It's also important to understand the perceived success of predictive analytics in the context of the problem being solved. For example in predictive analytics

It's also important to understand the perceived success of predictive analytics in the context of the problem being solved. For example in predictive analytics used to determine product distribution at major retailers, the goal isn't to be perfect. The goal is to be a little bit better than what humans (or their previous methods) could do. If you go to Target management and prove that your predictive analytics is 3.3% better at predicting how many blenders they'll need in each of their stores, but that 3.3% netted them 5 million dollars, they'll sit up and listen.