Most ERP projects are judged by how well the system is implemented, timelines met, modules deployed, users trained. But the part that quietly carries the highest risk isn\u2019t the implementation.<\/span><\/p>\n It\u2019s the data migration.<\/span><\/p>\n You can deploy a perfectly configured ERP system and still fail at go-live if the data feeding it is incomplete, inconsistent, or incorrect. And unlike configuration issues, data problems don\u2019t stay contained, they spread across finance, operations, reporting, and compliance.<\/span><\/p>\n That\u2019s why, in many cases, data migration is not just a step in the process. It\u2019s the make-or-break moment.<\/span><\/p>\n The Misconception: \u201cMigration Is Just Transfer\u201d<\/b><\/p>\n Data migration is often treated as a technical task:<\/span><\/p>\n Simple in theory. Risky in reality.<\/span><\/p>\n Because ERP data isn\u2019t just information, it\u2019s the foundation of how the business operates:<\/span><\/p>\n If that foundation is flawed, everything built on top of it is unstable.<\/span><\/p>\n Why Implementation Feels Safer<\/b><\/p>\n ERP implementation is structured:<\/span><\/p>\n Vendors provide:<\/span><\/p>\n There\u2019s a roadmap.<\/span><\/p>\n Data migration, on the other hand, is:<\/span><\/p>\n There is no universal playbook, only patterns and risks.<\/span><\/p>\n Where Data Migration Gets Risky<\/b><\/p>\n Legacy systems often contain:<\/span><\/p>\n When this data is migrated without proper cleansing, the new ERP inherits old problems, at scale.<\/span><\/p>\n Data is rarely isolated.<\/span><\/p>\n For example:<\/span><\/p>\n Breaking or misaligning these relationships during migration leads to:<\/span><\/p>\n Legacy systems and modern ERP platforms often structure data differently.<\/span><\/p>\n This creates challenges in:<\/span><\/p>\n Even small mismatches can result in significant functional issues.<\/span><\/p>\n Large organizations deal with:<\/span><\/p>\n The more data involved, the higher the chance of:<\/span><\/p>\n Testing environments rarely replicate real-world data conditions.<\/span><\/p>\n As a result:<\/span><\/p>\n At that point, fixing them becomes far more difficult.<\/span><\/p>\n Migration is often scheduled toward the end of the project.<\/span><\/p>\n By then:<\/span><\/p>\n This leads to shortcuts, and shortcuts in data migration are costly.<\/span><\/p>\n The Ripple Effect of Bad Data<\/b><\/p>\n When data migration goes wrong, the impact is immediate and widespread:<\/span><\/p>\n Unlike implementation bugs, data issues are harder to isolate and fix. They require correction at the source, and often, re-migration.<\/span><\/p>\n Why These Issues Surface After Go-Live<\/b><\/p>\n Data migration problems don\u2019t always appear immediately.<\/span><\/p>\n They emerge when:<\/span><\/p>\n By then, the system is already live, making corrections more complex and disruptive.<\/span><\/p>\n How to Reduce Migration Risk<\/b><\/p>\n Managing data migration effectively requires treating it as a strategic initiative, not a technical task.<\/span><\/p>\n Don\u2019t wait until implementation is nearly complete.<\/span><\/p>\n Begin:<\/span><\/p>\n \u2026well in advance.<\/span><\/p>\n Migration is an opportunity to improve data quality.<\/span><\/p>\n Remove:<\/span><\/p>\n Clean data leads to a stronger system.<\/span><\/p>\n Assign responsibility for:<\/span><\/p>\n Without ownership, accountability is lost.<\/span><\/p>\n Use:<\/span><\/p>\n Testing should reflect real-world conditions.<\/span><\/p>\n Migration should not be a one-time event.<\/span><\/p>\n\n
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1. Poor Data Quality in Legacy Systems<\/b><\/h3>\n
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1. Start Early<\/b><\/h3>\n
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