What is a semantic layer? A definition for the AI analytics era
Blog post from Metabase
Semantic layers translate raw data into standardized business definitions for metrics, dimensions, and rules, helping people, BI tools, and AI systems produce consistent answers to questions about concepts such as revenue, active users, and customer churn. While they traditionally required lengthy, centralized upfront modeling, AI and modern analytics tools are making them more iterative: candidate definitions can be inferred from existing queries, expressed in plain language, reviewed by domain experts, version-controlled, and updated quickly. The growing use of AI analytics raises the risk of ambiguous or hallucinated interpretations when definitions are absent, while cited research and benchmarks suggest that strong data curation and curated semantic layers can improve AI accuracy. Current approaches increasingly favor a shared core of governed definitions with flexibility for team-specific views, but organizations still face choices over whether layers should be built upfront or discovered through usage, centralized or distributed, generic or model-specific, and delivered as standalone infrastructure or within BI platforms.
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