data validation

Data validation is the systematic process of checking that data is accurate, complete, consistent, and appropriate for its intended use before it is relied on in a process, system, or decision. In industrial and regulated manufacturing environments, it commonly refers to verifying that production, quality, and business data are correctly captured, transformed, stored, and reported across OT and IT systems.

Key aspects

Data validation typically includes checks that:

• Format and type are correct (for example, numeric fields contain numbers, timestamps follow expected patterns).
• Ranges and limits are respected (for example, measurements fall within plausible engineering or specification limits).
• Completeness is ensured (required fields are present, no unexpected gaps in time-series or batch records).
• Consistency is maintained across systems (values match between MES, historians, LIMS, ERP, and reporting layers).
• Business rules are satisfied (for example, a batch cannot move to release status without associated test results).

Data validation can occur at multiple points, such as at data entry on the shop floor, during integration between systems, when transforming or aggregating data, or when generating reports and KPIs.

Operational meaning in manufacturing

In manufacturing operations, data validation commonly appears as:

• Configured checks in MES or electronic batch records to prevent invalid operator entries.
• Interface and integration tests ensuring that tags, units, and identifiers are mapped correctly between OT and IT systems.
• Reconciliation between source data (for example, historian or MES) and downstream KPIs or dashboards.
• Documented review of data transformations used in performance, quality, or compliance reporting.

In regulated settings, data validation activities are often documented and governed by procedures so that data used for product release, quality decisions, or official reporting can be traced back and reviewed.

Relation to system and KPI validation

Data validation is related to, but distinct from, validating a system or a KPI definition:

• System validation focuses on demonstrating that a system (such as an MES, LIMS, or data platform) performs as intended and is suitable for its intended use.
• KPI validation focuses on confirming that a metric’s logic, inputs, and calculations correctly implement the agreed definition.
• Data validation focuses on the correctness and reliability of the actual data values that flow through those systems and metrics.

When new KPI definitions or data pipelines are introduced, organizations may run old and new versions in parallel for a transition period to support data validation and identify discrepancies before fully switching over.

Common confusion

• Data validation vs. data quality: Data quality is a broader concept covering dimensions such as accuracy, timeliness, and usability. Data validation is a set of checks and activities used to assess and maintain those qualities.
• Data validation vs. verification: In some disciplines, verification refers to confirming that an implementation meets its specification, while validation focuses on fitness for intended use. Data validation often includes both elements in practice, but is usually described in terms of concrete checks on data values and structures.