Data Quality in Integrity Programs

What this topic covers

How traceability, units, calibration, timestamping and asset identity affect decisions.

Core ideas

Condition data without a reliable asset ID, measurement location or date can be misleading.

Trend analysis requires comparable methods and units over time.

Data-quality controls should flag missing context, impossible values, duplicate records and unexplained changes in measurement method.

Program tradeoffs

A mature program does not inspect everything equally; it identifies credible degradation and prioritizes the assets where loss of function matters most.

What good evidence looks like

Data quality, asset identity and decision ownership are as important as the sensor or inspection method.

Lifecycle perspective

Lifecycle thinking links design, operation, maintenance, renewal and retirement rather than treating integrity as a late-life activity.