Definition
Data quality is the degree to which data (asset master data, event logs, sensor readings, work order records) is accurate, complete, timely, and consistent enough to be trusted as the basis for a maintenance decision. It is a precondition for every other data-driven maintenance process, from predictive analytics to reporting, rather than a specific system or technique itself.
Key facts
- A precondition (accuracy, completeness, consistency) that other data-driven processes depend on, not a system or technique itself.
- Poor data quality anywhere in the chain (master data, event logs, sensor calibration) undermines analysis built on top of it.
- Common quality issues include duplicate asset records, inconsistent naming, missing timestamps, and uncalibrated sensors.
- Improving data quality is often a prerequisite before investing in more advanced analytics or AI applications.
How does Data quality work in maintenance?
Data quality determines whether the data feeding a maintenance decision (an asset's master record, a sensor's calibration, an event log's completeness) can actually be trusted, since inaccurate, incomplete, or inconsistent data produces unreliable analysis regardless of how sophisticated the analytical method applied to it is.
What does Data quality require?
Predictive analytics, anomaly detection, and AI applications all depend on the quality of the underlying data they are trained or applied on; poor data quality (missing timestamps, inconsistent asset identifiers, uncalibrated sensors) limits or invalidates the results these more advanced methods can produce, which is why data-quality work is often a necessary first step.
FAQ
Frequently asked questions
When are data suitable for a reliable maintenance decision?
When their data quality (accuracy, completeness, consistency, and timeliness) is sufficient to be trusted, since even sophisticated analysis built on poor-quality data produces unreliable results.
Last reviewed: