Ensuring Asset Data Integrity: Best Practices for EAM Migration with Quality Checks

A new EAM system migration is never just an IT project; it is a business transformation. The move offers an opportunity to clean up years of inconsistent or incomplete asset data. However, without proper planning, those same issues simply migrate into the new platform. Industry research shows that data-related issues contribute to over 70% of migration failures, underscoring the need for a structured quality assurance process before, during, and after the transition.

To start strong, organizations should conduct a full data audit well before any migration activities begin. This audit should go beyond simple error detection. It must verify that all critical metadata, such as manufacturer and maintenance history, is complete and accurate. Missing or incorrect metadata can slow down maintenance work and create inefficiencies that persist long after go-live. A comprehensive audit acts as the foundation, ensuring that only reliable information moves forward.

Building Quality Checks Into the Migration Process

Once the audit is complete, the next step is embedding automated quality checks directly into the migration workflow. Studies indicate that automation can reduce validation workloads by over 50%, helping teams focus on resolving the most complex issues rather than repetitive data cleaning tasks. These automated processes can ensure data conforms to established taxonomies before it enters the new system.

The use of standardization frameworks such as ISO 8000 is equally important, as they define consistent rules for asset naming and classification. Applying these frameworks during migration helps maintain uniformity across datasets, enabling better analytics and easier integration with other systems. Consistency is key not only for the migration itself, but also for ongoing data governance in the months and years that follow.

Maintaining Data Integrity After Go-Live

Many migrations falter in the months after implementation because data governance fades into the background. To avoid this, organizations should commit to continuous data monitoring. This means scheduling regular audits, tracking data quality metrics, and using automated dashboards to flag anomalies in real time. A proactive approach can prevent small errors from compounding into larger problems that disrupt operations.

Benchmarking against industry best practices (ISO 55000) also helps organizations understand how their asset data quality measures up. This comparison can reveal gaps that may not be immediately apparent from internal reviews alone. Combined with consistent governance, such benchmarking ensures the EAM continues to operate as a trusted source of asset information long after migration.

Conclusion

An EAM migration is more than a technology switch, it’s a strategic chance to elevate asset data integrity. By investing in early audits and applying standardization frameworks, organizations set the stage for success. Furthermore, committing to continuous monitoring ensures the new system operates at peak potential. Ultimately, quality checks are not just part of the migration, they are the backbone of long-term operational excellence.