Why Data Governance must work with the Semantic Layer

Data governance was built for compliance: document, classify, control. But when the goal is AI at scale, documentation isn't enough. You need business logic that machines can actually use. 

A semantic layer does exactly that. It defines metrics once and makes them accessible to every consumer — dashboards, APIs, AI agents — without duplication or drift. Yet most governance programs still treat the semantic layer as someone else’s problem. 

In this live session with Strategy, Charlotte Ledoux and HF Chadeisson will show what governance needs to become to operate effectively alongside a semantic layer: new roles, new ownership models, and new processes for governing meaning, not just data. 

What you’ll learn: 

• Why compliance-first governance breaks down when AI agents become data consumers
• How a semantic layer defines and distributes business logic at scale — once, for everyone
• What new roles and ownership models are needed to govern meaning, not just metadata
• How to build processes that keep metrics consistent across dashboards, APIs, and AI
• Where governance programs are headed as organizations move toward agentic AI workflows

What we’ll cover: 

• The real limitations of documentation-only governance in AI-first organizations
• A deep dive into the semantic layer — what it is, how it works, and why governance must own it
• New governance frameworks for metric ownership, versioning, and trust
• Live discussion and Q&A with Charlotte Ledoux and HF Chadeisson

Don't miss this opportunity to see how forward-thinking data teams are closing the gap between governance policy and the business logic that actually powers AI. Register now to take the first step toward a governance model built for the intelligence era.