Data Transformation Mapping for Legacy ERP Integration

Abstract

Data transformation mapping for legacy ERP integration is important because older enterprise resource planning systems often store business data in formats that do not directly match modern databases, applications, and reporting platforms. Transformation mapping helps convert legacy ERP fields, codes, tables, and business rules into standardized target structures for finance, procurement, inventory, sales, and human resource processes. Existing literature highlights source-to-target mapping, data type conversion, code translation, schema alignment, validation rules, cleansing logic, and migration testing as major practices in ERP integration. However, many organizations still face challenges such as undocumented legacy fields, inconsistent master data, duplicate records, incompatible formats, missing relationships, and weak validation during transformation. This research is important because inaccurate transformation mapping can cause data loss, failed integration, incorrect reports, and disruption of enterprise operations. This article discusses data transformation mapping for legacy ERP integration, focusing on source analysis, mapping rule design, business-rule conversion, data standardization, validation checks, exception handling, and target-system loading. The study concludes that effective transformation mapping improves data accuracy, reduces integration risk, strengthens ERP modernization, and supports reliable enterprise information management.