1.1 What Is Business Intelligence?

Let’s start with what BI isn’t. BI is not:

If that’s what BI is not, then what is it? BI combines products, technology, and methods to organize key information that management needs to improve profit and performance. More broadly, we think of BI as business information and business analyses within the context of key business processes that lead to decisions and actions and that result in improved business performance. In particular, BI means leveraging information assets within key business processes to achieve improved business performance. It involves business information and analysis that are

FIGURE 1-1 What business intelligence means in practice.

For business, the primary focus is to increase revenues and/or reduce costs, thereby improving performance and increasing profits. For the public sector, the primary focus is service to citizens, coping with budget constraints, and using resources wisely in support of an agency’s mission. Figure 1-1 illustrates this definition.

1.2 Business Intelligence in Action

To illustrate this practical working definition of BI, consider how the hotel and casino operator Harrah’s Entertainment uses BI to improve revenue and profit through customer relationship management.

Harrah’s runs not only its flagship hotel and casino in Las Vegas, Nevada, but more than two dozen casinos in a dozen other states. Its BI investment enabled Harrah’s to enjoy 16 consecutive quarters of revenue growth. In 2002, it earned a $235 million profit on more than $4 billion in revenue (Loveman, 2003). That was a startling improvement from Harrah’s solid but not spectacular performance only a few years earlier.

Harrah’s invested in BI to help it win and consolidate the loyalty of its best customers. Its first effort was the “Total Gold” program, which was modeled on airline frequent-flyer programs. However, Total Gold was too similar to the customer-loyalty programs offered by other casinos to give Harrah’s a killer edge, but it did prove to be a rich resource of data for Harrah’s subsequent BI efforts. In particular, the Total Gold data warehouse provided valuable business information about Harrah’s customers:

That business information, combined with business analysis, enabled Harrah’s both to know who its most valuable customers were and to offer them personalized service. Harrah’s evolved Total Gold into the “Total Rewards” program, which divided its gaming customers into three levels of service (gold, platinum, and diamond) based on their long-term revenue value to the company.

In addition to identifying its most valuable customers, Harrah’s also used BI to analyze what those customers wanted and what measures might win their loyalty. Diamond-level card holders would seldom if ever have to wait in line for anything, whether to check into the hotel, get their cars parked, or be seated in one of Harrah’s restaurants. If they called to reserve a room, they might qualify for special low rates based on predictions from BI about their probable value as casino customers. Platinum-level card holders received a slightly lower level of service, while gold-level card holders were essentially “flying coach.” Harrah’s succeeded in structuring its services to motivate customers to try to qualify for higher-level Total Rewards cards.

BI from the data warehouse even provided insight about how Harrah’s should arrange the floor plans in its casinos and how to make slot machines look more attractive. Real-time analytics enabled on-the-spot personalized service for valued customers, such as an instant grant of $100 credit to a loyal customer who’d hit a losing streak. All these factors helped motivate customers to come to Harrah’s and stay there to spend their gaming dollars. And this program would not have been possible without BI techniques applied to data warehouse information.

The combination of business information and business analysis is used by Harrah’s and many other successful organizations to make more structured and repeatable business decisions about the features and targeted recipients of direct marketing offers. Because motivating and retaining its most valuable casino customers is a key driver of profits, Harrah’s has refined its customer relationship management process, a core business process. The process explicitly embeds the use of the above-described business information and business analyses so that business decisions about whom to target with what measures are fact-based, analytically rigorous, and repeatable. These decisions are implemented through actions from Harrah’s front door to its casinos, restaurants, rooms, and telephone services. Those actions have improved Harrah’s business performance, resulting in increased profit.

The above example defines BI from a business perspective, not from a technical perspective, because BI is primarily about profit. That’s not a technical term and it’s not about bits and bytes; it’s about your bottom line. And it’s what you should expect from BI. It may also have occurred to you that BI needs to be highly specific to your industry and to how your company competes in that industry. Measures such as “Revpar” and “stays” are specific to the hotel industry and have no meaning in, say, the freight industry, in which measures such as “revenue per ton-mile” are the norm. More broadly, to get the most out of BI, you must adapt it to each specific company and situation. The kinds of business information, business analyses, and business decisions that BI must deliver or enable, and the way that BI creates business value must be specifically determined for each company. That’s the only way to get the best possible return on your BI investment. Given this, we see that business information and business analyses are components of BI that can be combined in a wide variety of ways to create the right BI approach for your organization. Table 1-1 shows examples.

1.3 The Origins of Business Intelligence

Now that we have a better understanding of what BI is, let’s take a brief look at its origins. This examination will help show where BI fits with other parts of the IT portfolio, such as enterprise transactional applications like enterprise requirements planning (ERP), and will help differentiate BI uses from other IT uses. It’s also important to understand that enabling BI technologies are mature, low-risk technologies that have been used successfully by major companies for more than a decade.

Although recently the term BI has become one of the new IT buzzwords, the organizational quest for BI is not new. Approaches to BI have evolved over decades of technological innovation and management experience with IT. Two early examples of BI are

Table 1-1
Combining business information with business analysis for BI

Both of these examples illustrate the desire of executives, managers, analysts, and knowledge workers to harness information to improve profits and performance. Both can also be seen as steps along an evolutionary path.

In the context of discussing information challenges for the 21st century, Peter Drucker observed that “… information technology so far has been a producer of data rather than a producer of information” (Drucker, 2001). This view comports with capital investment trends of the past 15 years. In the 1990s, much investment in IT was focused on the following:

Collectively, these kinds of IT can be considered transactional IT, with business benefits such as transactional efficiency, internal process integration, back-office process automation, transactional status visibility, and reduced information sharing costs. The primary motivation for many of these investments was better control over more efficient day-to-day operations. For example, ERP systems allow companies to track order status, inventory, and customer service in real-time. SCM systems provide supply chain planning functions, and CRM systems provide sales pipeline management and call center management tools.

As the 1990s unfolded, we also saw the emergence of data warehousing (DW), which is a means of harnessing the blizzard of data generated by transactional IT systems. Many of the early adopters of DW were in transaction-intensive businesses (such as financial services, insurance, and telecommunications) in which marketing managers tried to make sense of data about millions of customer transactions. Early efforts in DW were focused on conquering the IT challenges associated with loading, integrating, and storing large quantities of data. Although some organizations recognized the potential that DW approaches held for obtaining new insights into their business that would provide competitive advantage, many organizations limited their DW efforts to supporting better and faster reporting and to answering ad hoc requests for information by business users. After years of making substantial annual investments in DW programs, many organizations began to question the business value of DW investments. The introduction of BI as a new focus in the industry over the past several years is largely an answer to this quest. Vendors have also introduced new BI applications (such as activity-based costing, supply chain analytics, customer analytics, scorecards and dashboards) in response to business demands to have better information to analyze and measure business performance.

DW is a key enabler of BI. It became feasible and economical as a result of rapidly declining data storage and processing costs, special-purpose data integration tools, innovations in the way that data can be organized in databases, and innovations in the way data can be converted to information and presented to information consumers within a business. For the first time, it was technically possible to bring together data about the thousands or millions of daily transactions of a business and turn it into useful information. As the 1990s came to a close, enterprise applications had already been widely adopted by major organizations. Innovators were beginning to look at how to leverage IT for purposes such as strategic enterprise management, managing customer profitability, improving supply chain and/or operations performance, improving “front-office” business processes such as sales force management and campaign management, and improving indirect business processes such as budgeting and business planning. Many of the technical challenges had been overcome in DW, creating the opportunity to expand the use of DW to new parts of the enterprise and to industries that had lagged in adoption.

As was often the case with IT, technological advances spawned advances in management thinking about how to leverage the technological advances to create business value. At the turn of the 21st century, the principal limitations of DW, from the point of view of delivering business value, were that:

This is not to say that traditional approaches were deficient for designing, building, and deploying data warehouses. Rather, the problem was simply that such approaches did not design ROI into the process, which sometimes resulted in DW investments that did not pay off in improved profit or organizational performance.

Historically, many DW and BI initiatives have been driven by IT, and much of the focus within the industry has been on the technical aspects of delivering information to the BI user community. Now that many of the technical challenges and trade-offs are well understood, attention has now shifted to expanding the ways in which BI can be used to deliver business value and to enhancing BI development methods to ensure that BI investments pay off. Well-known companies in a wide range of industries have already realized some of the promise of BI, and the underlying methods and technologies for delivering business value are well established. For example, Avnet, Barclays, BellSouth, Ford, Hewlett-Packard (HP), Nationwide, and Sears have established BI programs that have been used to drive revenues, reduce costs, or both.

1.4 Business Intelligence Today

Peter Drucker (2001) has observed that over the past century, businesses have continually reengineered direct labor and asset productivity to the point that many industries are approaching diminishing returns. Accordingly, American businesses must look to other means to compete, and BI is bringing a powerful new tool to businesses. With an effectively executed BI program, businesses can compete by being better than the competition at leveraging information to improve profits and performance. An executive vice president at Wachovia Bank, for example, has stated that “Wachovia’s competitive position depends upon our ability to use information faster and smarter than our competition” (Davenport et al., 2001). This line of thinking—that BI can confer a competitive advantage–represents a paradigm shift in how information is used in business. To make this shift, businesses need to rethink how they use information in general and BI in particular.

Whereas the DW and BI industry has historically focused on the technical challenges, technical methods, and project management methods required to deploy DW and BI successfully, a key recent innovation is the use of business-centric BI methods. These methods are designed to help companies fully leverage the profit potential of BI. Business-centric BI methods—which extend the technical methods of BI pioneers such as William Inmon, Ralph Kimball, and Claudia Imhoff—design ROI into BI initiatives from the outset and systematically drive the use of BI into the core business processes and decisions that determine business results. The BI Pathway method discussed in this book is an example of business-centric BI methods.

Business-centric BI methods go beyond traditional approaches by putting rigor into defining the business value capture mechanism for each BI project. This includes determining and specifying—in advance—how business processes and key decision processes must change in order to leverage BI investments, which are managed as a portfolio. This also includes using process reengineering and process improvement techniques to ensure that BI projects actually deliver the intended ROI.

When using business-centric methods, the BI team no longer throws the BI application “over the wall” after users have been trained, hoping that the business organization will understand what changes are needed and how to make them. Rather, BI team responsibilities are extended to include helping the business organization execute the changes to the business processes and decision processes that drive business results. Business-centric methods recognize that the missing link in many DW and BI efforts is the lack of clarity in the value proposition and/or the lack of business process change to capture the business value of BI.

The availability and affordability of business-centric methods for designing and developing BI means that a cohesive BI system that drives profits and performance is well within the means of any business enterprise.

1.5 Using Business Intelligence to Capture Business Value

In economic terms, the business value of an investment (an asset) is the net present value of the after-tax cash flows associated with the investment. For example, the business value of an investment in a manufacturing plant is the sum of the incremental after-tax cash flows associated with the sale of the products produced at the plant. Similarly, an investment in BI creates an asset that must be used to generate incremental after-tax cash flow. Accordingly, BI investments should be subjected to a rigorous assessment of how the investment will result in increased revenues, reduced costs, or both.

Although there are hundreds of ways to express business benefits, no business value is associated with an investment unless the benefits achieved result in increased after-tax cash flows. Again, there is no business value associated with an investment unless the benefits achieved connect to strategic goals. For business, the focus is on primarily increased after-tax cash flows; for government agencies, improved performance and service to citizens. These principles apply to investments in factories, equipment, and BI.

For example, it is common for BI vendor value propositions to emphasize business benefits such as agility, responsiveness, customer intimacy, information sharing, flexibility, and collaboration. But investing in BI to achieve such business benefits may actually destroy business value unless those attributes can be defined in operational terms and realized through business processes that affect revenues or costs. For example, a $2 million investment in a BI application must result in incremental after-tax cash flow of at least $2 million or the organization will suffer a reduction in assets.

To illustrate this point, many companies use BI to improve customer segmentation, customer acquisition, and customer retention. These improvements can be linked to reduced customer acquisition costs, increased revenues, and increased customer lifetime value, which translate to increased after-tax cash flows. However, a BI investment that improves demand forecasting will not deliver business value unless the forecasts are actually incorporated into operational business processes that then deliver reduced inventory, reduced order expediting costs, or some other tangible economic benefit. In other words, the business benefit “improved forecasting” is useless unless it is somehow converted into incremental after-tax cash flow.

Looked at more broadly, the quest for delivering business value via BI can be seen as a matter of determining how an organization can use BI to

In other words, the business value of BI lies in its use within management processes that affect operational processes that drive revenue or reduce costs, and/or in its use within those operational processes themselves. Let’s illustrate this point with a couple of examples.

Just like Harrah’s, many companies these days aspire to use customer relationship management strategies that distinguish among customers based on their value. In retail banking, a customer with loans, large savings accounts, a checking account with large balances, and credit card balances who uses online banking is much more valuable than a customer with only a low-balance checking account who comes into a branch frequently. Clearly, the bank would not want to lose the former customer, whereas it might be willing to lose the latter. For the bank to implement a customer relationship management strategy based on the difference in customer value, it first needs BI applications that allow the bank to know which customers are highly valuable, which are valuable, which are less valuable, and which are not valuable. But that knowledge alone is not enough to ensure that the bank does not lose highly valuable customers. It must also have management processes and operational processes that take account of the differences in customer value and treat the highly valuable customers preferentially. For example, the bank might waive a late fee on a loan payment for the valuable customer but not for the less valuable customer.

The strategy of treating customers differently depending on their value as customers is also used in SCM. The central idea is to design and optimize supply chain business processes to provide superior service to those customers who drive the bulk of one’s profit. To do this, a manufacturer needs a BI application that allows it to know who its most profitable customers are. As with the bank, however, this knowledge is of little use unless it can be translated into business rules for manufacturing schedules that recognize that orders for the most valuable customers should be serviced ahead of those from marginal customers.

Because capturing the business value of BI depends on being able to use BI in a way that has an operational impact, organizations must look beyond the initial rollout of BI applications (Figure 1-2).

As shown in Figure 1-2, capturing the business value of BI requires organizations to go well beyond the technical implementation of a BI environment. Specifically, organizations must engage in effective process engineering and change management in order to capture business value from BI. The implication of this requirement is that BI methodologies must be extended to include these additional preconditions, as shown in Figure 1-3.

FIGURE 1-2 Looking beyond the rollout is essential to get the best result.

FIGURE 1-3 Business and technical preconditions for delivering business value through business intelligence.

Of the preconditions shown in Figure 1-3, those in light gray boxes are generally well understood, based on DW industry experience over the past decade. The other two preconditions, process engineering and change management, are not as well understood for BI applications. They currently stand as barriers to capturing the business value of BI. Let us examine this idea with an example.

In a typical large company, much of the information routinely available to managers comes in the form of static reports and from ad hoc information gathering and analysis. A general manager who receives a monthly profit and loss statement may notice that revenue is less than budgeted, in which case he or she will most likely assign a staff analyst, middle manager, or functional manager to figure out the factors contributing to the variance. The specific form of the analysis, the manner in which it is done, and the information sources from which the analyst draws are likely to be ad hoc and idiosyncratic. Most likely, the analyst will do the best he or she can with the information and time available, with little opportunity for extensive scenario analysis and assessment of alternative courses of action.

Imagine now that the company invests in a BI application for revenue management. The application is capable of looking at revenue trends by customer, by geographic region, by product, and by salesperson. Further, it is rolled out companywide, with online training available to any potential business user who may want to use the application. For some reason, however, revenues continue to decline and analysis of application use shows that only a handful of potential users regularly use the application. The chief financial officer (CFO) initiates a project review to find out why the projected incremental revenues have not materialized. It is discovered that there was no plan for how the BI application would be used within the user community and no plan for introducing and ensuring the efficacy of the changes required to capture the business value of the investment. This is particularly vexing to the CFO, given that a subject matter expert (SME) was part of the application development team.

To avoid the above scenario, we recommend using a structured approach to business value capture. In the sections that follow, we will look at strategic alignment, process engineering, and change management as key interrelated preconditions for capturing business value.

1.6 How Do We Achieve Strategic Alignment?

As DW matured in the 1990s, a considerable body of expertise developed around the task of aligning the use of BI with organizational strategies. Essentially, it is a matter of

Although this alignment process is straightforward in concept, a wide variety of challenges must be overcome, as with any endeavor in IT. For example, working with business users of BI to determine their business questions (information requirements) is still an art despite the existence of structured requirements gathering methods. Business users are sometimes be so focused on daily challenges that they have difficulty envisioning how BI can be leveraged to improve organizational performance. On the technical side, a wide array of choices must be made with respect to architecture, methodology, tools, technologies, and processes—choices that impact project risk, total cost of ownership, and ultimately the magnitude of the “investment” portion of ROI. There is also the challenge of incorporating sufficient architectural flexibility to respond to new BI needs as strategic drivers evolve.

Although the above-described challenges of strategic alignment are significant, a substantial body of knowledge exists describing how to go about meeting those challenges, and the methods used to achieve strategic alignment are effective and widely adopted. That said, strategic alignment, although necessary for achieving business value, is not sufficient in and of itself. The reason, as we’ve seen, is that the availability of strategically aligned BI does not guarantee it will be used to improve the results of critical business processes that determine the revenues and costs of the business. We must also engage in process engineering and change management.

1.7 The Need for Process Engineering

Many different types of processes are used to run a business. There are strategic, tactical, and operational planning processes. There are financial, operational, marketing, product development, and human resources management processes. There are performance monitoring and measurement processes, quality management processes, and continuous improvement processes. There are supply chain and customer relationship management processes. All of these processes involve the use of information, analytical frameworks, and tools to support the many decisions managers have to make. In other words, these processes require BI approaches in order to be optimized. The economic and technological advances over the past decade, in IT generally and DW specifically, have opened a new frontier for the use of BI to deliver business value.

In our view, the key challenge in using BI to capture business value lies in the way that information and analytical frameworks used within organizations have largely depended on individual initiative and ad hoc choices. At a broader level, the use of business information to conduct business analysis is often an idiosyncratic, ad hoc practice that varies by industry and by company within each industry. For example, revenue optimization models are a staple of asset-intensive, high fixed-cost industries such as the lodging industry and the airline industry, but they are not widely adopted in discrete manufacturing industries. Within industries, the information and analytical frameworks used varies by company positioning within the industry. Although enterprise applications such as ERP, SCM, and CRM provide structure, automation, and process standardization for managing day-to-day transactions, organizational efforts to utilize BI approaches are more unstructured, more ad hoc, and less widely adopted, For example,

• Use of scorecards and dashboards in the context of strategic enterprise management is still in its early days.

Although DW has been around for a decade now, most organizations are still in the early stages of exploitation of the potential of BI, and this presents both opportunities and risks. The opportunity, simply stated, is that effective use of BI can deliver incremental profit and superior performance. The risk is that organizations will not do the process engineering and change management needed for using BI to capture business value. To capture business value, it is our professional judgment that organizations will benefit from a rigorous process engineering approach. This entails looking beyond vendor value propositions regarding packaged analytics, “BI for the masses,” and “BI best practices” to determine specifically, with economic and process engineering rigor, how adoption of BI will result in incremental revenues or incremental cost reductions. To illustrate this concept, let’s examine the simple hypothetical BI application shown in Figure 1-4.

Assume that Company A manufactures a semi-custom product and competes on cost. Given that cost is the key basis of competition, Company A has developed a BI application that is used to monitor productivity. This application is strategically aligned because productivity improvement is critical to cost reduction. We can see from Figure 1-4 that actual productivity is less than planned productivity, so our BI application has delivered useful information. That said, we can also see from the questions posed that having useful information is not the same as exploiting that information. Unless there are specific management processes for using that information in a timely manner, having the BI application will not result in business value creation. Process engineering focuses on providing answers to the questions posed in Figure 1-4, and those answers can be captured as the foundation for business rules, standard processes, and standard analytical applications for responding to productivity variances.

FIGURE 1-4 Process engineering to capture business value.

This approach can be used for all planned BI applications and will allow organizations to move from ad hoc responses for recurring business conditions to effective repeatable responses that capture the business value of BI. From this simple example, we can generalize that the business value of BI lies in its effective use within management processes and/or operational processes that drive revenue or reduce costs. Accordingly, process engineering is the critical link between building and delivering BI applications that are strategically aligned and capturing the business value those BI applications are supposed to deliver. Although this proposition is hardly novel or remarkable, we believe that BI industry experience shows that the importance of process engineering has been overlooked or undervalued. BI has been viewed foremost as a technological tool, neglecting the fact that it must be embedded in specific business processes in order to deliver its full value.

We further believe that process engineering is especially important as we stand at the frontier of expanded use of BI—with its potential for altering competitive landscapes. Vendors are offering a wide range of innovative products with value propositions that are appealing as general propositions, especially to organizations that are prone to looking for quick fixes. Although we are bullish on some of these products when used appropriately, we remain convinced that organizations must be rigorous about determining how the use of these products can deliver business value in their specific contexts, and process engineering is essential to that determination.

1.8 Process Engineering in Practice

We have the privilege of serving each year as judges for the annual Best Practices in Data Warehousing Awards. The competition is conducted by The Data Warehousing Institute (TDWI), the leading membership association of IT and business professionals involved in DW and BI. One of us recently served on the panel of judges for the advanced analytics best practices category. Eight nominees were in that category, including leading companies in a variety of industries. Although all of the nominees had achieved a high degree of strategic alignment with their BI applications, what distinguished the leaders in the eyes of the judges was the degree to which they had integrated BI with value-driving business processes. For example, the category winner, Lands’ End, used a process engineering approach that included developing a corporate metric model and mind maps that anticipated the specific ways that inventory managers would use metrics to improve business performance. Another category leader, a leading automobile manufacturer, developed a closed-loop inventory management process that used BI to reduce inventory and cycle time. Viewed from the perspective shown in Figure 1-4, these companies captured business value by attending to process engineering and change management, thus satisfying key business preconditions for success.

When using process engineering to determine exactly how BI will be used to increase revenue or reduce costs, remember that the process should be tailored to the situation because the degree of process change associated with a BI investment varies from one situation to another. At one end of the spectrum, a BI application may simply deliver higher-quality information on a timelier basis. An example would be a BI application that provides managerial accounting information, such as historical product costs, to a company’s budgeting process. The typical budget process makes numerous assumptions using information that budget analysts have squirreled away in numerous spreadsheets. A new BI application would provide an integrated view of product costs that could be used across the budget department, but the budget process itself might not change much, other than being easier to obtain information for making budget assumptions.

At the other end of the spectrum, a BI application may involve totally new information, analytical routines, and management processes. An example drawn from our experience involved a BI application used by a $2.5 billion consumer products manufacturer for sales and operations planning. In this case, the manufacturer lacked sales trend information that could be used for demand forecasting and did not have a sales and operations planning process. For cost improvement reasons, the manufacturer determined that it needed such a process. From a management processes reengineering perspective, there was no “as-is” state, and the implementation of the BI application involved providing managers with new information, presented with new tools, for use within a new management process.

Given the potential differences in the scope of management process change, the scope of the management process engineering must be fitted to the task at hand.

1.9 The Need for Change Management

Process engineering identifies how BI applications will be used within the context of key management and operational processes that drive increased revenue and/or reduced costs. It provides a map of which processes must change and how they must change in order to create business value with BI applications. Thus, it lays the foundation for change management because process changes drive changes in individual and organizational behavior.

Change management is a generic discipline with principles that are generally understood and have been widely applied for decades to a variety of organizational change processes, including business process changes induced by IT investments in enterprise applications such as ERP. That said, change management as it applies to BI initiatives has not yet been developed into a systematic body of knowledge. A number of BI project failures can be attributed to ineffective change management.

In our view, these failures point to a shared problem in the BI industry: that of overstating the ease with which BI applications can be deployed and accepted within organizations. These overstatements—whether by ERP vendors with packaged data warehouses, by BI vendors with packaged analytics, by consultants, or by IT organizations themselves—have a tendency to produce situations in which the adoption risk associated with BI applications is systematically understated. The result is that change management activities are ignored or under-funded.

One of the primary change management challenges for BI applications is that most organizations use information and analytical frameworks within management processes in an unstructured, ad hoc manner and that the degree of support for such processes has, until recently, been very limited. The advent of collaboration capabilities within BI products presents a tremendous opportunity, but the application of business rules thinking and workflow technologies has been largely focused on repetitive, routine tasks such as processing invoices or purchase requisitions. Given that the targeted user community for many BI applications consists of executives, managers, and business analysts, the challenge of introducing structure in the use of information and analytical tools in any given case could be substantial. In effect, the use of BI within the executive and management ranks of companies is highly unstructured—especially compared with the use of transactional IT systems such as ERP, which is highly structured and standardized. To capture the business value of BI initiatives aimed at management processes, organizations will have to apply scientific management and process control thinking to “white collar” activities, a substantial change.

1.10 Business Value Analysis of Business Intelligence Initiatives

At this point in the discussion, we have examined strategic alignment, management process engineering, and change management as key preconditions for ensuring that BI investments result in business value (positive after-tax cash flows). We have argued that analytical rigor, process analysis, and empirical methods should be used in a structured manner to determine how BI can be used to deliver increased revenues and/or reduced costs. We believe there is no shortcut for rigorous up-front business value analysis of how investments in BI will deliver business value. Although traditional ROI analysis is certainly a key component of business value analysis, we recommend taking a broader analytical perspective, consisting of the following:

Business value analysis is the foundation of the business case for capital budgeting purposes, but it has a broader purpose as well. Specifically, the process engineering and change analysis activities identify the key business activities that must be successfully performed if the BI investment is to capture business value. For example, revised management and/or operational processes must be defined, the community of BI users must be trained in these processes and in the use of BI within the processes, and mechanisms for evaluating the progress of the change process must be implemented. In other words, process engineering and change analysis lay the foundation for managing for business value delivery.

1.11 Managing for Business Value Delivery

As with any capital project, capturing the business value projected for the investment requires effective management. Fortunately, the DW industry has developed an extensive body of knowledge about the technical development and project management preconditions for project success. In fact, our view is that with the maturing of DW tools and technologies over the past decade, technical impediments to success are no longer the central issue. Rather, we believe the more substantial challenge lies in meeting the business preconditions, particularly the needs for identifying opportunities for leveraging BI, process engineering, and change management.

To this point in the discussion, the perspective we’ve advanced is that analytical rigor, process analysis, and empirical methods should be used in a structured manner to determine how BI can be used to deliver increased revenues and/or reduced costs. Competing with this perspective is a marketing message to which executives are frequently exposed: that BI products provide “out-of-the-box” solutions that can be implemented in very short timeframes to deliver substantial business benefits. Experience suggests that business executives often respond to value propositions that use business language to make what are essentially emotional appeals to executives’ aspirations (beat the competition/make money) and insecurities (fear of failure). This is not to say that the BI products in the market cannot be used to deliver business value, because history has shown that they can—if they are used intelligently. Rather, it is to say that executives need to guard against these emotional appeals, because these appeals can cause an organization to underestimate the degree of process engineering and change management required for capturing the business value of BI investments, and when this happens, the organization does not manage for business value capture. It thus increases its chances of failure.

To overcome this risk, organizations need to focus like a laser on the key value capture activities—process engineering and change management. The BI project cannot stop when the BI application is deployed. In fact, we can consider the point of deployment to be like “halftime” in a sporting event. To ensure that business value is captured, the team must continue to perform at a high level. This is not to say that the players in the game will stay static. Once the BI asset has been built, the onus for business value capture falls on the business side, which is often not commonly discussed and understood. In fact, there is a strong case for this responsibility residing with the business, rather than with IT. Empirical studies suggest that IT investments deliver greater value when the responsibility for business value capture resides on the business side. This is also a cultural issue: those organizations with effective IT/business partnerships achieve better results.

Ultimately, capturing the business value of BI is a strategic challenge and opportunity, and we have seen that the potential for BI is substantial. With appropriate rigor and a willingness to manage for business value, there is no reason that organizations cannot capture the business value of BI, regardless of how it might be defined in their specific circumstances.

1.12 Key Points to Remember

1.13 Think Tank