Data Reconciliation for Operational Systems and Analytical Warehouses with Rule Mining

Abstract

Operational systems and analytical warehouses must remain closely aligned because enterprise reporting, dashboards, compliance outputs, and decisionsupport models depend on consistent source-to-target data movement. However, mismatches can occur through delayed ingestion, duplicate records, transformation-rule errors, reference-data changes, aggregation differences, schema drift, and warehouse loading defects. This article presents a scalable data reconciliation framework that uses rule mining to compare operational records with analytical warehouse outputs. The proposed framework combines source-warehouse mapping, transformationaware validation, mismatchpattern mining, rule confidence scoring, exception clustering, and reconciliation reporting. The results show that reconciliation coverage, mismatch detection accuracy, and rule confidence remain stable across increasing data volume levels. The framework also improves exception reduction, warehouse consistency, and reconciliation time as rule mining maturity advances from manual checks to automated exception prioritization. Overall, the article demonstrates that rule mining can convert reconciliation from a manual exception-handling task into a repeatable, scalable, and evidence-driven data engineering process.