Paper on AuDQ Auditable Data Quality Agent Rule Discovery Framework Released
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Paper authors including Jacopo Biggiogera have released the AuDQ auditable data quality agent rule discovery framework. The paper proposes an ontology-driven, warehouse-native data quality layer design vision for high-risk tabular data pipelines, treating data quality rules as first-class artifacts with support for versioning, governance text traceability, and immutable execution trajectory replay. The framework's agent rule discovery core includes both fully autonomous and guided rule acquisition modes, paired with a human-machine collaborative review interface. Preliminary validation experiments have been completed on real datasets from the finance and retail industries.
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- arXiv q-finAuDQ: enabling auditable data quality through agentic rule discovery
AuDQ, an auditable data quality layer for high-risk tabular data pipelines, proposes an ontology-driven, warehouse-native design vision that treats data quality rules as first-class artifacts, supporting version control, governance text traceability, and immutable execution trajectory replay. Its agentic rule discovery core includes both fully autonomous and guided rule acquisition modes, along with human-machine collaborative review interfaces, and has completed preliminary validation experiments on real-world datasets from the finance and retail industries.
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