Statistician George Box said, “Essentially, all models are wrong, but some are useful.” Clarifying what he meant, Box went on to say, “Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful?” I think he had a point that’s worth thinking about a bit.

The increased use of data mining and predictive analytical techniques within organizations to reduce risk and improve decision making means that managers will be exposed more and more to the results of these approaches. They will increasingly be using them to make recommendations or to decide on courses of action. But how do you know how wrong the model is and whether it can be useful?

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