CMMS Data Optimization

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 → critical fields left blank or skipped (e.g., manufacturer, failure code, lead time).
• Incorrect Data → wrong, inconsistent, or duplicate values (e.g., misclassified assets, duplicate parts, incorrect codes).
• Data Drift → gradual misalignment over time as new entries diverge from standards and governance lapses.

These problems occur across three critical levels of CMMS data:

• 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.

The Solution – CMMS Data Optimization

A Guided Path to CMMS Excellence

CMMS Data Optimization is the easy, structured way to fix the problem for good.

• Monitor – Score your CMMS data health, highlight risks, and build a cleansing plan.
• Assess – Conduct a deep-dive into your CMMS setup, workflows, and adoption.
• Cleanse – Deliver a fully refreshed dataset using accelerated tools, user-developed queries, and AI-powered smart detection.

Outcome

A fully refreshed, standardized, analytics-ready dataset.

• Continuous monitoring to sustain data health as a living program.
• Unlock rapid, accurate analysis across larger asset populations.
• A CMMS your teams can finally trust.