enjoy little or platform no authority in the enterprise). that the business analytics software market would grew by Unavailable or unwilling BI isn’t scalable - If there’s no 13.8% during 2011 to $32B and predicted it to be at $50.7B in 3 business representatives. common ground or revenue by 2016 [9,10]. Across a set of ROI cases, Nucleus standardization. Research, a research company found a $10.66 payoff for every Lack of skilled and available Hard to sell the return on 4 staff, or sub-optimal staff investment (ROI) of BI initiatives, $1.00 spent on analytics applications – suggesting that such utilization. and difficult to justify costs applications can be a very attractive investment route for chief No software release concept (no Inability to plan for or carry out 5 financial officers [11]. Although business analytics is hardly iterative development method). effective all and everything for managers dealing with modern No work breakdown structure Not having clear direction or complexities, it is a big deal for them – becoming increasingly 6 (no methodology). agreement on what or how to measure adopted in practice and emerging as an urgent challenge when No business or Don’t have adequate it comes to improving business processes and outcomes 7 standardization activities. understanding of current state of [12,13]. the BI environment No appreciation of the impact of Lack of governance, lack of data C. Need for Business Intelligence in the organizations 8 dirty data on business integrity. profitability. According to Gartner, business intelligence applications No understanding of the Lack of functionality in the have become among the highest-priority technology 9 necessity for and the use of software investments for most CIO’s as organizations see it as more meta-data. than just measuring and understanding the past performance of Too much reliance on disparate Difficult to achieve a balance 10 methods and tools (the dreaded between what some perceive as an isolated business activity. The role of business intelligence silver bullet syndrome). disparate sides is becoming more pervasive and is affecting the way information is used, analyzed and applied. As a result, II. LITERATURE REVIEW organizations can lead, decide, measure, manage and optimize performance to achieve greater efficiency and financial Business Intelligence projects have developed a reputation benefits. business intelligence is now becoming a key to better as being difficult, risky and expensive [17]. The term Business management of performance associated with the multiple intelligence (BI) refers to technologies, applications and dimensions of an organization and its business processes [14]. practices for the collection, integration, analysis, and Some of the other advantages of having a business intelligence presentation of business information [18] which makes initiative are that it contributes to faster decision making, Business intelligence project a blend on IT, Business and provides real-time performance measurement, brings greater Analytics projects. According to study done by Atre group, a insight into customer behavior and helps to identify new big and Business intelligence services business opportunities. company, there are many reasons for high failure rate, the biggest is that companies treat Business intelligence projects D. Reason for Business Intelligence failure as another IT project however Business intelligence is neither Given the positive value that analytics can bring to a product nor a system, It is, rather, a constantly evolving organizations, there are also quite a few challenges as well. strategy, vision and architecture that continuously seeks to These challenges can be categorized into ‘business align an organization’s operations and direction with its intelligence challenge’ and/or ‘business challenge’. Pyramid strategic business goals [16]. No one size fits all when it analytics [15], a leading business analytics company and Atre, comes to Business intelligence project management a big data management and business intelligence service methodologies. company [16] has identified the challenges as listed in the following table. A. Shortcomings of existing PM Methodology on Business intelligence projects. Lot of projects fails because of the low project maturity level [1] so research shows that a well-defined and tailored PM methodology is needed for the enterprise success. [26]. When purchasing or building custom software products, companies basically have two choices: Waterfall Model and Agile Methodologies. The Waterfall model follows a linear path and sequential process and Agile methodologies advices an incremental and iterative approach. Agile methodologies 1) IT project lifecycle management from Stewart were born after the Waterfall model and in response to the Many organizations seem to approach the whole shortcomings of the Waterfall model. management of IT in an unstructured or ad hoc manner throughout its life cycle [19,20]. The effective management of Waterfall model relies on initial requirements [27]. If the IT needs to be viewed as a structured iterative business requirement is not understood well, the change requests at the process, which offers organizational learning from each phase later phase costs a lot in terms of time, cost and resources. of the IT project life cycle [20]. According to Rodney A. Testing is not considered until the end of development [27] in Stewart [21], such an IT project life cycle framework should waterfall model increasing the project risk. The above points be comprised of three essential phases or modules: IT project make it harder to change directions of the project mid-way. selection, strategic IT implementation and monitoring and IT Waterfall model fails to be a good fit in Business intelligence performance evaluation, as seen in figure 1 below. projects as business intelligence projects takes shape as the project progresses and it’s hard to sign off on the IT project selection (SELECTIT) identifies benefits, risks requirements. Also, business intelligence project evolves as and cost involved in the project. IT implementation business goals and strategies evolve in the company making (IMPLEMENTIT) and monitoring emphasis on technology waterfall model a wrong fit to manage business intelligence diffusion by understanding markets, managing operations and projects. Hence there is a need to have a workable framework monitoring action plans. And, IT evaluation (EVALUATEIT) to implement and execute Business Intelligence projects. identifies and measures performance by using tools like balance scorecards and project health reports. Agile methodology has many advantages like contract transparency, increased flexibility, prioritizing business values, better collaborative effort, foster communication and a great installment-based costing however the disadvantage are include having unrealistic and unviable iteration demands, frequent testing and numerous deliveries, ambiguous costing, hard to predict timeline, it demands participations from everyone at all time including clients taking up significate time from business. All of the above disadvantages asks for a practical and feasible framework to manage Business Intelligence projects. B. Alternative approach This paper aims at conceptualizing a framework that best fits Business Intelligence project needs and tackles the organizational and analytical challenges that is existing today listed in table 1. The core of Business Intelligence projects is Fig. 1. IT project life cycle management process, Rodney A. Stewart, the analytical engine designed to serve business needs. Such September 2006. project calls for the disciplines of waterfall model and requires SelectIT - There have been numerous examples where IT the flexibility, team collaboration and user centered approach projects have failed to meet expectations [30,31]. This is that agile advocates. As Business Intelligence projects are sometimes due to a lack of prior assessment of risks and risky and expensive, [17] project vulnerabilities needs to be returns before management commitment is made and funding identified and resolved timely increasing project success. approval is provided [32]. Executives tend to lack the Integrating PM principles centering on analytics methods, skills and tools required for selecting a portfolio of architecture and balancing project vulnerabilities focusing in IT projects and tools, which add the greatest value to their systems thinking-based view to identify and respond to organization [21]. A well-structured IT project selection phase potential weakness and risks increases the success of the helps ensure that an organization selects those IT projects that business intelligence project. And hence this paper presents a will best support organizational needs and identifies and framework that integrates the three main aspects of business analyses an IT project’s risks and proposed benefits before a intelligence projects: IT Project Management, Analytics significant amount of funds and resources are allocated [21] Framework and Project Vulnerability which in turn increases Business Intelligence project is hard to select due to lack of the success rate of business intelligence projects. Business Intelligence skills as noted in table 1. Hence SelectIT concept can be used as a guide to selecting BUSINESS This new Business Intelligence ecosystem framework INTELLIGENCE projects. leverages on the following concepts to benefit from: Implement IT- Within most sectors of government and 1. IT project lifecycle management from Steward private industry, there are suggestions that IT investments are 2. Advance analytics framework from Ranjith Bose often accompanied by poor vision and implementation 3. Project vulnerability management from Ludovi approaches, insufficient planning and coordination and are Alexandre Vidal and Franck Marle rarely linked to business strategies [28–29]. Implementing BUSINESS INTELLIGENCE project have similar challenges vulnerability was devised by Ludovic-Alexandre Vidal and which can be addressed through Stewarts' implement IT Franck Marle [23], and as seen in the figure below illustrates process that involves SWOT analysis, diffusion strategy, the project vulnerability management process. The four steps operational strategy and monitoring plan. involved in vulnerability management are: EvaluateIT – most of the times evaluation is difficult to • Identification quantify. Business Intelligence projects need to be evaluated • Analysis upon completion to measure the actual benefits, learning path • Creating response plan and and ROI. Stweard suggests creating perspective-based • Monitoring and controlling indicators. 2) Framework for Business Intelligence using advance analytics by Bose In identifying advanced analytics opportunities and challenges, Ranjith Bose [22] points to the characteristics of Business Intelligence both on an operational and analytical viewpoint. His framework brings out the data and processing capabilities needed for maximizing Business Intelligence decisions. The framework integrates and advance analytics for intelligence delivery. His research shows a managerial approach to deploy advance analytics by packaged analytic applications (industry verticals); analytic application development platforms; and, customized application development.

Fig. 3. Project Vulnerability Management Process, Ludovic‐Alexandre Vidal, Franck Marle, 2012 Vulnerability, risk and change management are a necessary evil in Business Intelligence projects. Poor adoption, hyped Business Intelligence tools, resistance to accountability, change, and poor can increase project risks[24]. Identifying risks and managing vulnerabilities become extremely challenging. Project vulnerability guidelines created by Ludovic-Alexandre Vidal and Franck Marle can help Business Intelligence projects to identify and manage risk to a larger extent. Unified Business Intelligence ecosystem, a framework for Fig. 2. Framework for BI using advance analytics, Source: from [22] Business Intelligence projects that is conceptualized in this Ranjith’s Framework for Business Intelligence using paper uses the concepts of from the above 3 project advance analytics identifies key data elements and analytics management processes at the different phases of Business technologies from an integration and delivery point of view. Intelligence project lifecycle. This framework sets a structure for analytics and data science teams to implement Business Intelligence solutions. III. UNIFIED BUSINESS INTELLIGENCE ECOSYSTEM-AN For an organization to really use data as a strategic asset, they ALTERNATIVE APPROACH TO SOLVE BI CHALLENGES need to put a long-term program in place by tying vision, The intent of this research paper is to build an alternative strategy, goals, systems and continued learning. With this, we framework for driving Business Intelligence projects based the can ascertain the importance of business leaders in Business concepts adopted from the 3 processes described above. Intelligence projects. Without strong business drivers their alignment with the 3) Project Vulnerability Management Process by Ludovic- strategic business goals, the Business Intelligence decision- Alexandre Vidal and Franck Marle support initiative may falter [25]. To fill this gap, this paper The third process that plays a key role in Business provides an alternative approach to manage Business Intelligence project success is project vulnerability Intelligence projects. This approach is beneficial as it management. Business Intelligence projects are high risk addresses all the challenges listed in table 1. projects due to changing scope, and the little amount of This framework considers Business Intelligence project not control on the outcome that makes projects non-resilient to as a standalone IT project but as an ecosystem of complex uncertain events. Also, the static and the dynamic vision for network and interconnected teams. Business Intelligence projects can build in system vulnerabilities. A methodology for managing project Unified Business Intelligence Ecosystem cube in figure 4 A. Initiation depicts the Business Intelligence world in any organizations. The Unified business intelligence ecosystem is built on the The four indispensable teams on Business Intelligence standard 5 stages of project management. projects is represented by the sides of the cube. The first phase is project initiation. In this phase, the project idea undergoes the feasibility study. In this stage, Business leaders are responsible for selecting the BI project that identifies ROI and business benefit. Project selection can be done with series of brainstorming sessions, market needs analysis, organizations strategy roadmap and ROI realization. As this is a critical phase in which the project undergoes go/nogo decision, representation from all the cross-functional team is highly recommended.

Fig. 4. Elements of team in Unified BI Ecosystem

Key elements of Business Intelligence ecosystem- A. The Business Unit comprises of decision makers, leaders, sponsors and the senior management. ROI and benefit realization from Business Intelligence projects is the main focus of business units. This unit is responsible for mapping the company’s vision and Fig. 5. Project initiation in Unified Business Intelligence ecosystem mission to the project goals through a thorough market research. The Business unit builds Business Steward’s SelectIT module works best in selecting the Intelligence strategy and roadmap most beneficial BI project as SelectIT concept not only B. The Data science team are essentially the analytics provides a way to qualify projects but also quantifies the personals involving developers, data modelers, benefits, risks and costs. This also makes business units drive statisticians, analysts, visualization experts and the BI project instead of IT driving it. With a detailed managers. The primary responsibility of this feasibility study completed in the initiation phase, the business team is to collect, clean and integrate data, generate units get a clear understanding of risks and benefits involved advance analytics and deliver an intelligent solution in undertaking the project. to improve business. Based on project goals, data is In case of no-go decision, PM documents the reasons for modelled to either predict (i.e., forecast) and/or no go for future use. Creating and visiting these documents on prescribe solution (i.e., recommend) to the business. a regular basis is an extremely important step towards Creating the visual narrative completes the cycle of continued organizational learning and Business Intelligence Business Intelligence project. maturity. C. The IT team comprise of application specialists, tools specialists, configuration managers, security B. Planning and server professionals, network administrators or The second phase in the UBIE is the planning. Planning anyone who support the IT needs of Data science involves creating a detailed plan for BI execution and team. implementation. Planning for BI project can be tricky as the D. Project management is managed by the project requirements might change and the project might take a manager or the PMO based of organizations structure different direction with scoping. An acceptable amount of and PM/PMO is responsible for project success. flexibility should be considered in scheduling and scope. Project management in Business Intelligence projects has 5 Communication, risk and mitigations planning needs to be phases. done extensively and BI projects are perceived as high-risk projects. • Initiation • Planning Introducing project vulnerability framework in this phase • Executing helps identifying vulnerable process and elements in the • Monitor and Control system which in later phases becomes an unseen risk. Vulnerability emphasis on project weakness’, creates a • Business Intelligence Implementation response plan, analysis's it resistance and resilience's and prevents it from possible damage. Project vulnerability process begins in the planning phase and extends until implementation phase.

Fig. 7. Project Ecexution in Unified Business Intelligence ecosystem

This phase is the longest of all the phases. Due to the very nature of Business Intelligence projects there is a high possibilities of scope changes which puts project planning off Fig. 6. Project Planning in Unified Business Intelligence ecosystem the track planning. Providing flexibility to scope changes and Other general activities involve creating WBS, getting yet managing the project is challenging. Scope changes are estimated from data science and IT teams, planning the especially hard to deal when cross-functional teams are budgeting, scheduling, set up milestones, assess risks, impacted. communication and stakeholder management plans. It is PM can refer to Project vulnerability model explained by important to notice that as the number of cross-functional Ludovic-Alexandre Vidal and Franck Marle that helps PM to teams and stakeholder increase, the complexity of planning be sensitive to project events. As a part of monitoring and and managing project also increases. Setting right expectations controlling, creating timely audits, generating project metrics for teams and planning communications are critical at this dashboards help PM identify risks and communicate it to the phase. Business leaders are primary stakeholders in this phase stakeholders. and are informed about the project plans. Business units are engaged in each phase of project management and are As UBIE framework emphasis on holistic approach for expected to participate and made aware of every risk involved. Business Intelligence projects, aiming for Business Intelligence standardization and practicing it is the key for Some of the unique and Business Intelligence specific risks long-term Business Intelligence sustainability and this phase that can be planned and managed in planning phase are sets a foundation to do so. Data scrutiny and standardization availability of skilled team members, The business and methods should be evaluated and documented by data science technical skills required to implement a Business Intelligence teams to set up practices that works for the organization which project are quite different than other IT projects as Business in later projects can be used as the standards. This in turn Intelligence project integrates, analyzes and delivers contributes to Business Intelligence maturity. information derived from almost every area of the business as a whole and with this the required technical expertise varies as D. Implementation well. Having mandatory skills and IT/BI tools identified The insights developed by DS team is implemented prepares the organization to execute the project without delays through business rules in the implementation phase. Business and over expenditure. units evaluate the project goals with that of the insights to C. Execution, Monitor & Control make business sense. Execution is the core of the project lifecycle where the project is actually built. Based on the project goal data science team gathers data, build models and deriving intelligent solutions. Framework for Business Intelligence using advanced analytics by Ranjit Bose can set a base for architect Business Intelligence projects. Data comes in many forms, structure, unstructured, in form of pictures, texts, voice and videos. From the DS point of view, Taking data and converting it into meaningful insight to drive business is the key goal of execution. Various analytical processing is designed to derive meaningful insights.

Fig. 8. Project Implementation in Unified Business Intelligence ecosystem

Fig. 9. Transactions between cross functional teams in the BI ecosystem Introducing Stewards implementtIT module in this phase helps the project underlines on project management best practices to ensure Business Intelligence better implement the insights. By understanding the SWOT factors and aligning success. insights derived in the execution phase, the insight diffusion can be planned and monitored. IV. RACI MATRIX FOR BUSINESS INTELLIGENCE ECOSYSTEM E. Business Intelligence Maturity More often than not, projects like Business Intelligence that involves cross functional teams are in formless state of confusion where people do not The Business Intelligence ecosystem is constantly growing in maturity with recognize a boss to whom they feel responsible. This can possibly mislead teams constant feedback between teams. The results of Business Intelligence solution regarding the authority and power they hold toward Business Intelligence implementation act as input for newer projects. Lessons learnt, and any kind of project. In such conditions decision making becomes extremely hard and so explicit knowledge should be maintained and shared. And thus, the organization defining RACI matrix for Business Intelligence ecosystem is extremely can learn, implement, optimize and innovate in the Business Intelligence space. important. Table 2 provides the guideline to design RACI matrix for Business The model or flowchart in Figure 10 below shows transactions between cross- Intelligence project. functional teams in the Business Intelligence ecosystem. This framework

TABLE 2. CROSS-FUNCTIONAL TEAM AND RACI FOR BI ECOSYSTEM PM Phases BU DS IT PM Project Initiate Accountable for scope definition, Data science team brings skills and Consult IT to provide responses on IT systems Document and manage communication between teams. funding, value realization and experience in the data field and is consulted to support BI project. Eg- Cost of licensing, Gather and brainstorm ideas. PM binds all the teams to providing high level business goals. to validate the feasibility of achieving Security etc. have same understanding by being a translator between Input for requirement comes for business goals. DS team should provide a (C), (I) technical and business teams. PM is responsible for vision, mission statements, market realistic check to project from statistical or initiating the project. (R) needs analysis etc. (A) BI point of view. (C), (I) Plan BU helps in planning defining DS teams should to provide realistic Identify the IT needs and plan for IT licenses, PM should consult DS, IT and BU to create financial project timelines and scope and estimated to PM. In case of uncertainty infrastructure capabilities, system integration spend plans, Risk plan and the schedules and WBS. answers all the questions that regarding the estimated and skills, seek PM's and support (R) participating teams have. to get help from SME or consultant. (C), (I) (C), (I), (A) (C), (I) Execute BU is consulted and informed Responsible for project execution, modelling Responsible for IT integration Accountable for execution, PM should manage time, constantly in this phase. Following and delivering BI project (C) cost and scope, Provide project status metrics for up with DS teams and verifying the (R) Management review, Communicate with BU and DS in scope is actually translated into case of scope changes, Manage project risk, issues and projects transitions (A) (C), (I) Monitor and Plays an evaluator role, gives Provide project status, cooperate auditing Conduct IT auditing and help PM monitor IT Conducts all PM auditing activities. Manages PM control feedback to PM based of the metrics (C), (I) activities metrics. (R) provided by PM. (I) (C), (I) BI Responsible for project signoff, DS is informed about the activities in this Plan IT support, Manage licenses. IT Documentation of lessons learnt, closure reporting and Implementation validate if the scope is delivered as phase. DS support transition and/or trainings responsible for DB governance and celebrations planned. if needed. standardization (R) (A) (C), (I) (C), (I) V. UNIFIED BUSINESS INTELLIGENCE ECOSYSTEM (UBIE) analytical challenges that companies face in implementing FRAMEWORK ADDRESSING BUSINESS INTELLIGENCE intelligence. Dealing with BI project seems to be uniquely CHALLENGES challenging when organizations follows either sequential or iterative project management models. Research shows various challenges involved in implementing Business Intelligence projects. Challenges can This paper aims at conceptualizing a framework that best be classified into organizational challenges and analytical fits BI project needs and tackles the organizational and challenges. Organizational challenges are the once caused due analytical challenges. BI projects are highly risky and to the structure, processes, administration, organizational vulnerable with low maturity index. This paper integrates IT culture and management. Analytical challenges are more Project Management, Analytics Framework and Project technical, skills and talent associated. Adapting to unified Vulnerability frameworks creating a unified ecosystem and Business Intelligence ecosystem enables the organizations to increases the success rate of BI projects. reap benefits and the benefits can be summarized in following A holistic approach to manage Business Intelligence points. project is a key to overcome challenges rather than seeing A. This framework is designed to build a tight coupling Business Intelligence project implementation as an IT project. mechanism between cross-functional teams and is This paper identifies three best frameworks that’s exists today driven by open communication and engaging and and amalgamates those processes into unified framework that all the teams at every phase of the project. can serve BI projects and increase the success rate. The RACI matrix provides clear distinction regarding Business Intelligence projects are fast paced with the responsibility and accountability amongst cross- changing scope and involving various functional teams functional teams and resolves confusions concerning making Business Intelligence project vulnerable to failures. power and authority. Availability and affordability of Business Intelligence B. Unified Business Intelligence framework involves knowledge and tools are ever growing making it harder to business leaders and sponsor at all phases which manage talent, experience and opinions of people involved in imposes the sponsor to engage in the project life cycle. Business Intelligence functions. Major business decisions can happen in a faster when leaders are constantly involved in the projects. The exponential growth and parallel knowledge creation is making it harder to standardize. Given this situation, C. Lack of skills, talent, tools- Having cross functional organizations should create and follow internal Business teams involved at planning stages helps teams to Intelligence standardization that works best for itself. identify and plan project resources. Such teams can also work on resource sharing approaches. As there are The Unified Business Intelligence Ecosystem (UBIE) too many Business Intelligence tools available in framework in figure 9 is created to help organizations to market, identification and prototyping of a right tool overcome challenges and improve project success by creating that suits organizational Business Intelligence need is timely transitions and coordination between cross-functional time consuming. Providing heads up to the IT to take teams. It also shows learning and maturity path for Business care of prototyping, licensing, security and server needs Intelligent organization. Detailed project implementation steps at the initiation phase can save a lot of time in the is described through phases that can be used as a guideline by planning and execution phases. business intelligence organization to implement their BI projects. D. Business Intelligence projects performance are hard to measure. Post implementation of intelligence, it takes a RACI matrices described in table 2 helps Business time to see results. Lack of standardization in Business Intelligence organization define roles and responsibilities for Intelligence field make it harder to choose the KPI’s to Business Intelligence projects. measure the performance. However, an organization This framework is recommended in a Business Intelligence can build its own Business Intelligence standards environment as it is easy to implement and can fit into most of through constantly learning and optimizing the the Business Intelligence environment and on a longer run, solutions. Unified Business Intelligence ecosystem leads organizations towards Business Intelligence maturity. framework completes the cycle of the Business Viewing Business Intelligence project as a complex Intelligence project by considering the project results as interconnected network of activities performed by various an input for next Business Intelligence initiatives. By teams will help build a larger perspective than considering it doing so, the tacit knowledge and the explicit as an IT project. knowledge has lesser chance of getting lost in the project path. REFERENCES

VI. CONCLUSION [1] Available FTP: http://support.sas.com/resources/papers/proceedings12/021-2012.pdf This paper studies the current state of art of Business [2] Available FTP: https://www.advisory.com/-/media/Advisory- Intelligence projects. It lists various organizational and com/Research/ITSC/Research-Notes/2014/Anatomy-of-a-Business- Intelligence-Strategy-Document.pdf [3] 3Why do 70% of Business Intelligence Projects Fail? [21] A framework for the life cycle management of information technology [4] Available FTP: http://olap.com/why-do-70-of-business-intelligence- projects: ProjectIT Rodney A. Stewart * School of Engineering, Griffith projects-fail/. University, Gold Coast Campus, PMB 50 Gold Coast Mail Centre, [5] Justin Heinze , July 1, 2016 , Business Intelligence vs. Business Queensland 9726, Australia Received 26 September 2006; received in Analytics: What’s The Difference?[online], Available FTP: revised form 21 May 2007; accepted 22 May 2007 https://www.betterbuys.com/bi/business-intelligence-vs-business- [22] Advanced analytics: opportunities and challenges Ranjit Bose Anderson analytics/ School of Management, University of New Mexico, Albuquerque, New [6] Available FTP: The Institute, Q4/2002 Mexico, USA [7] R. Kalakota, Gartner says – BI and Analytics a $12.2 Bln market, Posted [23] Ludovic‐Alexandre Vidal, Franck Marle, (2012) "A systems thinking April 24, 2011, Available FTP: approach for project vulnerability management", Kybernetes, Vol. 41 http://practicalanalytics.wordpress.com/2011/04/24/gartner-says-bi-and- Issue: 1/2, pp.206-228. analyticsa-10-5-bln-market/ 2011 [24] Available FTP: [8] Decision Support Systems, A unified foundation for business analytics, https://www.cio.com.au/article/389451/top_five_rules_cios_minimise_ri Clyde Holsapple a, ⁎, Anita Lee-Post b , Ram Pakath c, Received in sk_bi_projects/ revised form 24 May 2014 [25] Jayanthi Ranjan, (2008) "Business justification with Business [9] R. Kalakota, IBM CIO study: BI and analytics are #1 priority for 2012, Intelligence", VINE, Vol. 38 Issue: 4, pp.461-475, Posted November 2, 2011. Available FTP: https://doi.org/10.1108/03055720810917714. http://practicalanalytics.wordpress.com/2011/11/02/ibm-cio-study-bi- [26] Method for Adaptation and Implementation of Agile Project andanalytics-are-1-priority-for-2012/ 2011 Management Methodology Arturs Rasnacisa , Solvita Berzisaa, ICTE [10] D. Harris, IDC: Analytics a $51B business by 2016 thanks to big data, 2016, December 2016, Riga, Latvia Accessed Jul 11, 2012 Available FTP: http://gigaom.com/cloud/idc- [27] Available FTP: https://ac.els-cdn.com/S187705091730056X/1-s2.0- analytics-a-51b-business-by-2016-thanks-tobig-data/ 2012. S187705091730056X-main.pdf?_tid=bf56056a-5e43-4eea-b679- [11] S. Swoyer, Demand for analytics keeps growing, The Data Warehouse 57c42799ead4&acdnat=1522344365_2c5b92b0825efc45bd480e04d3de Institute, 2012. 5a11 [12] Nucleus Research, Analytics pays back $10.66 for every dollar spent, [28] Available FTP: http://www.capaxglobal.com/blog/introduction-to-agile- Report L122, December 2011. bi-agile-vs.-waterfall [13] J. LaCugna, Foreword, in: J. Liebowitz (Ed.), Big Data and Business [29] Guida G, Tasso C. Design and development of knowledge-based Analytics, CRC Press, Boca Raton, FL, 2013, pp. vii–xiii. systems: from life cycle to development methodology. Chichester, UK: [14] J. Liebowitz (Ed.), Big Data and Business Analytics, CRC Press, Boca John Wiley and Sons; 1994 Raton, FL, 2013. [30] Raz O. Goldberg A. How to make IT work: cognitive perspectives for [15] Available FTP better information technologies performance. Eur Manage J 2006;24(2– www.umsl.edu/~sauterv/DSS4BI/links/pdf/BI/gartners_business_intellig 3):199–205. en_142827.pdf [31] Whittaker B. What went wrong? Unsuccessful information technology [16] Available FTP: projects. Informat Manag Comput Security 1999;7(1):23–9. http://www.pyramidanalytics.com/pages/blog/2017/02/meeting-the- [32] Kutsch E, Hall M. Intervening conditions on the management of project business-analytics-challenge-in-2017.aspx risk: dealing with uncertainty in information technology projects. Int J [17] Available FTP: http://www.atre.com/pdf/BI_top_101.pdf Project Manage 2005;23:591–9. [18] http://us.analytics8.com/images/uploads/general/2012_A8_How-BI- [33] Jung Y, Gibson GE. Planning for computer integrated construction. J Projects-Are-Different-r6.pdf Comput Civil Eng 1999;13(4):217–25. [19] Irani Z, Love PED. The propagation of technology management [34] Peansupap V, Walker DHT. Innovation diffusion at the implementation taxonomies for evaluating investments in information systems. J stage of a construction project: a case study of information Manage Informat Syst 2001;17(3):161–77. communication technology. Construct Manage Econ 2006;24:321–32. [20] Stewart, RA. Life cycle management of information technology projects [35] Raz O. Goldberg A. How to make IT work: cognitive perspectives for in construction. PhD Dissertation: Griffith University; 2003. better information technologies performance. Eur Manage J 2006;24(2– 3):199–205.