I think that this is more a criticism of the way that machine learning is commonly done, rather than a valid criticism of machine learning per se. I have certa

I think that this is more a criticism of the way that machine learning is commonly done, rather than a valid criticism of machine learning per se. I have certainly (sometimes) been able to build insights using (some) machine learning approaches, but it depends a lot on having the time, the resources, and the right organizational culture in place. Unfortunately, a lot of people approach machine learning algorithms expecting a magic bullet; attracted by the hyperbole, so the self-deception starts even before a line of code is written. Doubly-unfortunately, these people are usually the ones offering the highest salaries for "data scientists" and "big data engineers". Like most work of consequence, the mundane outweighs the exciting by a ratio of at least 10:1. Obtaining and cleaning the data, understanding what is missing, the sampling biases that are present, testing and training the algorithms, all of these things are deeply important and seemingly mundane; yet it is these very activities that help to build deep insight and experience. Perhaps we should pay our data scientists a bit (a lot) less, and we would get fewer flashy presentations and more substance.