Data Quality for Unstructured Data: Why Traditional Rules Don't Work on Text, Logs, and Documents

Traditional data quality rules such as range, null, format, and uniqueness checks often fail for unstructured text, logs, and documents because these sources lack fixed schemas and can pass structural tests despite containing incorrect or incomplete information. Effective unstructured-data quality practices fall into three categories: content and meaning checks, which use NLP, embeddings, sentiment analysis, and LLM evaluation to assess relevance, consistency, and accuracy; lineage and provenance checks, which record a document’s origin, custody, transformations, tool versions, and human or AI authorship; and extraction and processing fidelity checks, which measure OCR confidence, entity completeness, and chunking integrity. The text emphasizes that parsing and chunking errors can compromise all downstream retrieval and AI results, citing benchmark evidence that chunking strategies can substantially affect accuracy. It recommends applying conventional checks to any structured fields extracted from documents while using specialized validation for the original content, involving both technical teams and subject-matter experts to define quality standards.

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