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.
Establishing business metrics goes hand-in-hand with our business case development in terms of justifying our investment across time. Therefore, our logical progression of activities is to:
- Define metrics in support of the business case.
- Establish means to gather these metrics in terms of procedures and resources.
- Define reporting/analysis/feedback loop.
- Identify new measures/metrics to track based on analysis to feed back into step 1.
An approach one might undertake follows:
- Establish a business/IT core team.
- Educate the core team and define business measures.
- Conduct business measurement sessions with key stakeholders.
Establish a measurement framework for each benefit. a. This activity is completed during the joint sessions and validated with the core business managers who are responsible for performance. Measures need to be developed for both tangible and intangible benefits and must involve collecting both quantitative and qualitative returns.
Develop a justification approach as being either: a. Return on investment (ROI)
b. Net present value (NPV)
c. Internal rate of return (IRR)
d. Cost displacement
- Publish findings and recommended approaches.
Once accepted, review and revise the findings and obtain approval to proceed with a prototype investment. a. Iterate through prototype development and deployment by deploying the measurement process and gathering benefit returns on an ongoing basis for up to one year after deployment of the prototype system; measuring investment against projected targets on a rolling six-month basis; measuring results and proactively providing input to prototype refinement and adjusting benefit measures and mechanisms
- Adjust measurement framework and provide input to the second iteration of deployment (via subsequent prototypes).
Our set of implemented measures should focus on productivity improvement areas that are important to the business. Measuring financial benefits is only one part of the process, the others being establishing competitive and organizational or structural efficiency improvements for the business.
By holding the business accountable for ongoing measurement collection and analysis, the data warehouse financial analysis will result in:
- The ability to measure the productivity level of business management.
- Promoting operational efficiency through reducing physical infrastructure and resource costs.
- Measuring business diversification resulting in increased shareholder value.
- Avoiding capital or discretionary costs due to required purchases or taxes.
For a private sector enterprise, return on management equates to revenue, lower operating costs, shareholder equity, purchases, acquisitions and taxation. For a public sector enterprise, return on management equates to fees or licenses collected, less overhead staffing costs, benefits and materials purchases. Again, these are operational efficiency perspectives and are inward focused. To enhance these measurement metrics, we need to move beyond the purely operational view of the world and look at how we can improve our competitive position and measure this success to again validate our investment decision in this technology. These extended value measures include reducing customer acquisition and retention costs, dealing with new product profitability and enhancing our level of service by pushing our historical record of business back out to the customer touchpoints for use and analysis. These areas hold the thousand plus percentile improvements that we cannot realize out of purely operational savings and provide much greater value metrics for fueling future growth of our data warehousing investment.
Next month we will delve into the design process by discussing the rapid analysis and design of data structures and related processes. For a more complete description of the process and deliverables discussed in this month’s edition, please contact the author.
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