The science of statistical analysis is an empirical one, which is to say that a given question will yield a reliable number based on a formula. In business, such numbers are used to measure performance, identify shortcomings or uncover opportunities. Math, however, doesn't always cleanly reconcile itself to desired outcomes Take the health care industry, where the recently adopted buzz phrase "pay for performance" has been applied to measure not just the timely application of care to a patient, but the impact of that treatment on the human condition. "The model in managed care today says I'm not going to pay you for doing the work, I'm going to pay you for getting the right outcome," says Meg Aranow, CIO at Boston Medical Center, a 540-bed hospital and teaching facility affiliated with Boston University. "We won't pay you to just see the patient, we'll pay you to make the patient better. That's rational. But in health care, there's a lot of argument about what 'better' means."
The very same issue is under examination within many mature business intelligence initiatives where participants are asking whether accepted performance metrics are actually serving desired business outcomes. Does desirable cost takeout have a larger negative impact on the quality of our goods and services? Are the goals of executives aligned with the values customers associate with our business? Such questions can be surveyed, but in the end, the business needs to know why it is in business in the first place. (Aranow will address this question and others in her upcoming presentation at BI Forum, June 4-6 at the Argent Hotel in San Francisco.)
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