Abstract: Enterprise Resource Planning (ERP) systems offer significant benefits, but data migration poses a major challenge during implementation. Converting legacy data accurately into the ERP environment is critical, as data quality issues can disrupt operations, financial reporting, and decision-making. This paper provides a comprehensive analysis of ERP data conversion strategies and best practices to help organizations plan and execute successful migration projects.
The research evaluates the pros and cons of prevalent conversion methodologies, including Big Bang, phased and parallel approaches. While Big Bang migration appears faster and cheaper, it is highly risky for most organizations. Phased conversion is widely seen as the optimal approach, especially for larger firms with complex legacy landscapes. Incrementally migrating data by module or location constrains the scope of potential issues. Selective parallel runs of old and new systems provide a valuable safety net for critical data.
Automated conversion tools are also essential to handle today's data volumes efficiently. However, algorithms must be combined with human oversight and robust testing to validate conversion logic. The most successful migration initiatives invest heavily upfront in data profiling, cleansing, mapping, and governance. Proactive planning avoids disruptive legacy data issues from cascading into the ERP system.
Ultimately, the right conversion approach aligns with the overall ERP strategy and balances transformation goals with risk mitigation. By prioritizing data quality, organizations are better positioned to realize the full potential of their ERP investments. The findings provide a valuable framework for managers to assess conversion trade-offs and make informed decisions in planning ERP data migration projects.
Keywords: ERP, data conversion, data migration, legacy systems, phased implementation, automation
Cite:
Tirumala Rao Chimpiri, "Ensuring Data Quality in ERP Implementations: Key Conversion Considerations", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13340.
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DOI:
10.17148/IJARCCE.2024.13340
