Faithful readers of the OpenBI Forum over the last 28 months have probably noticed two common themes to the columns. The first is guidance for business intelligence (BI) from outside business, particularly academic areas such as statistics, operations research, computer science, economics, political science, cognitive science, psychology, etc. The second is an obsession with rigorous methods and designs for BI investigations to help “prove” the relationships implied by intelligence inquiries. How can a retail company be sure that that its latest marketing campaign resulted in increased sales and profits? How can a telecom company carefully test its new strategy that enhanced employee training will lead to better customer service which, in turn, will promote greater customer loyalty and subsequent profits? How can a financial services company determine which offers will entice prospects to become profitable customers? In each case, BI is testing hypotheses of logical form “if x then y,” or “the more of x, the less of y” or “x causes y.” The tighter the BI design, the more comfort a business can have that the interventions they initiated indeed caused the noted results.

Epidemiology and the Evidence Hierarchy

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