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AI Data Analysis Requires Clear Warehouse Structure and Definitions

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A semantic or metrics layer should be placed between a data warehouse and an AI system to ensure reliable analysis. This layer requires clear model names, documented data grain and keys, and curated views instead of raw tables. For Retrieval-Augmented Generation (RAG), metadata must be embedded with data chunks to provide context. Data quality concerning uniqueness, freshness, and unambiguous definitions is critical, as AI will confidently repeat any underlying errors.

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