EAM Migrations: Garbage In, Garbage Out (GIGO)

Companies might decide to conduct an EAM Migration, hoping that migration alone will solve their maintenance issues. Although EAM Migrations provide an abundance of benefits in improving maintenance processes, the benefits will only be reaped if asset and maintenance master data quality issues are fixed in the process. Simply switching to a new system isn’t enough to fix master data issues and may even make the issues you are currently facing worse.

Ultimately, it doesn’t matter how grand your new EAM system is if the data about your assets and how to maintain them itself is not fit for purposes. The phrase “garbage in, garbage out” (GIGO) is used in coding to highlight how a program’s outputs cannot be better than its inputs. The same is true when it comes to EAM asset and maintenance master data. Below are ways in which poor-quality EAM data can negatively impact maintenance.

Preventive Maintenance (PM) Plan Data

PM data is more complex than other types of master data, which makes it more challenging to get it right. They can cause a lot of disorganization and inconsistencies within your EAM system, primarily when time-based PMs are organized with condition-based strategies. For example, your PMs could be located at varying asset hierarchy levels. When migrating to a new EAM system, your PMs will still be organized the same way unless action is taken to reorganize them during the process. PM disorganization can make it difficult to prioritize work, measure work, and determine the resources and manpower needed to complete work.

Operations

Poor EAM data can impact operations in a multitude of ways. For one, insufficient data can reduce a company’s operational readiness. Operational readiness is about putting operations workers in a position to succeed by having the right people, systems, and procedures in place. Poor EAM data can make it difficult for the operations team to devise a comprehensive operational strategy, ensure data adheres to corporate standards, meet deadlines, and more. Again, switching systems will not improve your operational readiness unless the master data is fixed before it is loaded onto the new system.

Asset Integrity

Inaccurate and poor quality EAM data can affect the ability of a company’s assets to meet environmental, health, and safety standards.  This can result in more hazardous situations among a company’s assets and lead to operational failures. Poor data also leads to poor asset lifecycle management and negatively impacts ROA. In turn, it can decrease an asset’s lifespan, quality, and ability to maintain and repair the asset successfully. Inputting poor quality EAM master data into your new EAM system is going to result in poor asset integrity.

Solution

An EAM Migration can be an excellent investment for companies looking to upgrade their current asset management system or want to implement one for the first time. However, the data that is input into the new system will have the same output regardless of the system in place. This is why improving EAM asset and maintenance data during the migration makes sense when you have the time and opportunity to do so.

HubHead’s solutions can help edit, cleanse, enrich, de-duplicate, and validate your data according to corporate standards before it’s loaded into your new EAM system. Contact us to book a demo or download our brochures to learn more.