Data science maturity: stages, assessment, and roadmap

This post details a framework for assessing and improving data science maturity through a governance lens. It defines stages of maturity (Ad Hoc, Managed, Defined, Optimized), provides diagnostic exercises across four dimensions (prediction access controls, metric definition consistency, AI output inspectability, decision coverage), and outlines a stage-by-stage roadmap for implementing governance. The roadmap includes steps like documenting and endorsing data assets, centralizing metric definitions, implementing role-based access controls and workspace-level change policies, building full semantic layers, and establishing observability and auditability. The post emphasizes that trust and verifiability are key measures of maturity, especially with the rise of AI.