“Remember the good old days,” is not something that you want to hear in company meetings. It shows that your company has been taking steps backward in terms of quality and efficiency instead of maintaining standards or improving. If you follow our blog, then last week you learned all about how to cleanse your data. Once you cleanse data, you want to keep the data quality high. It is important to make sure that you are not inputting dirty data into an already clean system. These 5 steps will make sure that your data quality remains or improves.
1. Make Plans
Chances are, if you once had dirty data, then after the cleanse you will eventually revert back to your old ways and your old problems. Data cleansing is similar to when you clean the bathrooms and everything is perfect for a whole 5 minutes until someone uses it. Data quality requires constant upkeep as well as diligent and accurate entries in order to remain high. This is why we recommend that our customers prepare plans using schedules, goals, and standards for data collection going forward.
2. Have Purpose
The previous step requires goals to be set on the quality and type of data that you want to have in your CMMS and EAM systems. You need objectives for collecting that data so that the right type of raw data is meticulously collected. Focus on the types of analysis that you want to be able to conduct with your data. For example, if you decide that you want to be able to conduct a quantitative risk analysis using your data, then you can start implementing processes to collect the data that is needed for this analysis.
3. Prioritize
Put bluntly, some of the data you collect is far more important than others. Data related to equipment that is considered critical, meaning that it could have a drastic impact on health, safety and environment, is incredibly important. This data needs to be 100% complete and accurate, so set parameters that enforce this compulsory data to be complete. Then set desirable data to have a medium required completeness percent, and non-mandatory data to have low completeness.
4. Implement Accountability
The first step towards having accountability for data input requires a documented and traceable origin of data entry. Ideally, there should be a closed loop in data transfer, so that there are several checkpoints for data quality. NRX experts believe that notifications are the simplest way to accomplish this. Incomplete and inaccurate data should be checked and edited before being forgotten and lost in large quantities of incoming data.
5. Provide Resources
If your CMMS and EAM systems need improvement, then make sure to provide your employees and management team with the resources that they need to implement changes. Changing the way things have always been done will require training and motivation.
What We Do?
We provide maintenance and reliability professionals at asset-intensive businesses with world-class software solutions for analyzing, visualizing, building, editing, organizing, approving, and sustaining high-quality Asset and Maintenance Data for their Enterprise Asset Management (EAM) and Computerized Maintenance Management (CMMS) systems.
Contact us to find out how we can solve your data quality issues and improve your CMMS and EAM efficiency.
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