The High Cost of Data Quality Issues

Poor industrial data quality creates cost in several ways. The largest cost is often not the bad value itself. It is the time and uncertainty required to find the cause and determine what was affected.

Data problems can look like process problems

An operator can see an unexpected KPI and assume that the process changed. An engineer can spend hours checking equipment before the team discovers that an AF analysis was changed, a tag stopped updating, or a display points to the wrong source.

During that time, the team can delay decisions or use information that is not reliable.

Common causes include:

• Stale or missing source data

• Incorrect tag mappings

• Bad engineering units or metadata

• Failed or changed calculations

• Broken AF references

• Untracked configuration changes

• Displays that use obsolete data sources

The cost is cumulative

A single issue can consume time from operations, controls, PI administrators, reliability engineers, and data teams.

The direct cost can include:

• Troubleshooting labor

• Rework

• Delayed reports

• Lost production opportunity

• Additional contractor support

• Reprocessing or backfilling data

The indirect cost can be larger. When users lose confidence in the PI System, they create manual checks and parallel spreadsheets. Those workarounds add more cost and make governance harder.

High-consequence data needs more control

Not every PI Point requires the same level of oversight.

Data that supports safety, environmental reporting, production accounting, equipment protection, or important maintenance decisions should have stronger controls.

For these signals, teams should know:

• The source

• Expected update behavior

• Important transformations

• Downstream uses

• Recent changes

• The responsible owner

This makes it possible to investigate problems quickly and assess impact.

Detect issues before users report them

Many data incidents are first reported by a dashboard user. That is late in the failure chain.

Automated monitoring can detect stale data, bad values, analysis failures, broken references, and important configuration changes earlier.

Monitoring should also include context. A failed unused tag is not equal to a failed tag that drives a critical display.

Reduce time to understand, not only time to detect

Detection is only one part of the problem.

When an issue occurs, the team must answer:

• What failed?

• When did it start?

• What changed?

• What depends on the failed object?

• Which decisions or reports can be affected?

Lineage, change history, and usage information reduce the time required to answer these questions.

The operating objective

The objective is not perfect data. Industrial systems are too large and dynamic for that standard.

The objective is to find important problems early, understand their effect, and restore trusted data before the issue becomes an operational event.