The Seven Critical Data Quality Challenges in Enterprise Asset Management

Due to the complexity of Enterprise Asset Management (EAM), data quality challenges are inherent in its practice. Such prevalence, however, prevents the asset managers from reflecting upon the substantive state of organizational productivity, performance, and prospective growth.

For instance, some organizations’ assets are not accurately defined, so their real value is unknown or hidden. Likewise, some organizations have inconsistent descriptions or identifiers for assets which can lead to inconsistencies when used in reports and analysis.

Not only this, at times, there are gaps between what an asset looks like (actual) and what it should look like (ideal) — disparity of the highest order as far as the business decisions are concerned.

The thing is that the quality of decisions for asset management depends on the quality of data available. If this data is incorrect, the organization might never achieve its objective of successful EAM and, in turn, might never be able to cater ideally to its customers.

And while software solutions have been developed to help enterprises with asset management, a host of situations still reflect upon the data for asset management being dispersed, inconsistent, and inaccurate.

The Seven Biggest Data Quality Challenges that Hinder EAM​

Lin et al., through a profound case study in 2006, investigated the data quality issues pertaining to asset management. As a result, they were able to outline the major factors that influenced data quality — from interoperability standards to cleansing techniques and organizational structure/culture to control systems. Personnel competency was also a factor listed out amongst others, which didn’t come as a surprise.

After all, the quality of data depends on the combination of technology, organization, and people. To that end, here are the most critical data quality challenges that make the lives of modern-day asset managers difficult.
  1. Incorrect Data​

  2. Lack of Uniformity​

  3. Falsified Tagging​

  4. Lack of Standard-Authority​

  5. Incorrect Validation​

  6. Multiple Data Sets​

  7. Data Acquisition​