This article outlines the importance of having governed and discoverable data for successful AI projects. It highlights common pitfalls in AI implementation and presents a structured approach to ensure data quality and compliance. A roadmap is provided for creating a reliable data stack that supports effective AI systems.

This article discusses the evolution of data governance from a rigid, compliance-focused approach to a more dynamic, context-driven model. It argues that as AI systems become more autonomous, organizations need to shift from controlling data to ensuring accountability and intentionality in how data is used. The author emphasizes the importance of negotiating meaning and maintaining oversight in increasingly complex socio-technical environments.