Why Data Quality Determines Whether Your ERP Migration Succeeds
Most ERP projects are derailed not by the technology, but by the product data that gets loaded into it
Healthcare organizations invest millions in ERP migrations. New platforms, redesigned workflows, extensive training programs, months of project planning. And then go-live arrives, and the system works exactly as configured, but the data inside it does not. Purchase orders fail to match invoices. Barcode scanning breaks at the point of use. Clinicians cannot find the products they need under familiar names. Finance cannot reconcile spend against contracts.
These are not technology failures. They are data failures. And they are far more common than most project teams anticipate. Research from Gartner found that poor data quality is the primary cause of ERP implementation underperformance, with organizations reporting that data issues account for a disproportionate share of post-go-live remediation costs. In healthcare supply chains, where item masters can contain hundreds of thousands of records with complex clinical, regulatory, and contractual attributes, the risk is especially acute.
Supply chain data quality is not a peripheral detail in ERP migrations. It is the factor that most often determines whether a project delivers a usable system or a new platform weighed down by old problems.
The Pattern: Health systems routinely underestimate item master complexity going into an ERP migration, then spend the first 12 to 18 months post-go-live correcting data problems that were present before the project started and carried forward into the new system.
What Gets Migrated When Data Is Not Clean
The default approach to ERP migration is to extract data from the legacy system, map it to the new data model, and load it into the new platform. That process works well when the source data is clean, standardized, and complete. When it is not, migration becomes a mechanism for institutionalizing existing problems in a new environment.
Duplicate item records carry forward and multiply as mapping rules create additional variants. Inconsistent manufacturer names that caused contract matching failures in the old system cause the same failures in the new one. Missing GTINs that prevented reliable scanning continue to prevent it. Outdated UNSPSC classifications that distorted spend analytics in the legacy environment distort them in the new one as well. The platform changes. The underlying data problems do not.
A study published in the Journal of the American Medical Informatics Association found that data quality problems in clinical and supply systems are significantly more expensive to correct after system go-live than before, due to the complexity of remediation in a live operational environment. The cost of cleaning data before migration is a fraction of the cost of cleaning it after.
By the Numbers: According to IBM, the cost of fixing a data error after it has been loaded into production is estimated at 10 times the cost of fixing it at the point of entry. In a live ERP environment with thousands of connected transactions, the multiplier grows further.
The Four Places Data Quality Determines Migration Outcomes
1. Migration Risk and Timeline
Data quality problems are one of the most reliable predictors of ERP project delays. Testing cycles lengthen when item records fail validation. Integration testing surfaces conflicts between the item master and connected systems that require manual resolution. Data remediation work that was not scoped into the project competes for time and attention with configuration and training.
The Healthcare Information and Management Systems Society (HIMSS) has documented that ERP and clinical system implementations in healthcare consistently run over schedule, with data readiness identified as a leading contributing factor. Project teams that invest in item master quality before migration begins have more predictable timelines because they are not discovering and resolving data problems mid-project.
2. Testing Accuracy and Confidence
User acceptance testing is only as meaningful as the data it runs on. If test scripts are executed against records with missing attributes, incorrect classifications, or mismatched vendor information, the testing process validates system behavior against data that does not reflect real operational conditions. Problems that should surface in testing carry forward to go-live instead.
Clean item master data makes testing faster and more reliable. When records are complete and standardized, test cases can be designed against realistic scenarios, integrations can be validated against accurate product attributes, and the team can go into go-live with genuine confidence that the system has been tested against the data it will actually run on.
3. User Adoption and Frontline Experience
User adoption is where ERP migrations most visibly succeed or fail. And nothing erodes adoption faster than a system that does not work the way frontline staff expect it to. Nurses who cannot find products under familiar names revert to workarounds. Procurement staff who encounter scanning failures at receiving abandon the new process and return to manual entry. Supply chain managers who cannot trust the spend data the new system produces stop using its analytics.
These behaviors are not resistance to change. They are rational responses to a system that is not performing reliably. Poor system performance in the early post-go-live period is one of the strongest predictors of long-term adoption failure. Accurate product data, complete GTINs, and standardized item descriptions are what make the system perform reliably from day one.
4. Operational Stability After Go-Live
The weeks immediately following go-live are when operational risk is highest. Purchasing teams are learning new workflows. Inventory management processes are being validated in practice for the first time. Clinical supply teams are building confidence in a new system. Any significant data problem that surfaces during this period creates disruption at the worst possible moment.
Common post-go-live data failures include scanning breakdowns driven by missing or incorrect GTINs, contract matching failures caused by vendor name inconsistencies, reimbursement delays from invalid HCPCS codes, and inventory inaccuracies that trigger emergency purchasing. Each one is correctable. But correcting them in a live operational environment, while the organization is still learning the new system, is costly in time, labor, and confidence.
From a Recent Engagement: Symmetric delivered more than 98,000 GTIN and packaging updates in 60 days for a health system ERP transition, stabilizing point-of-use scanning and protecting go-live readiness across all active supply locations.
What Clean Data Actually Requires Before Migration
Preparing item master data for an ERP migration is more involved than running a deduplication script or filling in missing fields. It requires a structured approach that addresses product identity, attribute completeness, classification accuracy, and vendor alignment before migration begins.
The elements that matter most are:
Verified product identity. Each item record connected to a confirmed manufacturer, catalog number, and GTIN so the new system has a reliable foundation for scanning, traceability, and contract matching.
Complete and current classifications. UNSPSC codes validated against the current release, HCPCS codes checked against the latest CMS standard, and regulatory attributes current as of migration date.
Resolved duplicates. Duplicate records identified and consolidated before migration so they do not carry forward and compound in the new environment.
Normalized manufacturer and vendor records. Supplier names standardized and mapped to active contracts and GPO agreements so contract matching works correctly from day one.
Clinical and operational attributes. Sterility, implantable status, packaging hierarchy, and country of origin populated for items where those attributes affect clinical decisions or regulatory compliance.
Reaching this standard before migration does not eliminate all post-go-live work. But it changes the nature of that work from emergency remediation of critical failures to routine refinement of a system that is already performing well.
Why Data Governance Must Be Part of the Migration Plan
One of the most common mistakes in ERP migrations is treating data quality as a pre-migration project rather than an ongoing operational discipline. Item master data cleaned before go-live will begin to decay immediately if there is no mechanism to keep it current. New products get added without complete attributes. Manufacturer updates do not get reflected in item records. Classifications drift as product lines change.
Building data governance into the migration plan means establishing who owns data quality by category, what validation standards new records must meet before entering the system, and how updates from manufacturers and regulatory bodies get incorporated on an ongoing basis. The AHRMM Cost, Quality, and Outcomes (CQO) Movement identifies continuous data governance as a foundational requirement for supply chain performance, and the ERP go-live moment is the best opportunity most health systems will have to establish those practices from a clean starting point.
Health systems that build governance in from the start protect the investment they made in pre-migration data quality. Those that treat the cleanup as a one-time exercise watch that investment erode within the first year.
Key Principle: An ERP migration is not just a technology project. It is the most natural moment a health system will ever have to establish a new standard for how product data gets managed. The organizations that recognize this use the migration to build a data foundation that outlasts the implementation.
How Symmetric Health Solutions Supports ERP Migrations
Symmetric Health Solutions provides the product intelligence and master data foundation hospitals need so ERP migrations succeed on the strength of their data. The platform enriches item master records with verified manufacturer data, FDA registration information, and GS1 global product identifiers, ensuring GTINs, classifications, clinical attributes, and vendor mappings are complete and current before migration begins.
In practice, that means resolving duplicate records at scale, validating and backfilling missing GTINs and packaging strings, standardizing manufacturer and supplier names against active contract data, and delivering enriched item records on the timeline that migration projects require. The 98,000-update engagement described earlier is one example of what that looks like at full deployment speed.
Symmetric also supports the post-migration environment through continuous enrichment, ensuring that item master data stays current as the market evolves rather than decaying back toward the problems the migration was meant to leave behind. As a member of the Healthcare Industry Resilience Collaborative (HIRC), Symmetric contributes to shared normalization standards across more than 1,200 hospitals, raising the data quality baseline for the industry rather than just individual health systems.
The goal is straightforward. When an ERP goes live, the system should work the way it was designed to, because the data inside it is accurate, complete, and ready. Everything the platform was built to deliver, faster purchasing cycles, reliable scanning, cleaner analytics, stronger contract performance, depends on that foundation being right from the start.
