Rethinking Master Data Management

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Click to learn more about author Jeff Kinard.

Master Data Management (MDM) is seen as a necessary evil by many organizations. However, most haven’t found an effective method or service to handle it. In the case of asset-heavy industries, the strategies currently deployed are archaic, magnifying the problems.

Many organizations overlook and underestimate MDM, and it can quickly spiral and become an expensive and time-intensive process. Done properly, MDM requires cooperation and collaboration across teams and site locations to help govern and maintain records and lists.

When it comes to maintenance, Master Data Management is crucial. However, most companies have not found an effective way to manage data cleansing and sustainment. As a result, they struggle to find the data they are looking for. Agreeing on a taxonomy that everyone adheres to is a significant pitfall that trips many enterprises up. Determining what is the right amount of detail to how it should be organized to how much granularity of detail, are only a handful of the taxonomy elements that must be agreed upon and, critically, continually adhered to.

So, What Can Go Wrong?

For example, if maintenance teams aren’t maintaining the proper equipment records, then this leads to inaccurate reliability information. If materials are free-texted, this means procurement teams have to find and purchase the materials without leveraging existing contracts and prices. If the wrong part is obtained, then the best-case scenario is that it results in postponing scheduled work, wasting wrench time until the correct part is procured. If the part is for equipment that is already down this will ensure that the equipment will remain offline until the correct part is located. In some situations, if the wrong part is used, then it could potentially cause an accident like an explosion.

If materials are free-texted, this means procurement teams have to find and purchase the materials without leveraging existing contracts and prices. If the wrong part is obtained, then the best-case scenario is that it results in postponing scheduled work, wasting wrench time until the correct part is procured. If the part is for equipment that is already down this will ensure that the equipment will remain offline until the correct part is located. In some situations, if the wrong part is used, then it could potentially cause an accident like an explosion.

Why Software Doesn’t Solve All the Problems

There are a plethora of MDM software solutions available on the market that help ensure that data cleansing is consistent. However, there are still inherent issues, including the fact that even with software, it’s still a massive cleaning project, and the taxonomy still needs to be defined, which means that all of the taxonomy related issues remain.

Even if an organization can reach agreement on a taxonomy, there are still issues keeping it updated with all the new data generated after each cleanse. This is where problems start to creep in.

The Path Forward

There needs to be a mindset shift around how to approach master data cleansing. Organizations need to stop viewing their taxonomy as a competitive advantage and realize that they have the same plant assets as everyone in their industry. Therefore, sharing a common data taxonomy helps everyone. By adopting a Data-as-a-Service (DaaS) model, this will address many of the current issues with MDM, especially around taxonomy and ongoing data cleansing and sustainment.

By utilizing DaaS, you no longer need to manage and maintain your own taxonomy or codes or have to input descriptions cutting down on potential errors. Also, it removes the need to manually sustain data as the web-based system automates any updates throughout the lifetime of the database. Master data cleansing projects will be obsolete as master records are automatically maintained, and data governance happens in the cloud.

It’s time for heavy asset industries to embrace a new way of approaching MDM. Adopting DaaS is an essential step in the march towards predictive maintenance becoming a reality.

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