How It Happens (The 3 Levels of Data Quality Issues)
Where CMMS Data Goes Wrong
CMMS data issues typically fall into three universal categories:
- Missing Data → report unsatisfactory CMMS data quality and low trust in their system (2025 ITI Research Findings).
- Incorrect Data → the average cost of poor data quality for organizations (Gartner).
- Data Drift → gradual misalignment over time as new entries diverge from standards and governance lapses
CMMS data issues typically fall into three universal categories:
- Configuration Data
- Master Data
- Transactional Data
- Examples: Manufacturers, asset classes, cost centers, failure codes.
- Impact: Poor configuration creates chaos in reporting and prevents consistent roll-ups across sites.
- Examples: Asset records, spare parts, PMs, job plans.
- Impact: Incomplete or inconsistent foundational records drive inventory waste, unreliable schedules, and compliance risks.
- Examples: Work orders, notifications, service requests.
- Impact: Missing or inaccurate work order details make analytics unreliable, masking trends and blocking meaningful insights.
Summary line:
Without active monitoring, missing, incorrect, and drifting data gradually compound across all three levels — until the CMMS can no longer be trusted.