Why Predictions Are Not Enough

In recent times, I have read a number of articles lamenting the frequent lack of value resulting from large-scale analytics and data science initiatives. While I have seen substantial value driven from many efforts, I have also seen examples where the results were very poor. My belief is that oftentimes the problems can be boiled down to one basic mistake. Namely, thinking that generating predictions, forecasts, or simulations is enough. It is not.

Predictions Are The Starting Point…

Almost by definition, advanced analytics or data science initiatives involve applying some type of algorithm to data in order to find patterns. These algorithms are typically then used to generate one or more of the following:

Predictions about future events. For example, who is most likely to respond to a given offer?
Forecasts of future results. For example, what sales can we expect from the upcoming promotion?
Simulations of various scenarios. For example, what will happen if I shift some of my budget from paid search to television advertising?

There are other uses of algorithms and nuances between different types of predictions, but for our purposes here these three examples suffice and illustrate the point.

In each case, the output is information about what might be expected in the …

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