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February 2017, Vol. 5, No. 1

Prescriptive models take analytics one step beyond

In the beginning, there was descriptive analytics -- data-parsing methodologies that cleverly analyzed large amounts of data about customers, products, financials or most anything else and yielded insightful new categories for those items. Predictive analytics then followed as an even more dazzling practice that could fine-tune our understanding of "what comes next" with great accuracy and granularity so we could maximize our time and investment in planning for the outcome we want. Next in line is prescriptive analytics: the science of outcomes. It's less intuitive and much harder to embrace, yet it feeds the enterprise the kind of news we don't necessarily want to hear. Descriptive and predictive results simply provide better data for making decisions -- always a good thing -- and an important refinement of what is already happening. But prescriptive results take it a step further: They tell us what to do. That makes prescriptive at least as important as its siblings in moving the enterprise forward. Prescriptive models don't ...

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