Ensuring Data Quality in CMMS Systems: Techniques and Tools

This paper discusses the DQ in CMMS and focuses on ways and tools for data accuracy for better maintenance efficiency and decision-making. It explains what information is crucial to keep CMMS reliable, accurate and efficient to support maintenance processes, asset tracking and overall performance. This paper describes six DQ dimensions: accuracy, completeness, consistency, uniqueness, timeliness, and validity as they pertain to the implementation of CMMS. Furthermore, it is informative on the part that data users, custodians, and producers play in maintaining quality data. The paper also addresses several procedures, including data formatting, cleaning, prepping, managing, and inspecting, that are crucial for maintaining data excellence in CMMS. Moreover, it discusses the application of sophisticated solutions – CMMS software with strong quality performance, data quality tools, integration tools and BI tools used to track and enhance data quality. A study’s conclusion is focused on pointing out the importance of having a proper way of data quality management in CMMS to enhance the maintenance processes and minimise potential mistakes.