Automated Data Model Anomaly Detection for BI Integrity

The system segments the data model analysis process into distinct functional modules: data retrieval module, element identification module (fact/dimension classification), table relationship determination module, anomaly detection module, and report generation module. Each module handles a specific aspect of the analysis, making the complex automated analysis process manageable and maintainable while preserving high productivity.

The system applies local quality by implementing specialized detection algorithms for different types of anomalies specific to data model characteristics: ragged hierarchy detection examines parent-child relationship structures, many-to-many relationship detection analyzes table connection patterns, and double counting detection reviews aggregation logic. Each anomaly type receives targeted analysis appropriate to its nature, achieving high detection precision without requiring a monolithic complex system.

The system performs preliminary actions by automatically generating the data model structure, identifying all elements and their relationships, and establishing the factual framework before conducting the actual anomaly detection. This preliminary structuring of the data model enables subsequent anomaly detection to proceed efficiently with focused algorithms, ensuring comprehensive reliability checks without excessive time consumption.

The system implements feedback mechanisms where the automated analysis process continuously refines its anomaly detection based on the generated data model structure and identified relationships. The system provides feedback loops that verify detection results against the established data model, allowing automated operation to maintain high precision by continuously validating findings against the factual data structure rather than operating in isolation.