EAM Data Migration Success: How Data Quality Assessment Can Make or Break Your Project

Gartner reports that more than 83% of data migration projects either fail or run over time and budget, often because data quality issues are not addressed early. In EAM migrations, this risk is amplified as asset data is both highly interconnected and is operationally critical. This means that migrating poor-quality data not only reduces the reliability of the new system, but can also disrupt day-to-day operations and erode trust in the system before it is fully adopted.

Why Data Quality Matters in EAM Migrations

EAM systems depend on accurate data for asset lifecycle planning and cost control. A Forrester survey finds that more than a quarter of data professionals estimate their organization loses over $5 million annually, with 7% reporting losses of $25 million or more due to data quality challenges. EAM data spans complex asset hierarchies, maintenance histories, spare parts inventories, and compliance documentation. Errors during EAM migration can multiply costs if duplicate records, missing maintenance histories, or inconsistent naming conventions are carried into the new platform.

Common Data Quality Pitfalls

Legacy EAM data often contains outdated location codes, duplicated asset IDs, missing technical attributes, and mismatched units of measure. Engineering changes may be documented in CAD systems but never reflected in the EAM, as this leads to gaps between physical assets and their digital records. These issues can cause maintenance delays, incorrect procurement orders, and inaccurate asset reporting in the new system.

The Role of Data Quality Assessment

A data quality assessment systematically evaluates asset data before migration. IBM outlines six key dimensions accuracy, completeness, consistency, timeliness, validity, and uniqueness that define high-quality data. For EAM projects, this means confirming every asset has a valid ID, ensuring all required fields are populated, and standardizing formats to match the target system requirements. Addressing issues before migration prevents flawed information from contaminating the new system. Standardized data improves system usability and supports advanced capabilities such as predictive maintenance and reliability analytics. Additionally, it reduces the risk of post-launch rework and ensures the migration delivers a measurable return on investment.

Conclusion

An EAM migration is more than a technical transition; it is an opportunity to strengthen asset data quality. A thorough data quality assessment ensures that accurate, consistent, and complete records form the foundation of the new system. By prioritizing data quality, organizations reduce migration risk and ensure long-term system success.