My data scientist colleagues all get excited about the science of the analytical process. The goal is competing – sometimes with themselves! – to achieve the best model they can get. The process looks like this: Gather the best data set one can get that best fit the business problem. Often, this involves merging dozens of pre-existing data sets, making the process take weeks … if not months and, in some cases, years. Use this data to create the first model. But my data scientist friends are never satisfied. Instead, they are constantly trying to improve the model using machine learning algorithms that no business person understands.