DATA QUALITY AND INTEGRITY MANAGEMENT FRAMEWORK FOR ENTERPRISE ERP MIGRATION PROJECTS

Abstract

Enterprise Resource Planning migration projects are critical components of organizational digital transformation because they enable enterprises to replace fragmented legacy systems, modernize business processes, consolidate operational information, adopt cloud platforms, improve analytical capabilities, and establish integrated enterprise architectures. However, ERP migration initiatives frequently experience substantial difficulties due to incomplete records, duplicate entities, inconsistent master data, invalid formats, obsolete values, broken relationships, semantic mismatches, transformation errors, unauthorized modifications, missing lineage, reconciliation failures, and weak governance. Poor-quality or corrupted data can cause incorrect financial reporting, disrupted procurement, inaccurate inventory positions, payroll errors, customerservice failures, compliance violations, and reduced confidence in the migrated platform. This paper proposes a comprehensive Data Quality and Integrity Management Framework for Enterprise ERP Migration Projects. The framework integrates legacy data discovery, source-system profiling, business-critical data classification, metadata management, qualityrule definition, duplicate detection, master-data harmonization, schema mapping, semantic transformation, referential-integrity validation, migration-wave planning, secure extraction, staging-zone controls, transformation monitoring, reconciliation, lineage tracking, anomaly detection, role-based authorization, API governance, auditability, migration Digital Twins, rollback readiness, post-migration monitoring, and continuous quality improvement. The proposed methodology establishes a multistage migration lifecycle in which data is assessed before extraction, validated during transformation, reconciled after loading, and continuously monitored after production cutover. Quality dimensions including completeness, accuracy, consistency, validity, uniqueness, timeliness, conformity, and referential integrity are evaluated according to business-specific requirements. A migration Digital Twin maintains synchronized representations of source datasets, transformation states, target structures, quality exceptions, lineage relationships, and migration-wave progress. Machine-learningassisted anomaly analytics identify unusual records, transformation deviations, duplicate patterns, and integrity risks that may not be captured through static rules. OpenAPI-oriented service contracts support interoperability among migration tools, ERP applications, validation services, quality dashboards, and governance platforms, while secure lifecycle management protects migration credentials and sensitive enterprise data. A representative analytical evaluation compares conventional extracttransform-load migration with the proposed framework across data completeness, duplicate reduction, transformation accuracy, reconciliation success, referential-integrity violations, migration defects, exceptionresolution time, rollback readiness, cutover stability, and post-migration business incidents. The results indicate substantial improvements in migrated-data reliability, traceability, operational continuity, and governance. The study concludes that successful ERP modernization requires data quality and integrity to be managed as continuous lifecycle capabilities rather than as isolated cleansing activities performed immediately before cutover.