Data governance framework: key components and best practices

This post details a framework for assessing and improving data science maturity through a governance lens. It defines stages of maturity and provides diagnostic tools to assess current standing across dimensions like prediction access controls, metric definition consistency, AI output inspectability, and decision coverage. It also outlines a stage-by-stage roadmap for implementing governance, starting with documentation and endorsement, progressing to automated controls and continuous monitoring.