OT data governance refers to the systematic management, standardization, and quality control of data generated from industrial operations, ensuring it can effectively support enterprise decision-making and AI applications. It covers the end-to-end process—from data acquisition, format transformation, quality validation, and access control to storage and downstream usage. Unlike IT data governance, OT data governance must account for diverse equipment communication protocols, complex data types, real-time operational requirements, data security, and the constraints of edge computing environments.

Specifically, the core objectives of OT data governance are:
– Enable cross-department and cross-system data integration and reuse
– Improve data usability and consistency
– Reduce the technical and manpower barriers for data integration
– Protect the security and integrity of operational data
– Comply with regulatory requirements, such as IEC 62443 and ISO/IEC 27001

This whitepaper aims to help readers understand how to build an effective OT data governance strategy and framework in response to OT data ETL challenges. Achieving true data value requires a structured collaboration mechanism across departments—integrating the efforts of IT teams, manufacturing teams, operations and maintenance teams, and data science teams.

Comprehensive data governance also includes data asset inventory, definition of critical data, access control, and data lifecycle management. These mechanisms must be supported by systematic institutional design and disciplined implementation to be effective.