Industrial Data Governance: Challenges, Practices, and Insights

In recent years, data governance and data assetization have become key focuses of industrial digitalization, attracting increasing attention from enterprises. This presentation addresses how industrial enterprises can implement and scale data governance to create tangible business value.

Background

Industrial digitalization has matured over a decade; most enterprises now tightly align data technology and value with their industrial and business development. Drivers include national policies, industry evolution, and the "14th Five-Year Plan" emphasis on data economy. Data is now recognized as a core asset essential for the transition from automation to intelligent digital transformation.

Challenges include data acquisition, governance, quality assurance, and efficiency. Approximately 80% of effort in digital transformation is spent on data-related activities.

Practice

Two major practice categories are highlighted:

High‑end complex equipment : Data governance for wind turbines, steam turbines, large fans, compressors, etc., covering production processes, operating environments, and optimization.

Long‑process production lines : Data governance for semiconductor and other long‑duration production lines, handling massive, multi‑dimensional data from devices, sensors, and business processes.

Examples include wind‑farm data (SCADA, wind measurements, environment, fault information) generating billions of records daily. The governance workflow involves real‑time processing, standardization, and transformation into high‑quality data services for downstream applications such as fault prediction, spare‑part forecasting, and financial planning.

Thinking

Key reflections:

Data governance must be end‑to‑end, covering the entire data chain from source to business value.

Governance should be phased, focusing on quality assessment, standard conversion, and incremental value delivery.

Data should be treated as a shared asset across departments and partners, requiring consistent standards and semantics.

Organizational and process frameworks ("digital office") are as important as technical solutions.

A simple data‑governance conceptual model is presented, showing the flow from production systems to platforms and finally to value‑driven applications.

Summary

To measure the ROI of data governance, teams should evaluate business impact, adopt phased governance, and perform demand‑driven data assetization. A systematic approach combining technology, standards, and organizational capability is essential for sustainable industrial digital transformation.

Q&A

Example: A national grid project integrated heterogeneous wind‑farm data to provide unified power‑forecasting services, demonstrating how standardized, real‑time data governance can unlock significant business value.