A new catalog classifying 35 data error types into missing, incorrect, and redundant categories for tabular data, with definitions and examples to improve data quality management.
Journal of Management Information Systems12(4), 5–33 (1996)
3 Pith papers cite this work. Polarity classification is still indexing.
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A multi-layer ELT data quality framework using LLM semantic tests detected all 16 injected anomalies versus 7 in a manual baseline, with full cross-store agreement in 106 seconds.
The evaluation finds proprietary tools like Informatica and Ataccama offer more comprehensive features and emerging LLM assistance than open-source options like Great Expectations and Deequ, but LLM integration across all tools is limited to rule creation with no direct data validation support.
citing papers explorer
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A Catalog of Data Errors
A new catalog classifying 35 data error types into missing, incorrect, and redundant categories for tabular data, with definitions and examples to improve data quality management.
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A Multi-Layer Testing Framework for Automated Data Quality Assurance in Cloud-Native ELT Pipelines
A multi-layer ELT data quality framework using LLM semantic tests detected all 16 injected anomalies versus 7 in a manual baseline, with full cross-store agreement in 106 seconds.
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Evaluating Data Quality Tools: Measurement Capabilities and LLM Integration
The evaluation finds proprietary tools like Informatica and Ataccama offer more comprehensive features and emerging LLM assistance than open-source options like Great Expectations and Deequ, but LLM integration across all tools is limited to rule creation with no direct data validation support.