The author is right in one sense: there's a lot of black magic around predictive analytics, machine learning, modeling, etc. However, some of these techniques

The author is right in one sense: there's a lot of black magic around predictive analytics, machine learning, modeling, etc. However, some of these techniques yield a great deal of paydirt. Look at the spam, or rather the lack thereof, in your inbox. Thank the enormous success of predictive analytics for eliminating a great deal of it. As he mentioned, weather forecasting is actually pretty good, all things considered. Kvetching about unexpected rainy days demonstrates a misunderstanding of how we should evaluate the success of weather forecasting techniques. As useful as these techniques are, there is a great deal of ignorance in where they are useful and where they aren't. People selling things and those with various political agendas use that ignorance to foist "modeling" on an unsuspecting public. No complex modeling used for predictive analytics is useful without a great deal of validation. You come up with a model and set of techniques, you feed in historical data, you compare the outputs of your predictions with subsequent historical data, you adjust your techniques to produce more accurate predictions, wash, rinse, repeat. There's no real getting around the need to perform this cycle ad nauseum if you want results that aren't just noise.