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IAIDQ Publications

Ensuring Maintenance Data is Good Enough to Trust: Understanding the Characteristics of Data Quality
January 2011
David Berger, P.Eng.

If you’ve ever been surprised by the results of a query or report generated by your Computerized Maintenance Management System (CMMS), there are several possible reasons. Perhaps you misread the numbers or pressed a wrong key. Maybe you weren’t aware of certain information, your expectations were unrealistic, or you were in denial. Unfortunately and all too often, poor data quality is the culprit — “garbage in, garbage out.” If indeed that is the reason, your CMMS software is not worth the cost of the computer on which it runs.

It’s quite astonishing just how reliant management is on a CMMS to provide information such as budget variances, asset availability and performance, energy consumption, payroll hours consumed, work backlog and so on. Yet despite our thirst for information, there’s sometimes little thought as to where the data is coming from and whether it reflects reality. It’s our inexplicable blind faith in technology that is our weakness — as if anything the CMMS outputs to screen or paper must be accurate because a computer processed it. As many maintenance managers have discovered over the years, the quality of data input into the CMMS can be sadly lacking.

This article explores 16 characteristics of trustworthy data. Outlined below is the definition of data quality, including examples of how it can be compromised.

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