CMMS, EAM & Spare Parts Management worked example

Maintenance Data Quality Score with decision impact of poor cmms data of 20 score: a worked example

Push decision impact of poor cmms data up to 20 score and the picture changes. This example computes every intermediate figure at that operating point. a maintenance or asset-management team needs to prioritize data cleanup, master-data governance, training, and required-field controls for a maintenance data quality review

The inputs for this scenario

  • Decision impact of poor CMMS data: 20 score (raised for this scenario; the documented default is 8)
  • Frequency of incomplete or incorrect records: 6 score (unchanged)
  • Weakness of data validation controls: 5 score (unchanged)

Working through the calculation

  • Applying the documented formula (Maintenance Data Quality Score risk score = decision impact of poor CMMS data × frequency of incomplete or incorrect records × weakness of data validation controls) to the inputs above produces each figure below.
  • At this operating point the engine returns 11.35 score for maintenance data quality score risk score, the number this scenario is built around.
  • At this operating point the engine returns 20 score for decision impact of poor cmms data.
  • At this operating point the engine returns 6 score for frequency of incomplete or incorrect records.
  • At this operating point the engine returns 5 score for weakness of data validation controls.

How this compares with the baseline

  • Against the tool's baseline example, where decision impact of poor cmms data sits at 8 score and the headline result is 6.55 score, this scenario comes in 73.28% above the baseline at 11.35 score.
  • It multiplies the decision impact of poor data, the frequency of incomplete or incorrect records, and the weakness of validation controls into a single data-quality risk score. The value of this scenario is the size of the gap it exposes: that gap, priced out over a year, is the budget you can justify spending to close it.

Results at a glance

  • Maintenance Data Quality Score risk score: 11.35 score (headline result)
  • decision impact of poor CMMS data: 20 score
  • frequency of incomplete or incorrect records: 6 score
  • weakness of data validation controls: 5 score

Run it with your numbers

  • Every input above is editable in the live Maintenance Data Quality Score calculator, which recalculates instantly and can be shared with the inputs intact.

Last reviewed 2026-05-12.