RISK-AWARE DATA MIGRATION ARCHITECTURE FOR LARGE-SCALE ENTERPRISE RESOURCE PLANNING MODERNIZATION

Abstract

Large-scale Enterprise Resource Planning modernization has become a strategic requirement for organizations seeking to replace fragmented legacy applications, aging mainframe environments, isolated databases, inflexible business processes, and tightly coupled enterprise platforms with scalable cloud-enabled and service-oriented digital ecosystems. However, ERP modernization programs frequently encounter substantial migration risks arising from inconsistent master data, duplicate records, undocumented dependencies, schema incompatibility, incomplete historical information, data-quality defects, regulatory constraints, integration failures, security exposure, business downtime, reconciliation errors, and uncontrolled cutover activities. This paper proposes a risk-aware data migration architecture for large-scale Enterprise Resource Planning modernization. The proposed methodology integrates legacy landscape discovery, business data classification, migration dependency mapping, data-quality profiling, risk identification, risk prioritization, canonical data modeling, transformation governance, secure extraction, staging-zone protection, validation, reconciliation, phased migration, rollback readiness, continuous observability, and postmigration assurance. The architecture establishes a migration control plane that maintains end-toend visibility across source systems, transformation pipelines, target ERP modules, interfaces, business owners, migration waves, and risk states. Data entities are evaluated according to business criticality, regulatory sensitivity, quality condition, dependency complexity, migration volume, transformation intensity, and operational impact. High-risk data objects receive stronger validation, approval, reconciliation, and rollback controls, whereas lower-risk objects follow streamlined but governed migration paths. The framework further introduces risk-aware migration waves to reduce uncontrolled big-bang cutovers and supports automated evidence collection for auditability. A representative experimental evaluation demonstrates improvements in migration accuracy, reconciliation success, data-quality compliance, dependency visibility, defect detection, cutover predictability, rollback readiness, and post-migration stability compared with conventional migration practices. The findings establish that risk-aware data migration provides a practical foundation for reliable ERP modernization by combining technical transformation, business governance, security, observability, and continuous assurance.