Evaluating Data Science Outputs More Formally

This chapter describes teaching colleagues some basics and provides a starter set of questions for decision‐makers. Decision‐makers evaluate data scientists and their work every day, even if only informally. The information quality (InfoQ) framework can be used to assist in the design of a data science project, as a midproject assessment, and as a postmortem to sort out lessons learned. InfoQ is determined by eight dimensions that can be assessed individually in the context of the specific problem and goal. These dimensions include data resolution, data structure, data integration, temporal relevance, generalizability, chronology of data and goal, operationalization, and communication. A hands‐on InfoQ workshop uses InfoQ to help an entire team understand the importance of clear goals and what it takes to achieve information quality with respect to those goals. It combines individual work, team discussions, and group presentations, using the InfoQ framework.