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

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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). © Bolder Technology, Inc. 2012 3 Revenue 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
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