Data quality KPI

A data quality KPI is a key performance indicator used to measure how well data meets defined quality criteria for a business or operational purpose. In manufacturing and regulated operations, it commonly refers to metrics that track whether data is accurate, complete, consistent, timely, valid, and usable across systems such as MES, ERP, QMS, historians, and connected shop floor applications.

The term refers to the measurement itself, not the data set, the reporting dashboard, or the root cause of bad data. A data quality KPI can be calculated for master data, transactional data, equipment data, quality records, genealogy records, supplier data, or integration outputs.

What it typically includes

• Accuracy: whether data correctly reflects the real-world item, event, or condition.

• Completeness: whether required fields or records are present.

• Consistency: whether the same data matches across systems, sites, or reports.

• Timeliness: whether data is captured and available when needed.

• Validity: whether values conform to allowed formats, ranges, rules, or reference data.

• Uniqueness: whether duplicate records are avoided where only one should exist.

How it appears in manufacturing systems

In practice, a data quality KPI is often used to monitor data that supports production, traceability, release, planning, maintenance, and quality workflows. Examples include the percentage of production records with all required fields completed, the rate of duplicate material master records, the share of lot genealogy records posted within a target time window, or the number of interface transactions rejected because of invalid codes.

These KPIs may be tracked at the process level, system level, site level, or data-domain level. They are commonly reviewed as part of data governance, integration monitoring, exception handling, and operational reporting.

What it is not

A data quality KPI is not the same as a business performance KPI such as OEE, scrap rate, or on-time delivery, although poor data quality can affect those measures. It is also not identical to a data validation rule. Validation rules check individual entries or transactions, while a data quality KPI summarizes performance over time.

Common confusion

Data quality KPI is often confused with data integrity. Data quality focuses on whether data is fit for use. Data integrity usually refers more specifically to the reliability, completeness, and trustworthiness of data throughout its lifecycle, including controls around creation, change, and retention.

It may also be confused with report quality or analytics accuracy. Those can be affected by data quality, but they are not the same thing.