From Mess to Masterpiece: How Data Cleansing Improves Your BOM Accuracy

A study conducted by IBM finds that poor-quality data costs the U.S. economy more than $3.1 trillion every year due to lost productivity and operational errors. Inaccurate Bill of Materials (BOM) records are part of that cost, as they drive procurement delays and costly rework. As such in asset-intensive environments, a BOM must be accurate as when its data contains errors, the impact is immediate.

Common Causes of BOM Data Issues

BOM data degradation often begins with traceable causes. Common sources include manual data entry without enforced standards, which results in inconsistent part naming and format variations. System migrations can introduce structural errors, such as truncated descriptions or misaligned fields, when data from legacy systems does not map cleanly into the new environment. Engineering changes that are not synchronized across all dependent systems create discrepancies between physical components and their digital records. Over time, these issues accumulate, leading to duplicated part entries and outdated specifications.

Restoring BOM Accuracy Through Data Cleansing

Data cleansing provides a systematic framework for restoring the integrity of BOM records. It encompasses the detection and correction of inaccuracies, and the elimination of inconsistencies that undermine reliability. In BOMs, cleansing involves verifying part numbers, standardizing material specifications, and aligning units of measure across systems. The process also supports larger data governance efforts such as master data alignment and the standardization of asset hierarchies.

Importance of Data Cleansing

Gartner reports that poor-quality data is responsible for nearly 40% of enterprise process inefficiencies, including failures in supply chain systems. BOM errors directly contribute to these inefficiencies, especially when parts data is incomplete or misaligned. To prevent these issues, BOMs must be validated regularly. Each item must align with its engineering specification and appear only once in the hierarchy. IBM outlines six dimensions of high-quality data accuracy, completeness, consistency, timeliness, validity, and uniqueness that provide a standard for assessing this work.

Automation helps by enforcing structure during data entry. Systems can reject incomplete records or flag irregular formatting. However, manual review remains essential for high-risk or compliance-related BOMs. When BOM data is clean, teams spend less time correcting mistakes. Inventory remains accurate. Work orders move faster. Maintenance teams trust what they see in the system. These are not technical improvements, they are operational ones.

Conclusion

Data quality is not a byproduct of routine operations; it is the result of deliberate effort. Achieving and maintaining high standards requires structured review processes and disciplined execution across all contributing teams. For BOM data, this discipline translates into greater operational control and a measurable reduction in unexpected disruptions.