Data governance is the organizational framework that defines how data is owned, managed, accessed, and controlled across an enterprise. It typically includes policies, roles, processes, and technical controls that guide how data is created, modified, shared, retained, and monitored.
In industrial and manufacturing environments, data governance commonly covers data originating from OT systems, MES, ERP, quality systems, historians, and laboratory or maintenance systems. It seeks to ensure that operational and compliance-critical data is accurate, consistent, understood in context, and handled in line with regulatory and internal requirements.
Key elements of data governance
While implementations vary, a data governance framework usually addresses:
- Data ownership and stewardship: Clear assignment of who owns specific data domains (such as production, quality, maintenance) and who is responsible for day-to-day data stewardship.
- Data definitions and standards: Common, documented definitions for key data elements and KPIs (for example, how a batch, lot, downtime event, or quality defect is defined), including naming conventions and master data standards.
- Data quality rules: Criteria for completeness, accuracy, timeliness, and consistency, along with procedures to detect, correct, and prevent data issues.
- Access and usage controls: Rules for who can view, change, approve, or export data, including role-based access, segregation of duties, and alignment with cybersecurity and privacy requirements.
- Lifecycle and retention: How long specific data types are retained, how they are archived, and how they are eventually disposed of, especially for records subject to regulatory or customer requirements.
- Data lineage and traceability: Documentation or tooling that shows how data moves between systems, how it is transformed, and which sources feed critical reports and KPIs.
- Change oversight: How changes to data structures, master data, integrations, or KPI logic are requested, reviewed, tested, approved, and documented.
Operational role in manufacturing
Operationally, data governance appears in activities such as:
- Standardizing KPI definitions and ensuring that reports from MES, ERP, and BI tools use the same underlying logic.
- Controlling who can modify master data (such as materials, routings, equipment, and specifications) and recording those changes.
- Defining how electronic records from production, quality, maintenance, and labs are captured, time-stamped, and linked to lots or serial numbers.
- Coordinating with OT and IT teams to ensure data from shop-floor equipment is reliably collected and correctly contextualized.
- Providing a formal route for resolving data conflicts between plants, departments, or systems.
Relation to KPI frameworks
In the context of manufacturing KPI frameworks, data governance defines how metrics are sourced, maintained, and controlled. It clarifies:
- Which systems are the authoritative source for each KPI.
- Who owns the definition and calculation logic for each metric.
- How changes to KPI formulas or data sources are evaluated and approved.
- How evidence for reported performance is stored and can be reconstructed for internal or external review.
Common confusion
- Data governance vs. data management: Data management is the operational work of moving, storing, modeling, and reporting data. Data governance defines the rules, responsibilities, and oversight under which that work is performed.
- Data governance vs. IT governance: IT governance focuses on broader technology strategy and decision-making. Data governance is specifically about data assets and how they are controlled, even when responsibilities span multiple functions (operations, quality, IT, OT, and compliance).