Curating Intelligence—Metadata, Reference Data, and Graphs as the Brain of AI

If plumbing moves data, curation gives it meaning. This is where data becomes intelligence.

At AMD, we see curation as the inflection point where AI systems transition from reactive tools to reasoning partners.

Why Curation Matters

Security is a pre-requisite for building the context layer. Raw data, even when clean and stored securely, lacks context. Autonomous AI requires understanding relationships—between systems, events, users, and outcomes.

Metadata as the Control Plane

Metadata provides the semantic layer, describes what data represents, how it should be used, and why it matters. For AI systems, metadata provides the map needed to navigate complex enterprise environments.

This enables faster, more accurate retrieval, reduced ambiguity in queries, and consistent interpretation across domains.

Reference Data: Aligning with Enterprise

Reference data standardizes entities such as services, locations, environments, and ownership. This alignment allows infrastructure, application, and business data to converge into a shared operational truth.

Knowledge Graphs and Context Grounding

Knowledge graphs model relationships dynamically. They allow AI systems to traverse context, connect signals, and reason across domains without manual redefinition.

This is essential for multi-hop reasoning, explainable AI outputs, and adaptive decision-making.

Takeaway: Curation transforms data platforms into something meaningful—intelligent systems capable of reasoning, not just reporting.