Poor EAM/CMMS data has a major impact on a company’s asset integrity. Not only can it affect the expenses in your maintenance process, but it may also impact health and safety standards.

Root problems in EAM data can be difficult to identify – all red flags just look like flags if you do not know what to look for. If you are wondering about potential red flags in your EAM/CMMS data, consider the following:

1. Using Outdated Tools

Spreadsheets or homegrown software programs do not meet the needs of current maintenance engineering teams; they need an option that can build, enrich, validate, and transfer maintenance master data (MMD). Moreover, issues of outdated EAM tools may make it difficult to transparently display the progress of projects to owners and operators.


2. Operating in Silos

Organizations that span across various geographical locations may find themselves working in silos with their maintenance master data. Some EAM tools makes it difficult to collaborate on resources for building MMD. Consequently, project owners lose access to the bigger picture of a project since they cannot refer to prior information from the company’s data build.


3. Building Maintenance Master Data Too Late

Maintenance master data (MMD) is essential to keeping an accurate record of assets, parts, vendors employees, etc. As a company grows, so does its data. As such, if master data is built with inconsistent standards or incomplete information, it can cause unnecessary issues with management decisions. The recommended time to begin building your MMD is during the Feed stage of the Asset Lifecycle.


4. Difficulty Managing Change

There may be instances where EAM data changes. As such, the new data must adhere to pre-existing standards. Efficient tools in EAM/CMMS systems should apply, enforce, and automate standards. If not, it can slow down the build process, deteriorate data quality and may even result in regulatory penalties.


5. Inability to Learn from the Past

Data provides valuable insight into the company’s maintenance process, which can be used to increase productivity. Even small and medium size businesses have found great results from leveraging their EAM data to gain real-time data analytics on current trends and future opportunities. In contrast, inaccurate data gives a false view of maintenance operations, which can work against the organization’s goals.

Maintenance data that follows a standard and is absent of duplicate and obsolete parts produce reliable analytics for engineering teams to use.


6. Lack of Rigorous Validation

Manual governance processes fail to account for large MMD builds. These capital projects can be complex so human error is prone to occur, which exposes the company to critical risks. Organizations should automate this process to improve the quality of their master data.


7. Having an Unreliable Asset and Maintenance Data Foundation

Reference data like spare parts data, BOMS, etc. are necessary for building MMD; but if it exists across various databases and software applications, it can be difficult to assemble it into one location. It is important to ensure that all data is incorporated into the MMD so maintenance engineering teams can make efficient decisions. Otherwise, they will have to waste time finding reference data while performing a task.


Need Help Understanding Where Your EAM Master Data Stands?

Whether you know exactly what your EAM master data needs or have no idea where to start looking, our benchmarking service can help! Our consultants address inefficiencies in EAM/CMMS systems by comparing it to both internal standards and industry best practices. Furthermore, we provide actionable reports so organizations have a clear roadmap to improving their maintenance data. To learn more, click on the link below to read our brochure or book a meeting with one of our consultants.

Related Posts
Here’s What Happens to Maintenance When your EAM Data is Bad

Effects of Poor EAM Data on Operations

Impacts of Inaccurate EAM Master Data on Maintenance and Operations

Share this article