The P2V Framework: How Data Warehouse Performance Affects Business Value Richard Hackathorn, Bolder Technology Inc

A BTI Study

The P2V Framework: How Data Warehouse Performance Affects Business Value Richard Hackathorn, Bolder Technology Inc.

This study explores a fundamental issue in Information Technology. How does the data warehouse enable a corporation or government agency to realize tangible business value from increasing revenues or decreasing costs? For example, if the data warehouse executes a frequently used query many times faster, how does the company realize greater business value from this increased performance? The study explores how specific performance of the data warehouse enables business value, which is called the Performance-to-Value (P2V) framework. Through real business scenarios across several industries to illustrate this relationship, the P2V framework explains the dynamics based on the S-Curve. Guidelines are suggested to best leverage the performance of data warehouses. Today’s levels of performance are positioning data warehouses as catalysts for innovations in both effectiveness and efficiency.

Why Invest in Data Warehouses? ...... 2 Performance-to-Value Relationship ...... 2 Norfolk Southern – Where Is My Rail Car? ...... 4 Cabela’s – How Can We Improve Our Marketing? ...... 6 Southern California Edison – How Much Electricity Am I Using? ...... 8 AT&T Retail – What is Happening in My Store? ...... 9 Station Casinos – How Can We Treat You Better? ...... 12 Time-to-Value Curve ...... 13 Four P2V Zones ...... 15 Leveraging the P2V Relationship ...... 17 Summary ...... 17 Endnotes ...... 19 About the Methodology ...... 20 About Bolder Technology ...... 20 About Our Sponsors ...... 20 Why Invest in Data Warehouses? Any corporation or government agency faces difficult decisions about where to invest their limited resources. Information technology (IT) in general and data warehouses (DW) in particular are two investment alternatives amid many. So, why invest in data warehouses? The concept and practice of data warehousing has matured greatly over the past decade. The majority of corporations or government agencies today have developed and operated a data warehouse of some kind for many years. For most companies, data warehousing has become an essential “mission-critical” component of their IT infrastructure. The focus of this study is not whether to develop a new data warehouse but whether to invest in increasing the performance (or capability) of the data warehouse to handle new workloads that can support new business functions. This type of incremental DW investment can span options, such as making simple memory upgrades, adding more MPP processing nodes, converting DW platforms from one vendor to another, or rearchitecting the DW infrastructure. The practical application of this study is quite simple. Imagine an executive who is faced with a decision to upgrade the DW architecture. Take for instance, moving to massively parallel processing (MPP) to dramatically increase query processing speed. Is this upgrade worth the cost? This is a simple question, but one that requires an … requires an understanding of understanding of how better DW performance will result in higher how better DW performance will business value. Such discussions unfortunately center on result in higher business value technological issues rather than business issues since the former is easier to quantify. So, let’s explore a framework that can shift emphasis to the latter …to business value. Performance-to-Value Relationship This study deals with two important factors—Data Warehouse Performance and Business Value—along with the relationship of how the first affects the second, as shown in Figure 1. This relationship is called Performance-to-Value or simply P2V.

Data Warehouse Business Performance P Value V

Figure 1 – The P2V Relationship This figure implies that the first factor—Data Warehouse Performance or simply ‘P’—enables (or influences, or enhances) the second factor—Business Value or simply ‘V’. This implies that P is a ‘necessary’ factor1 to enable V within a specific business scenario. In the business scenarios later, we will indicate how P improves V and whether this impact is small or large. In scenarios where careful measurements are recorded, we can make valid statements like: a 25% increase in P will cause a 50% increase in V.

© Bolder Technology, Inc. 2012 2 The first factor, P, includes the potential ways of improving the capability or capacity of the data warehouse. An analogy for performance of a data warehouse is a unit of electrical power, like a watt of electricity. An ampere of current flows through an electric motor plugged into a 110-volt outlet consumes 110 watts of electricity and produces mechanical power, which can be used for a variety of tasks. Like electrical power, greater performance from a data warehouse produces faster queries, supports more concurrent users or both, depending on how the workload is configured. In particular, below are two lists of possible options for DW performance. The left list is system- oriented performance factors, while the right list is user-oriented performance factors that are visible to DW users. System-Oriented Performance User-Oriented Performance Faster CPU cores with larger memories Faster query response times More MPP processing nodes Greater number of concurrent users More efficient parallelism Deeper analytic algorithms More efficient query optimizer Cross-function data integration More advanced indexes/compression Linking Big Data with enterprise data Solid state storage and fast disk arrays Automating decision rules & execution In-database parallelized scripts Integrated KPI framework Easy data delivery to mobile devices The second factor, V, is the business value resulting from a DW performance factor within a business scenario. Below are two lists of possible business values. On the left, the benefits are potential business values that improve business processes but lack a direct connection to financial outcomes. On the right, the benefits are realized business values since they are directly connected to the corporate financial statement. Potential Value Realized Value Quick customer response Increased customer revenue Deeper customer insights Better cost savings/avoidance Flexible business processes Greater customer loyalty Just-in-time fresh data Reduced customer churn Accurate analytic results Higher sales margins Informed executives via dashboards Shorter sales cycle Less idle inventory A useful perspective is the Value Wedge2 shown in Figure 2 as a financial chart of Revenue and Cost with increasing Margins over time. These increasing Margins are the initiatives that management is continually driving to increase revenue (effectiveness) and decrease costs (efficiency).

Value Margins Wedge

Costs

Reduce Costs Figure 2 – Value Wedge For example, a business scenario that increases DW responsiveness (P) could result in quicker customer service time (V). This would improve effectiveness by increasing customer loyalty and expected future customer revenue. Or, this would improve efficiency by reducing service personnel headcount and expected future personnel costs. Or, this would improve both. The sum of future customer revenue and personnel costs would be the financial impact of V in this scenario.

Let’s consider a series of real business scenarios to provide a concrete basis for the above P2V discussion. Let’s look at the following five companies in different industries to illustrate a broad range of DW situations. All are publically documented case studies of Teradata customers in recent years. See the endnotes for details.  Norfolk Southern – Where Is My Rail Car?  Cabela’s – How Can We Improve Our Marketing?  Southern California Edison – How Much Electricity Am I Using?  AT&T Retail – What Is Happening with My Store?  Station Casinos – Why Am I Being Treated So Nice? Norfolk Southern – Where Is My Rail Car? Norfolk Southern Railway Corporation is a 182-year old, $11 billion transportation company that moves 1,800 freight trains daily on 20,000 miles of railway.3 Its 11,000 customers in 8,000 firms depend upon Norfolk to transport rail cars containing their raw materials or finished goods throughout the U.S. To efficiently operate business processes, the company needs to know where those rail cars are and when they will arrive at customer facilities. Previously customers would call Norfolk’s service centers and request the status on their rail cars. Since the information was fragmented across many production systems, the service representatives would have to query these systems to determine the status. Often the requests were more complicated, such as all late arrivals for the past quarter or all damage reports for shipments over the past year. For complex requests, the service representatives had to create batch reports to be generated at a later time. This implied that customers would have to wait

© Bolder Technology, Inc. 2012 4 hours or days before receiving an answer. In addition, Norfolk kept getting more and more requests, which was becoming a burden to the production systems and to the service personnel. Over the past five years, Norfolk consolidated a variety of corporate information into their data warehouse, along with real-time status data from scanning barcodes on rail cars as they passed specific locations. A query wizard was developed to help customers through the steps of specifying a query for rail car information. Now customers access this query wizard via a secured self-service website to request their own information. The queries run in less than 30 seconds, giving customers answers within seconds as compared to hours or days. Figure 3 shows a customized report for the Norfolk Ashville Yard Tracks that is sorted on the time that the rail cars (filled with coal) pass a specific waypoint. This report runs in 20 seconds, contains links to additional detailed data, can be exported in multiple formats, and scheduled for delivery.

Figure 3 - Norfolk Customized Report The website provides 24x7 access/delivery and services over 4,500 individualized reports per day. With real-time car barcode scanning, the data latency improved to less than an hour, permitting the location of a rail car to be accurately determined. After the initial release, a new algorithm was added to the self-service capability that estimated the arrival time of the rail car at the customer facility. Thus, the information shifted from “where is my rail car” to “when will my rail car arrive here”. By taking into account many factors (rail traffic, rail repairs), this algorithm eliminated guesswork and became more relevant to the customer. Defining V and P What is the business value V attributed to this solution? And, what is the DW performance P required to enable this solution?

© Bolder Technology, Inc. 2012 5 The business value V is based upon external customer self-service and internal personnel savings, the combination of which improved effectiveness (revenue increase) and efficiency (cost reduction). First, the value to Norfolk from this customer self-service is high, since customers perceived that they are in control of accessing their information and view Norfolk as being responsive to their needs. By cutting the response times to less than a minute from hours or days, customers change their behavior to utilize this information more in their decisions. Instead of being a mystery that forces customers to react to the arrival of rail cars, they can plan their activities more efficiently to have the necessary resources available at the proper time. This benefit increases customer loyalty to Norfolk since business activities between the companies are more closely linked by the shared data. The value from improved scheduling, inventory management and monitoring of shipping costs dramatically increases with quick access to the cargo routing information. This helps customers shave costs out of the supply chain while increasing their dependency on Norfolk Southern versus rail companies that cannot deliver the data as fast. Second, the value to Norfolk in personnel savings results from a shift in use of the call center. No longer servicing a large volume of information requests, call center employees can now concentrate on high-priority issues for customers. Rather than continually answering routine questions, the call center employees derive greater satisfaction in helping customers with problems caused by storms disrupting rail lines, for instance. The DW performance P is the set of factors that enabled this self-service scenario. First, the general infrastructure for the data warehouse supports the consolidation of corporate data and the operation of a reliable production system. Second, the specific requirements of query responses within 30 seconds, external 24x7 access, delivery of 4,500 individualized reports per day, and data latency of less than an hour could be supported with existing DW resources. In general, Norfolk has established SLA requirements that query reports be delivered quickly (under 30 seconds) and reliably. Reports can be scheduled so that timely delivery is assured and be requested as fax, email, or FTP files in formats HTML, text, CSV, XLS, or XML so that subsequent analyses can be easily performed. Another important change is that the demand is now coming from external customer requests, which have higher security restrictions and less predictable loads, putting stress on workload management. In summary, this scenario is a good example of shifting resources from internal customer support to customer self-support. It is also a classic case of investing in general DW infrastructure from an enterprise perspective so that the company is better able to pursue a variety of business innovations. The bottom-line is that Norfolk can generate more V for the same P. Cabela’s – How Can We Improve Our Marketing? Cabela's is a leading specialty retailer and direct marketer of hunting, fishing, camping and related outdoor merchandise, founded in 1961.4 They operate

© Bolder Technology, Inc. 2012 6 large destination retail stores in many states and carry over 225,000 sports items. The business problem was that their previous data warehouse was actually a data mart supporting few subject areas that did not integrate across business operations and could not support their planned future growth. Over time, the data mart had reached its capacity limits and become costly to maintain. Further, the various data sources had no consistent approach to data governance so that basic issues regarding data integration were not being resolved. Without adequate data integration, business analysts were not efficiently utilized since their efforts were focused on building a separate analytical data mart, rather than performing analyses. Whenever analyses were produced, most of the effort was finding and preparing the data, which was often out-of-date. The solution was to implement an integrated data warehouse using a 4-node 32 TB 5500H Teradata platform and the Teradata logical data model for the retail industry. The data warehouse supports 350 users, of which 200 are typically … explore and experiment directly in concurrent. the warehouse without disrupting In addition, a 250 GB analytical sandbox using the SAS the other warehouse users development environment is supported within the Teradata system. Enabled by the analytical sandbox, analysts can explore and experiment directly in the warehouse without disrupting the other warehouse users. The analytical sandbox allowed analysts to work with non-production DW data from third-party sources. Defining V and P The business value V to Cabela’s derives from the integrated nature of the new data warehouse. Specific benefits noted are:  Elimination of 6 TB of redundant data located on various servers  Improvement of data freshness often by weeks especially as used in analytics  Better effectiveness in targeted promotional fliers  Tripling of analytical reporting levels The DW performance P is the various technical capabilities within the Teradata platform to support the single view of the business and the compatibility to Cabela’s business of the retail data model that guided data integration. A specific example of P is the use of in-database processing with the Teradata SAS Integrated Solution. As a major catalog direct marketer who mails 140 million catalogs to 120 countries, a critical issue is which catalog to send to which customer. Cabela’s once had five statisticians working on this issue. But with in-database processing, the turn-around times are much faster so that they were able to reduce the number to just two. This freed up three statisticians to contribute to other areas of the company. So, their analytics group that had been only marketing-focus became enterprise-focused. The efficiency of in-database processing relieve the pressure to hire “an army of statisticians” to solve these type of business problems. Another example is the ability to link a newspaper promotion or website banner ad to a specific store purchase. In other words, integrating data across multiple marketing channels is critical. A

© Bolder Technology, Inc. 2012 7 similar example is the use of web clickstream data to determine “the path that customers took while viewing the page, how that generated sales, what worked and what did not.” Analysts are free to refocus their attention on generating more business insights. In summary, this scenario illustrates the replacement of an older DW infrastructure with a newer one, thus improving the company’s ability to execute marketing programs and initiatives. The bottom-line is that Cabela’s is able to generate more V with new P capabilities. Southern California Edison – How Much Electricity Am I Using? Southern California Edison (SCE) is a large electric utility with over 14 million customers in 180 cities serving 9,000 megawatts daily.5 The traditional way of collecting utility data is for utility employees (meter readers) to manually read five million electricity meters once per month to generate customers’ monthly bills. In addition, customer service changes, billing verification, and service investigation require an additional 2.5 million physical visits to customers’ sites annually. This traditional way is inflexible because with data latencies of a month, SCE must plan for excess capacity to satisfy infrequent peak periods. What is needed is a way for consumers/businesses to conserve energy during these times, avoiding spikes in demand that require that peak capacity. However, it takes a long time and large investments to construct new electrical generation plants to handle peak loads. So SCE launched a project that employs a smart meter to monitor electrical demand in near real-time and encourage consumers to reduce their use of air conditioner and appliance loads at peak intervals (e.g., during afternoon and evening of hot summer days). In other words, SCE provides timely information for load shifting thus minimizing these peak demands. SCE upgraded their electrical distribution system with a plan to install five million smart meters over four-year period costing $1.6B. These meters can capture data hourly, resulting in 720 meter reads/month from a single customer or over 5 billion reads/month from all customers. In addition, service switches and verifications can be performed remotely. The data warehouse contains 143 staging tables to 120 core data tables, along with about 200 ETL processing jobs that move data from source to staging to core tables. Consumers can access a secure website to determine daily usage, project billing amounts, and obtain budget assistance. An innovative part of the solution is to put consumers directly in the loop with a smart phone app that displays budgeting notifications, as shown to the right. The consumer can request automatic notifications to reduce their energy consumption when it reaches unusually high levels, thereby saving money for the consumers and reducing demand for SCE. Figure 4 shows the web application that configures the notification process, allowing the consumer to set monthly spending goals.

Figure 4 - SCE Budget Notification and Reporting Defining V and P What is the business value V attributed to this solution? And, what is the DW performance P required to enable this solution? The business value V is still unfolding; however, the usage of smart meters is expected to reduce total electrical demand by 1,000 megawatts and lower residential demand by 1%, resulting in 365K tons less greenhouse emissions. The DW performance P required is the ability to load 28GB of meter data each morning so that it is available to consumers by 8:00am.6 The report on the right side of Figure 4 shows the usage as of the previous day. These reports can be requested by consumers from SCE’s website and are generated within seconds. The warehouse maintains 39 months of meter data plus other associated data (rate information, hourly temperature, data logs), totaling an estimated 42TB. In summary, SCE is adding intelligence to its electrical distribution system so that it can better interact with its customers to level out peak electricity demands and to avoid construction of more generating capacity. The bottom-line is that SCE can generate more V for the same P. AT&T Retail – What is Happening in My Store? AT&T is a global telecommunications company with $124 billion in revenue in 2008.7 AT&T Mobility (formerly Cingular Wireless and currently AT&T Wireless) has 80 million subscribers, who are serviced from 2,200 company-owned retail stores, in partnership with thousands of independent sales agents. The retail stores are split across regions and markets to focus on their unique competitive situations using a decentralized management approach. Key to this decentralized approach is providing detailed sales information to thousands of in-store employees, along with thousands in Marketing and Finance support roles. The business problem is to shift store managers from spending time gathering data to spending time selling to customers. It is the common challenge of delivering the right information to the right people at the right time. Traditional BI query tools are appropriate for business analysts but are not suitable for store employees or for high-level executives. It was not sufficient to

© Bolder Technology, Inc. 2012 9 deliver a standardized (one-size-fits-all) HTML report late in the business day or wait for an analyst to create a custom report. Further, once the information was delivered, the store manager was unable to slice-and-dice the data to understand its implications. AT&T already has ten data warehouses in production throughout the corporation. AT&T Mobility is supported by the eCDW data warehouse on a Teradata system, which has over 6,000 users. Each day, about 1,500 users are active, of whom half are BI analysts running two million queries per day, consuming ten million CPU second. The Teradata platform has 80 nodes maintaining over 60 TB of user data, with the largest table having 290 billion rows. Subject areas covered in the data warehouse span billing, point-of sales data, and call centers, all built with a 3NF design. The solution involved an initial six months of intense discussions involving all levels of users. The resulting design was quite different from the original conception and went well beyond a typical dashboard. The solution was tailored to 27 different markets that catered to the entire hierarchy from the individual store managers to regional presidents. Current information was delivered by 9 a.m. each morning with full analysis capability and sub-second response times. Further, the information was customizable to the individual store manager, allowing trending and comparisons over time periods and markets. The approach used the eCDW data warehouse to create the in-database Sunrise data mart that integrated the existing data hierarchies into business metrics used to measure store sales performance. As shown in Figure 5, the Sunrise data mart was created and maintained within the eCDW platform, thus avoiding complications of additional infrastructure. All aspects of retail store operations were integrated together, from wireless/wired devices, activations, customer traffic, customer surveys, and the like. Users could choose from a ‘cafeteria’ of 150 KPIs to create custom dashboards to be generated nightly based on their configuration choices. The developers used custom Java GUI widgets as containers to display data generated from database macros. This approach was said to be “fast and configurable”.

Figure 5 - Sunrise Data Mart for AT&T Retail Defining V and P What is the business value V attributed to this solution? And, what is the DW performance P required to enable this solution?

© Bolder Technology, Inc. 2012 10 The business value from this solution was significant in increased sales revenue. There was a “drastic reduction in time spent to get vital actionable information to effectively manage a store”, thus freeing managers to be managers, not analysts. It changed the social dynamics of managing a store by allowing managers to quickly respond to problems when they saw problems with their KPIs and enabled effective coaching of under-performing store managers. Since the workload from the Sales Dashboard is only 100 CPU seconds or a small fraction of a node’s performance capacity, AT&T could add 30,000 new users of the sales dashboard with their existing infrastructure. They refer to it as a “free application” from a runtime perspective, ignoring costs from dashboard development. The DW performance that enabled this value is derived from the design of the Sunrise data mart with the workload management of the Teradata system. By properly prioritizing the short- running queries that feed the Sales Dashboard, the long-running queries that support backoffice operations do not impact response times for the store managers. The SLA requirement is to deliver results to store managers in less than one second. Performance statistics show that the average elapsed times are in the range of 0.25 to 0.55 seconds, as shown in Figure 6.

Figure 6 - Performance Statistics for AT&T Sales Dashboard The Sales Dashboard is constantly changing, with new metrics always replacing old ones as business priorities shift in the competitive landscape. Good answers to business problems always generate more questions. Once store managers could see ‘how’ their stores are doing, they want to know ‘why’ sales are at these levels, thus stimulating a stream of new requests for information. In summary, the Sales Dashboard for the AT&T Retail division is delivering pervasive BI to its entire organizational hierarchy. This solution enhances the role of the store manager who seeks to detect and correct emerging problems. This Sales Dashboard is designed to be more than a dashboard that only displays a set of KPI figures; it has become a useful BI tool that enables problem analysis. The bottom-line is that AT&T can generate more V for the same P using their existing DW infrastructure.

© Bolder Technology, Inc. 2012 11 Station Casinos – How Can We Treat You Better? Station Casinos is a large entertainment and gaming company with 12,000 employees, 20 properties in Las Vegas, 500 total casinos in multiple states, and $90 billion in annual revenue.8 In the highly competitive industry of gaming, knowing and catering to your customers is a primary competitive advantage. However, Station Casinos had many disparate systems so that the data about a specific customer was not comparable. An executive at Station Casinos stated their objective as: “It’s not about sending the right customer the right offer. It’s about contacting the customer in the right way and building a personal relationship.” The solution to the problem of knowing/catering to customers had the following objectives:  Realize a return within one year  Strive for one integrated solution as a shared vision with a vendor partner  Capture and interact with data in real-time (defined as 6 seconds)  Use an industry data model emphasizing the Customer entity  Add tools for campaign management, BI enterprise reporting, and data mining The company developed a new DW infrastructure that captured and integrated data from 500 diverse sources, spanning gaming, lodging, food, beverage, entertainment, bowling, conventions, and race tracks. In total, the data warehouse manages over 9 million accounts for the customer loyalty cards. New components for the DW infrastructure included Teradata platform, Informatica data integration tools, Aprimo Relationship Manager, Teradata Warehouse Miner and Cognos reporting/analysis tool. The priority was better marketing and promotions, based upon deep knowledge of customer segmentations. Key success indicators were a more efficient (quicker and cheaper) capability for building marketing campaigns plus more effective ways to reach (touch) customers. Impacts from campaigns were evaluated within days so that timely adjustments were possible. Data quality was substantially improved so that people could trust customer data. Defining V and P What is the business value V attributed to this solution? And, what is the DW performance P required to enable this solution? The business value V is realized by Station Casinos in multiple areas:  Improved profitability from players by $2 million.  Saved $1 million per month from a $13 million slot promotion budget.  Raised monthly revenue from slot machines by 4% after marketing expenses.  Achieved a 14% improvement in guests returning within four months of a prior visit.  Increased acquisition of new customers by 160%.  Improved financial close from 15 days to 10 days.  Conducted precise marketing by increasing customer segments from 14 to 160.  Reduced cost of marketing production expenses by $500,000 per month.

© Bolder Technology, Inc. 2012 12 The DW performance P that enabled this V includes the ability to capture data from all the various touch points with customers, including 500 casinos (with 22,000 slots and 410 tables), 20 hotels (rooms, services), 69 restaurants, bars, theaters, bowling lanes, convention facilities, live entertainment shows, spas services, and sports bookings. In … includes the ability to capture each of these venues, thousands of point-of-sale devices flow data from all the various touch their data into the data warehouse in real-time. In particular, points with customers Station Casinos has an SLA requirement of less than 6 seconds from the moment of capture to when the data is available in the data warehouse to support interactions with the customer. An additional performance requirement is the standardization of customer names and addresses, along with eliminating duplications. This assures data quality necessary to support the loyalty program. Reliable customer profiles enhances the ability of Station Casinos to create and execute timely marketing campaigns and then quickly evaluate results. In summary, Station Casinos is a classic study of embracing data warehousing successfully so that the right technology is assembled efficiently to generate the intended benefits effectively. The business value V is tangible financial return but, more deeply, it is an enhancement of the customer’s experience with Station Casinos. This is the result of DW performance P that enabled the proper ‘touches’ with customers by frontline employees and marketing campaigns.

Based on the description of the above business scenarios, we will explore further the relationship of performance-to-value (P2V) in the following two sections. First, let’s consider how time affects value. Second, let’s extend time to show how performance in general affects value. Time-to-Value Curve The previous sections described business scenarios in which DW performance resulted in business value. Let’s explore the P2V relationship further by quantifying this relationship better. It would be useful to start with an early article that studied the business value of time: Do quicker results from the DW generate in higher business value? To answer this question, an article9 in 2004 introduced the concept of the Time-Value Curve and Action Time, as shown in Figure 7.

Capture Latency Data Ready For Analysis

Analysis Latency Information Delivered

Decision Action Latency Taken

Action Time Time

Figure 7 – The Time-Value Curve Business value only results when an action is taken in response to some business event. Further, the quicker an action is taken, the more business value is gained. From Figure 7, the Action Time is the time from a business event to when an action taken. It is the sum of the latencies required for the following three steps:  Capture Latency – Capture, cleanse and load the data into the data warehouse  Analysis Latency – Analyze, present and deliver the data to decision makers  Decision Latency – Enumerate alternatives and decide upon the action If the Action Time can be reduced, how does it affect the Value? Figure 8 shows that, if action is taken more quickly, then the Value is greater; hence, Value is gained. For the data warehouse, this implies that we have to capture the data faster, analyze the data faster, and decide faster. All three steps are necessary to realize the business value.

Value Business Event

Action Taken Quickly

Value Value Action Gained Taken Slowly

Time

Figure 8 - Value Gained from Quicker Action How does the Time-Value Curve relate to this study?

© Bolder Technology, Inc. 2012 14 The Time-Value Curve is a P2V relationship. Time is one aspect of DW performance that relates to how fast some task is completed, which is the same as the Time-to-Value Curve. To generalize the Time axis to the P axis, we need to flip the direction of the horizontal axis, so that more P (like faster queries) generates more V (like more revenue), as illustrate in the right curve of Figure 9.

Value Value

Time Performance

Figure 9 – Converting Time-Value to Performance-Value Notice that the curve in both cases has a distinctive ‘S’ shape and is often called the S-Curve,10 which is used to explain the behavior of many types of physical and biological systems. Four P2V Zones As shown in Figure 10, this P2V Curve has four zones with unique characteristics that have important implications to managing data warehouses.

Value Watch-Out Zone

Easy-Street Take-Off Zone Zone No-Go Zone

Performance

Figure 10 – The Four P2V Zones Let’s discuss the four zones, along with their practical implications.  No-Go Zone: Performance improvements result in small increases in value, at least initially. As an analogy, if one is pushing a heavy object, the object will not move until sufficient force is applied to over-come resistance. This is the lowest level of DW

© Bolder Technology, Inc. 2012 15 performance necessary to generate an initial level of business value, especially after moving from one DW platform to a new one. The good news is that minimal cost is expended. However, more DW performance is required before value will increase significantly.  Take-Off Zone: Small performance improvements result in ‘amazing’ increases in value. One would be motivated to acquire more and more performance since the payoffs are well worth it. However, one may be seduced into thinking that these ‘amazing’ increases (from exponential growth) will continue forever. Expect slow-down of value increases during these ‘good times’ in the Take-Off zone.  Easy-Street Zone: Any performance improvements result in solid and predictable increases in value. One has the flexibility to increase (or maybe decrease) performance to match the desired level of value. Life of a DBA managing the DW is stable and predictable. Hence, one has a degree of flexibility and agility that can be exploited when demands are volatile.  Watch-Out Zone: Any performance improvements result in little or no increases in value, very much like the No-Go zone. However, this disappointing lack of value will follow a period of relative value prosperity and, thus, will be unexpected. So in the good times, one must “watch out” that more performance continues to be a good investment for the company. What should be done when one reaches the Watch-Out Zone at right end of the P2V Curve? One should ‘watch out’ that more investment in DW performance results in more value. It does not make sense (and cents) to push performance further. Each P2V Curve assumes a certain DW infrastructure, such as a stack of storage technology, parallelism architecture, operating system and so on. The P2V relationship behaves as described above for incremental changes to this infrastructure. If the basic infrastructure is changed, then there is a new ballgame, along with a new P2V Curve to describe its characteristics, as shown in Figure 11.

Value

Performance Figure 11 – Jumping to a New P2V Curve

© Bolder Technology, Inc. 2012 16 The challenge for DW strategists is to recognize the signs as their infrastructure evolves along the P2V curve through the four zones. And, the toughest challenge is to recognize the Watch- Out Zone and manage the jump in infrastructure to a new P2V curve. Leveraging the P2V Relationship How does the P2V relationship leverage our understanding of DW best practices? Let’s consider the previous business scenarios. First, Norfolk Southern allows access to their data warehouse to their customers to determine the status of rail cars. Norfolk is in the Take-Off Zone where a small increase in P results in a large increase in V. In other words, Norfolk did more than provide simple data access; the query wizard enabled customers to sort, summarize, filter, compare, and trend over time any of their data. By providing data downloads in various formats, customers can also perform extensive analyses and couple their business processes into this data. Hence, Norfolk brought their customers closer by linking business processes, thereby changing the nature of their vendor- customer relationship. Second, Cabela’s improves its ability to marketing to its customers, increasing customer service and company revenues. Cabela’s is illustrative of jumping from the Watch-Out Zone to a new infrastructure transitioning them in the Take-Off Zone. Third, Southern California Edison assists its customer in budgeting their energy consumption with real-time notification of demand data. SCE is in the pre-Take-Off or even No-Go Zone since more work (and more DW performance) is needed to realize the full business value, especially given the considerable infrastructure investment. On the other hand, consumers using the energy budgeting tools are receiving value, which results in higher customer satisfaction. Fourth, AT&T Mobility assists its retail sales employees and especially store managers with the Sales Dashboard that provides KPI data and tools to compare and analyze sales trends. AT&T is in the Easy-Street Zone since the Sales Dashboard is constantly evolving while catering to greater number of users. Finally, Station Casinos seeks ways to enhance customer experience with a proactive touch customized to each customer. Station Casinos is now in the Easy-Street Zone since they can easily expand their coverage and services. Summary The focus of this study is to create a framework that explains the relationship of DW performance to business value. In other words, where is the best leverage for investing in DW infrastructure improvements? The study extends prior work on how time (speed to react to business events) affects value in decision situations. Business scenarios from different industries describe and emphasize the various ways that DW performance drives various types of business value. How can you apply the P2V relationship to your company? Does your company have a history of evaluating how your data warehouse generates tangible business value? Do you have a

© Bolder Technology, Inc. 2012 17 documented track record of DW innovations, which hopefully converted your DW performance to realized value? Where is your data warehouse infrastructure on the S-Curve? Are you in the No-Go Zone, trying new DW innovations with minimal resources, hoping for some small business impact? Are you in the Take-Off Zone, where new DW innovations are surprisingly leading to visible success stories? Are you in the Easy-Street Zone, where DW innovations are expected solid successes and accepted as the norm? Or, are you in the Watch-Out Zone, where your DW innovations have a long record of successes and are expected to continue forever? Consider these questions as you launch into yet another DW enhancement. And, best to your endeavors!

Endnotes

1 However, P may not be a ‘sufficient’ factor for V. See the section on Necessary and Sufficient at http://en.wikipedia.org/wiki/Causality. In other words, for V to result in actual business value, V requires P and possibly other factors. 2 The Value Wedge is adapted from the Profit Wedge as used in numerous articles over the past decade. David Schrader of Teradata explained the concept well in the context of data warehouses. See http://www.teradatamagazine.com/v10n02/Features/Build-a-better-faster-value-chain/ . 3 Teradata, “On the Right Track” whitepaper, 2012, EB-6598, http://www.teradata.com/case-studies/Norfolk- Southern-On-the-Right-Track/. Also, Teradata, Norfolk Southern Uses ADW for Superior Customer Satisfaction and ROI, Podcast Transcript, 2008, EB-0208, and Blair Hanna & Linda Richardson, Norfolk Southern, Enhancing Customer Relationships with Active DW, Teradata Partners Conference, October 2007. 4 Cheryl Krivda, Hunt for Customer Insight: Cabela’s Hits Pay Dirt With Its Analytic Sandbox, Teradata Magazine, September 2008, http://apps.teradata.com/tdmo/v08n03/Features/CaseStudies/CustomerInsight.aspx. Also, Colleen Marble, The Buck Starts Here, Teradata Magazine, Third Quarter 2012, http://www.teradatamagazine.com/v12n03/Features/The-Buck-Starts-Here/ and Dean Wynkoop, Cabela’s, Cabela’s Uses In-Database Processing for Improved Speed/Data Quality, podcast, http://www.teradata.com/podcast/cabelas-in-database/. 5 Andrew Eisses, Southern California Edison, SCE SmartConnect: Using Teradata to Build a Smarter, Cleaner Energy Future, Teradata Partners Conference, October 2011. 6 Although the data can be capture from smart meters in real time throughout the day, usage data is updated within the data warehouse on a daily basis, so that the electrical usage for the previous day is available to consumers by 8:00am each morning. See the graph at the bottom right of Figure 4. 7 Telaryn May, Matt Boos, Patrick McHugh, Damian Mahon, Paradise By The Sales Dashboard Light, Teradata Partners Conference, October 2009. Also Telaryn May, Delivering More and Better for Less: Agile for a Sales Dashboard, Teradata Partners Conference, October 2010. 8 Marc Oppenheimer, EDW Usage in the Casino Industry, Teradata Partners Conferences, October 2011. Also see the Teradata Magazine article, http://www.teradatamagazine.com/v12n01/Features/High-Stakes/ and Teradata White Paper, http://www.teradata.com/case-studies/Station-Casinos-No-Limits-Station-Casinos-Breaks-the-Mold- on-Customer-Relationships-eb6411/, and Van Baltz, Mastering the Competitive Gaming Terrain, Station Casino talk, April 2009. 9 R.D. Hackathorn, “Real Time to Real Value”, Information Management (formerly DM Review), January 2004, http://www.information-management.com/issues/20040101/7913-1.html. In the article, the framework was referred to as the “Value-Time Curve”. This study incorporates several enhancements to the original article. 10 The S-Curve is used to explain relationships in many areas from biology (population growth) to political science (voting habits). Depending on the context, it is also called Sigmoid Curve and Logistic Function. See http://en.wikipedia.org/wiki/Logistic_function for background on the history, mathematics, and applications. Often the horizontal axis is time, implying that the independent variable increases with time. This study uses the horizontal axis for DW performance, which generally increases with time as the DW matures.

© Bolder Technology, Inc. 2012 19 About the Methodology The methodology of this study is to compile and analyze business scenarios that are dependent on data warehouse infrastructures. By analyzing case studies from across various industries, a framework was created to explain the relationship of DW performance to business value. The intent is to contribute to professional education — to share insights with other IT professionals so that we can mature as an industry, amid escalating business challenges and rapidly evolving technology. The primary author is Richard Hackathorn of Bolder Technology with substantive contributions from Dan Graham and David Schrader of Teradata. Teradata permitted access to their large library of business case studies compiled over the past decade; hence, the case studies are a snapshot to IT projects in the period of 2005 to 2010, as indicated in the endnotes. Although the sample of case studies is biased toward customers of Teradata, the sample is representative of the experience and maturity trends across the entire BI/DW industry. We are appreciative of the many companies and professionals who were willing to openly share their experiences. Finally, we are appreciative of Teradata Corporation for their assistance and sponsorship of this study. About Bolder Technology Bolder Technology Inc. is a twenty year old consultancy focused on Business Intelligence and Data Warehousing. The founder and president is Dr. Richard Hackathorn, who has over thirty years of experience in the Information Technology industry as a well-known industry analyst, technology innovator, and international educator. He has pioneered many innovations in database management, decision support, client-server computing, database connectivity, data warehousing. Richard was a member of Codd & Date Associates and Database Associates, early pioneers in relational database management systems. In 1982, he founded MicroDecisionware Inc. (MDI), an early vendor of database connectivity products, growing the company to 180 employees. It was acquired in 1994 by Sybase, now part of SAP. He is a member of the IBM Gold Consultants and the Boulder BI Brain Trust. He has written three books and was a professor at the Wharton School and the University of Colorado. He received his degrees from the California Institute of Technology and the University of California, Irvine. About Our Sponsors Teradata is the global technology leader in enterprise data warehousing, analytic applications and data warehousing services. Organizations around the world rely on the power of Teradata’s award-winning solutions to obtain a single, integrated view of their business to enhance decision-making, customer relationships and profitability. Teradata and the Teradata logo are registered trademarks of Teradata Corporation and/or its affiliates in the U.S. and worldwide. NetApp, creates innovative storage and data management solutions that deliver outstanding cost efficiency and accelerate business breakthroughs. NetApp’s dedication to the principles of simplicity, innovation, and customer success has made it one of the fastest-growing storage and data management providers today. For extreme performance, capacity, and nonstop availability, Teradata chose to integrate the NetApp E-Series storage systems into their Data Warehouse and Appliance solutions. Together, NetApp and Teradata provide high performance, cost-effective, reliable, scalable, and space-efficient storage for dedicated workloads. Discover how businesses built on NetApp go further, faster at www.netapp.com. EB-7301 > 0812