Enterprise Data Governance: A Complete Modern Framework

This article presents a comprehensive framework for enterprise data governance, covering its core components, five pillars, and critical role in the AI era.

• •Data governance answers three key questions: who owns the data, who can access it, and how to keep it accurate, secure, and compliant over time
• •Core components include data ownership/stewardship, metadata management, data quality management, access controls, data lineage, and data discovery/classification
• •Poor data quality costs organizations an average of $12.9 million per year; data teams spend ~80% of their time on data discovery, preparation, and protection
• •The five pillars are Data Quality, Data Security, Data Management, Data Compliance, and Data Stewardship
• •AI governance has become a board-level concern, and organizations with the highest AI returns maintain comprehensive governance frameworks covering every stage of model development