INTELLIGENT LEGACY-TO-CLOUD ERP TRANSFORMATION USING AUTOMATED DATA MIGRATION AND VALIDATION

Abstract

Enterprise Resource Planning systems form the operational foundation of many organizations by integrating finance, procurement, inventory, manufacturing, human resources, customer operations, supply-chain processes, and regulatory reporting. However, numerous enterprises continue to depend on legacy ERP platforms characterized by tightly coupled architectures, obsolete technologies, fragmented databases, customized schemas, manual interfaces, limited scalability, and expensive maintenance. Migrating these environments to cloud-based ERP platforms is complex because decades of operational data may contain inconsistent formats, duplicate entities, missing attributes, obsolete codes, undocumented dependencies, incompatible master-data structures, and business-critical historical records. This paper proposes an intelligent legacy-to-cloud ERP transformation framework using automated data migration and validation. The proposed methodology integrates legacysystem discovery, metadata extraction, dependency analysis, data profiling, schema mapping, semantic transformation, duplicate detection, anomaly identification, data-quality scoring, automated cleansing, incremental migration, reconciliation, business-rule validation, referential-integrity verification, APIbased integration, secure credential management, cloud deployment, and post-migration monitoring. Machine-learning techniques are incorporated to identify mapping candidates, anomalous records, duplicate entities, and highrisk migration objects. The framework maintains traceability between legacy source records and cloud targets so that transformed data can be audited and reconciled. A staged migration model combines pilot migration, historical backfill, incremental synchronization, controlled cutover, rollback readiness, and continuous validation. Representative evaluation indicates that the proposed framework can improve mapping accuracy, reduce duplicate records, increase validation coverage, decrease migration defects, shorten reconciliation effort, and reduce business disruption compared with predominantly manual migration approaches. The framework provides a practical foundation for ERP modernization, cloud transformation, intelligent data engineering, automated validation, enterprise integration, and scalable digital operations.