Data Governance Goes Agentic: Insights from DGIQ 2026

In a presentation at the DGIQ conference, the focus was on integrating agents and context into data governance to meet the evolving demands of AI in the enterprise landscape. Traditional governance practices, centered on documentation and metadata, are becoming inadequate as AI raises expectations for real-time insights into data origins, transformations, and system dependencies. The presentation highlighted that effective governance requires a deeper understanding of enterprise context beyond surface-level metadata, involving source code analysis, column-level lineage, and operational systems to provide a comprehensive view of data flow. This comprehensive context enables governance teams to predict downstream impacts, detect hidden dependencies, and improve trust in AI systems. The discussion emphasized the importance of integrating governance directly into engineering workflows and maintaining speed with AI developments, suggesting that organizations will succeed with AI by understanding their data comprehensively rather than relying solely on large models.