raw data

Raw data commonly refers to unprocessed values captured directly from a source, such as sensors, machines, operator inputs, or IT/OT systems, before those values are cleaned, transformed, aggregated, or interpreted.

What raw data includes

In industrial and manufacturing environments, raw data typically includes:

• Time-stamped sensor readings (for example, temperature, pressure, speed)
• Machine status codes and event logs from PLCs, SCADA, or MES
• Unaggregated production counts, scrap counts, and cycle times
• Operator entries such as checklists, comments, or manual measurements
• Transaction-level records from MES, ERP, LIMS, or quality systems

Raw data is usually stored in historians, databases, log files, or message streams before any significant business logic is applied.

What raw data does not include

Raw data does not include values that have been substantially processed or interpreted, such as:

• Indicators that combine multiple raw data points into a single derived metric
• Key performance indicators (KPIs) such as OEE, on-time delivery, or defect rate
• Aggregated reports (for example, hourly, shift, daily summaries)
• Statistical calculations like averages, control limits, or capability indices

Once rules, calculations, or contextual enrichment are applied, the result is no longer considered raw data, even if the original values are retained.

Operational role in manufacturing systems

In OT and IT environments, raw data is the foundation for monitoring, analysis, and compliance activities. It is:

• The input to indicators and KPIs used in performance dashboards and reports
• The evidence base for traceability, genealogy, and deviation investigations
• The feedstock for advanced analytics, such as predictive maintenance models
• A key element of data integrity and audit trails in regulated operations

Effective governance often distinguishes raw data from transformed data so that users understand which values are directly measured and which are calculated or modeled.

Relation to ISO 22400 concepts

In the context of ISO 22400, raw data corresponds to basic measurements captured from equipment and systems. These measurements can then be transformed into indicators by adding calculations or context, and a subset of those indicators may be designated as KPIs for operational or business decision-making. The standard emphasizes structural distinctions but does not, by itself, guarantee how any site defines or governs its raw data.

Common confusion

• Raw data vs. indicators: Indicators typically aggregate or calculate values from raw data and may include business rules or contextual information, while raw data remains as-captured.
• Raw data vs. logs: Logs are often collections of raw data, but a log file may also contain derived or formatted entries; individual log entries can still be raw data if they directly reflect unprocessed measurements or events.
• Raw data vs. cleansed data: Once data has been filtered, corrected, or normalized, it is no longer strictly raw, even if it retains the same basic structure as the original measurements.