Data Governance in the Age of AI

AI systems depend fundamentally on data, yet most data governance programs were designed for analytics and reporting—not AI. This course explores how data governance must evolve to ensure trustworthy, reliable, and compliant AI outcomes. Examine real-world AI incidents through a data governance lens, identifying where failures in data collection, preparation, and use introduced risk. Learn how data governance responsibilities map across the AI lifecycle—from training data to AI-generated outputs—and how governance artifacts like the business glossary, data catalog, and ML metadata store support AI data. Discover how to extend traditional data governance programs for AI through new governed assets, quality dimensions, policies, roles, and controls—while clearly distinguishing data governance from AI governance.

Learning objectives
Explain how AI systems amplify data governance risks and dependencies across the data lifecycle.
Distinguish data governance responsibilities from AI governance responsibilities in AI initiatives.
Identify data governance failures that contribute to AI incidents using real-world examples.
Determine where data governance applies across the AI lifecycle, including training, feature, input, and output data.
Create data governance artifacts needed to make a data governance program AI-ready.