SMART DATA MIGRATION FRAMEWORK FOR SECURE AND EFFICIENT ERP MODERNIZATION

Abstract

Enterprise Resource Planning systems form the operational backbone of contemporary organizations by integrating finance, procurement, inventory, manufacturing, human resources, customer management, supply chain operations, and enterprise reporting. However, many organizations continue to depend on aging ERP platforms characterized by fragmented databases, obsolete interfaces, duplicated records, inconsistent master data, tightly coupled customizations, limited scalability, weak interoperability, and increasing cybersecurity risks. ERP modernization requires the reliable transfer of large volumes of business-critical information from legacy platforms toward modern cloud, hybrid, service-oriented, or modular enterprise environments. Conventional migration approaches frequently emphasize bulk extraction and loading while providing insufficient support for semantic consistency, data quality, lineage, security, reconciliation, rollback, and business continuity. This paper proposes a smart data migration framework for secure and efficient ERP modernization. The proposed methodology integrates migration readiness assessment, source-system discovery, metadata extraction, automated data profiling, dependency analysis, business-rule identification, intelligent data classification, quality scoring, schema mapping, transformation governance, sensitive-data protection, API-based integration, staged migration, change data capture, reconciliation, lineage tracking, anomaly detection, rollback management, and postmigration observability. Data entities are classified according to business criticality, sensitivity, quality, dependency, transaction frequency, and migration complexity so that appropriate strategies can be selected for master, transactional, historical, reference, configuration, and unstructured information. Machine learningassisted anomaly detection identifies suspicious transformations and unexpected distribution changes, while rule-based validation protects deterministic business constraints. Security mechanisms incorporate controlled identity, encryption, secrets management, least-privilege access, immutable audit evidence, and integrity verification across migration pipelines. Representative experimental evaluation across a simulated multi-module ERP modernization environment demonstrates improvements in migration accuracy, duplicate reduction, reconciliation success, data quality, processing efficiency, security traceability, and rollback readiness compared with conventional extracttransform-load migration. The results further indicate that phased migration with continuous validation provides stronger reliability than single-event bulk transition. The proposed framework supports integration with governed APIs, cloud-native orchestration, MLOps services, Industrial Internet of Things platforms, Digital Twins, and enterprise analytics. The study demonstrates that smart, security-aware, and metadata-driven migration can provide a practical foundation for reliable ERP modernization while preserving operational continuity and enterprise trust.