Header-driven framework assesses data quality using 120,000 header columns
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An explainable framework can now run semantic column annotation and data quality validation across 120,000 heterogeneous columns using only metadata headers, mapping them to 39 standardized types without needing to parse or ingest any raw cell values. For engineers shipping LLM agents or RAG pipelines over massive tabular datasets, this enables extremely fast, privacy-compliant schema classification and source-quality filtering before raw data is ever sent to an LLM context window.
A new framework achieves Column Type Annotation and Data Quality Assessment for around 120,000 header columns using only metadata, enabling large-scale semantic table interpretation without requiring cell values, which matters for production Knowledge Graph preparation and validation.