What is a Context Layer?

A context layer is described as a governed architectural tier that sits between enterprise data sources and the human or AI tools that use them, supplying verified business definitions, relationships, policies, and auditability rather than duplicating underlying data. It combines structure, meaning, and governance by cataloging data assets and valid joins, resolving identities and business rules across systems, and enforcing access controls, certification, and traceability at query time. Unlike a semantic layer, which primarily standardizes metrics and dimensions within analytics environments, a context layer also addresses cross-system entity matching, non-metric rules, steward approval, and context retrieval tailored to individual AI requests. The proposed workflow collects metadata from catalogs, pipelines, BI tools, and query logs; has people certify it; packages only relevant information for a question; and supplies that context before an AI agent reasons, reducing reliance on raw schemas and potentially unreliable inferences. The piece argues that organizations can adopt this model incrementally through governed data products and federated access to existing data sources, with Starburst presented as one platform supporting this approach.

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