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CONCEPT Cited by 1 source

Context coverage as quality metric

A data quality dimension measuring whether a data product carries sufficient documentation and metadata for AI agents (and humans) to reason over it correctly.

Definition

Traditional data quality focuses on completeness (are all expected rows present?) and freshness (how old is the latest record?). Context coverage adds a third axis: does the product carry enough explanation for a consumer — human or agent — to understand what it means, what caveats apply, and how to use it?

Operational form: a badge per data product that shows: - Whether table descriptions are present - How much column-level documentation exists across the product's attached tables

"For AI-ready data, context coverage has become a first-class quality metric." (Source: sources/2026-07-21-databricks-why-rd-data-belongs-in-the-lakehouse-and-why-agents-need-it)

Why it works

The badge makes the gap visible at the point of consumption: "Coverage went up because engineers could see the gap directly in the product surface where the data would be consumed." The metric turns documentation from an after-the-fact cleanup task into something engineers measure and improve during delivery.

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