Everyone agrees AI needs context. Almost no one agrees on what that means.
Metadata maps your data. Knowledge graphs add domain structure. Semantic layers compute the right answer. And none of them, alone, are enough.
This Data Science Connect panel brings together leaders across the context stack to cut through the noise and examine what production-grade, context-aware AI actually requires.
What This Session Covers
- How metadata, semantic layers, and knowledge graphs each contribute, and where they fall short on their own
- Why AI hallucinations are often a compute problem, not just a retrieval problem
- The role of RAG, long-term memory, and orchestration in dynamic, evolving AI systems
- Governance and guardrails for AI that needs to get the number right, every time
- What the next 18 months of semantic infrastructure looks like, including the emerging standards war
What You’ll Walk Away With
- A clear-eyed view of the context stack: Understand the real differences between metadata, semantic, and knowledge graph approaches, and when each one matters.
- A framework for 100% accurate AI outputs: Why getting enterprise data “mostly right” isn’t good enough, and what architecture closes the gap.
- Practical guidance on governance at scale: How to deploy context-aware AI without sacrificing auditability, access control, or trust.
AI doesn’t hallucinate because the model is weak. It hallucinations because the context underneath it is incomplete.
