Osmos Global Publication · Osmos Perspective
Predictive Maintenance Is a Data-Quality Programme
Sensor volume cannot compensate for weak failure labels
Johnson Controls reported substantial AI use among surveyed facilities teams while identifying data quality and integration as a leading barrier.[6] Full methodology remains gated.
Analysis
Predictive maintenance depends on asset identity, operating context, failure history, work quality and intervention outcomes—not simply telemetry.
Osmos Global analysis: readiness begins with the ability to reconstruct why an asset failed and whether the maintenance action restored performance.
Leadership implication: a disciplined CMMS history may be more valuable than another ungoverned sensor deployment.
Decision lens
Recommended action. Validate asset hierarchy and failure coding for one critical system before modelling.
Cite this
Osmos Global Research & Knowledge Centre (2026). Predictive Maintenance Is a Data-Quality Programme. Osmos Perspective, Osmos Global. https://www.osmosglobal.org/articles/predictive-maintenance-is-a-data-quality-programme
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Discussion
Add what you are seeing on the ground.