Return on investment (ROI), the yardstick against which most corporate IT projects are measured, has not been consistently used as a justification for data warehousing for two reasons. First, in the rush to implement this highly popular decision support solution and important competitive weapon, early adopters have tended to evaluate data warehousing using less stringent criteria than for other technology outlays. Second, due to the relative immaturity of the technology, data warehousing projects are recognized as inherently risky and deserving of greater latitude in delivering ROI. As data warehousing gains in popularity, however, the next wave of corporate users is becoming more results oriented and cost conscious and demanding a more accurate measurement of quantitative and qualitative returns.
Although hard numbers supporting data warehousing ROI are only beginning to trickle in, Forrester Research asserts growth is "guaranteed,"1 and IDC Canada predicts a 38 percent annual compound rate of growth through Y2000.2 Many companies are initiating or expanding their data warehousing projects in expectation of significant business benefits. Data warehousing's major attractions, according to a Forrester survey of 50 Fortune 1,000 companies, are better data to guide decisions, the ability to make decisions faster and a competitive advantage, all of which carry the potential for enormous returns.1 Data warehousing can also strengthen marketing effectiveness by supplying integrated customer profiles and revealing trends. From a cost savings standpoint, a warehouse can deliver a more efficient decision support infrastructure than individual systems.
Moving beyond such intuitive expectations to measurement of ROI not only gives management statistical evidence of an implementation's success, but also provides a mechanism for managing and retaining its benefits throughout the data warehouse's lifecycle. To achieve this goal, managers need to undertake a four- step process:
- identifying the organization's anticipated benefits from data warehousing;
- establishing a measurement baseline;
- measuring the investment and the actual benefits; and
- determining how to retain these benefits as organizational objectives change.
Before the benefits of data warehousing can be measured, an organization must decide what it hopes to gain in terms of both "hard" and "soft" results. The information and knowledge supplied by a data warehouse are not ends in themselves, but rather a means to achieve specific outcomes, such as increased revenue or productivity, lower costs or better customer service. In addition to the more straightforward advantages, such as time saved in preparing reports or consolidation of data storage, companies should consider such intangibles as faster reaction time and more accurate decision-making. Once a company has identified the top business needs to be addressed by the data warehouse and has crystallized desired outcomes, it can move on to the measurement phase.
Accurately measuring ROI hinges on establishing a baseline, a snapshot of the way the organization operates without a data warehouse. A baseline serves as a comparison point so that companies can estimate the expected benefits. The baseline should include data on such criteria as time, human resources, cost, performance, results, etc. For example, how long does it currently take to locate, extract, understand and apply data? What is the average cycle time to bring a new product or service to market? What are the demands on time and resources to develop and deploy a customer-centric application, and how do these costs impact overall productivity? And are results delivered in time to act on a competitive threat or new product release?
The next step is selecting and, if necessary, modifying a measurement technique that can accurately reveal the returns delivered by the data warehouse relative to top business needs. Because the true value of a data warehouse must reflect both quantitative and qualitative benefits, the traditional ROI calculation--which analyzes tangible benefits minus costs--omits the "soft" benefits that help make a compelling case for a warehouse. As a result, business analysts are turning to other techniques to augment or replace ROI. These include Net Present Value, which gauges tomorrow's return in today's dollars; Cost Displacement, which compares the cost of the new system against the one it will replace; and Business Value-Added, which measures technology not in dollars but in terms of its support of key business goals and metrics. Further, these approaches can be modified to determine intangible benefits.
Once an organization has measured anticipated benefits and has chosen to build a data warehouse, it should put in place the mechanisms for ensuring continued returns over the life of the system. A data warehouse must be flexibly managed to accommodate the inevitable changes in business size, goals, markets, structure and other variables that can alter expectations about what the warehouse should provide. Managers should be held accountable for ongoing measurement and collaboration that can ensure the business benefits of the warehouse are maintained or increased. Without a strong commitment from management, the data warehouse is likely to fail.
The continued popularity of data warehousing as a strategic tool is indisputable, although few users to date are able to apply numbers to its value. To ensure the success of this sizeable IT investment, however, it is important to define and quantify up front the tangible and intangible benefits expected by both management and users. By undertaking a methodical evaluation process that tightly links key business goals to anticipated returns, an organization can help ensure the warehouse initially delivers on its promise and maintains its value throughout business changes.
1 The Forrester Report, "The Fortune 1,000 is on a Data Warehousing Rocket Ship," by Ted Schadler, Stan Dolberg, Elizabeth W. Boehm, Craig Massey and Tom Buss, V. 8, No. 6, Sept., 1997, Forrester Research.
2 IDC Special Report, "The Foundations of Wisdom: A Study of the Financial Impact of Data Warehousing," by Stephen Graham, Dirk Coburn and Carsten Olesen, 1996, International Data Corporation (Canada) Ltd.
( The information contained in this column is general in nature and is not intended to address the specific circumstances of any indivual or entity.)
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