This month we wrap up our discussion of the data warehouse planning process by discussing how to measure our success. It is critical to the success of our ongoing data warehousing efforts to establish metrics early in the data warehousing process. Some believe, based on the types of DSS applications we intend to deliver, that such measurement is difficult – or even impossible – to predict. Yet, we must predict in some measurable, recognizable fashion to ensure continued commitment and funding to our data warehousing environment.

When undertaking business metric design, we must first define what it is we hope to accomplish or justify. A crucial first step is to develop a set of ground rules in establishing our measurement framework (in this case, tangible value back to the business in terms of operational cost savings and/or new business opportunities to be realized on an annual basis (or sooner)). Our business metric process must, therefore, establish and collect measurements of our success on a regulated, timed basis that can be audited and verified. Statements about data warehouse value vary widely based on the type of implementation and range from a few percentile gain up to thousands of percent improvement in either operational savings or profitability. Because the way people define data warehousing varies widely, and because its initial role is often to enable more complete and accurate management reporting, our measurement and metric process needs to change across time. The first set of measures we create needs to be revisited often, as the business matures in its understanding and use of the warehousing environment and moves beyond business event recording and reporting into business event prediction where the real value in warehousing is realized.

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