Hype around data challenges gave the false impression that the data scientist and the predictive score are the main drivers of the process. Even industrial processes (e.g., CRISP-DM and Dataiku) that have been around since the nineties usually put the data scientist in the center and deployment at the end of the process. While they are not wrong, they are mostly irrelevant. Building and optimizing the predictor is easy. What is hard is finding the business problem and the KPI that it will improve, hunting and transforming the data into digestible instances, defining the steps of the workflow, putting it into production, and organizing the model maintenance and regular update.