A growing number of companies are setting up predictive analytics programs and investing in predictive modeling tools. But fully understanding the nuances of the discipline and honing internal processes for building effective predictive models can be elusive goals. The great promise is learning things from your data that you may not have thought of before.  But also remember that the data is all that the modeling program sees of the world. If the data has a hidden bias or constraint, the machine will think that everything does. A great example is an artificial intelligence program that chugged overnight on a database being entered from the Encyclopedia Britannica. It was looking for any hidden links or patterns that weren’t obvious from this great summary of common-sense or background knowledge. The result was, essentially, “Hey, everyone born before 1900 was famous!”