Statistical Anomaly Detection Approach for Field Mapping Validation in Enterprise Payroll Data Migration

Abstract

Enterprise payroll system migrations from legacy platforms to modern cloud-based solutions present significant data quality challenges, particularly in field mapping validation. This research proposes a statistical anomaly detection framework specifically designed for automated validation of field mappings during Oracle Cloud Payroll to SAP SuccessFactors migrations. The framework employs distribution-based analysis techniques including Kolmogorov-Smirnov tests, Chi-square tests, and multi-threshold outlier detection mechanisms to identify systematic mapping errors, data type inconsistencies, and business rule violations. Experimental validation using a dataset of 50,000 employee records across 120 fields demonstrates that the proposed approach achieves 95.3% detection accuracy while reducing manual validation time by 42%. The framework successfully identified critical anomalies including decimal precision loss in salary calculations, date format inconsistencies in employment records, and null value propagation in benefit deductions. These results establish statistical anomaly detection as a viable automated quality assurance mechanism for enterprise payroll data migrations, offering substantial improvements over traditional rule-based validation methods.