data readiness

Data readiness commonly refers to the degree to which data is in a usable state for a specific purpose, such as running operations, calculating KPIs, feeding an MES/ERP integration, or supporting audits. It combines questions of availability, structure, quality, and governance to determine whether data can be trusted and used without additional preparation.

Key aspects of data readiness

In industrial and manufacturing environments, assessing data readiness usually involves checking whether:

• Data is available: Required data points are actually captured (for example, machine states, start/stop times, scrap codes, operator IDs) and retained for the needed time window.
• Data is accessible: Systems that hold the data (MES, ERP, historians, QMS, equipment PLCs) can expose it in a practical way, such as via interfaces, exports, or reports.
• Data is structured and consistent: Fields, units, time stamps, equipment IDs, and codes use agreed conventions so that information from different sources can be combined and compared.
• Data is accurate and complete: Key values are reasonably correct, within expected ranges, and not full of gaps, duplicates, or obvious entry errors.
• Data is governed: There are clear owners, basic change control for definitions and mappings, and documented rules for how data is created, corrected, and archived.
• Context is defined: There is enough metadata (such as product, batch/lot, routing step, shift, and site) to interpret the data in relation to processes and standards.

Operational use in manufacturing

Operationally, data readiness is often evaluated before:

• Implementing or standardizing KPIs (for example, OEE or ISO 22400 performance indicators).
• Rolling out integrations between MES, ERP, PLM, QMS, or data lakes.
• Building dashboards for production, quality, or maintenance visibility.
• Supporting investigations, root cause analysis, or regulatory inspections that rely on historical records.

Data may be considered not ready if, for example, machine downtime reasons are rarely coded, production counts are reset inconsistently, or lot identifiers do not match between MES and ERP. In such cases, additional work like data cleaning, configuration changes, or process discipline is needed before the data can be relied on.

Common confusion

• Data readiness vs. data quality: Data quality focuses on accuracy, consistency, and completeness of data itself. Data readiness is broader and also covers access, structure, ownership, and suitability for a specific use.
• Data readiness vs. system readiness: System readiness refers to whether IT/OT systems are deployed, configured, and stable. Data readiness focuses on the state of the data those systems generate or store.

Relation to standardized KPIs and integrations

When organizations move from ad hoc KPIs to standardized frameworks (such as ISO 22400 for manufacturing performance indicators), data readiness becomes more visible. Standard definitions clarify which data elements are required, how they must be time-stamped, and how events (for example, planned vs. unplanned downtime) should be coded. This exposes gaps in current data capture, mapping, or governance that must be addressed before the new KPIs or cross-system integrations can operate reliably.