Key to success in an A.I. or IoT or data strategy is data scientist productivity. Data scientist productivity is defined as the volume of business-critical results driven through data science. The difference between successful data-driven companies and not so successful ones is the productivity and throughput of the data science team. Increasing data scientist productivity and throughput leads to positive side effects, including standardization of data science processes, tooling and data science methodology, as well as an increase in the availability of case studies and foundational data science that can trigger and speed up other data science efforts.