Comprehensive Guide to Data Governance and Data Asset Management

1. Business Digitization Goal – The aim of business digitization is to create an integrated flow of business, information, and data, reducing information loss across strategic planning, business objectives, solution design, data aggregation, analysis, and monitoring.

2. Data Governance Construction Path – Data governance links business flow to information flow, data flow, and database tables, ensuring that even if source systems cannot be changed, governance is applied at the ODS layer to produce accurate data representations.

3. Typical Enterprise Digital Platform Framework

The framework consists of four layers: (1) Business systems as data sources, (2) Data middle platform for aggregation and modeling, (3) Self‑service data consumption for analysts and developers, and (4) Intelligent decision‑making such as dashboards and AI applications.

4. Core Content of Data Governance

Data governance involves two main actions: business data governance to create true data images, and analysis system governance to design reasonable analytical structures based on those images.

5. Data Governance Implementation Path

The path consists of two parts: (1) Governance activities starting with data inventory to build a comprehensive data‑asset map, followed by standardization and quality improvement; (2) External empowerment by establishing governance frameworks, roles, and processes, then iteratively updating the asset map.

6. Data Asset Inventory – Starting Point

Data assets are the foundation for all governance actions. An example from a manufacturing company shows how to identify business objects across production, logistics, and after‑sales, and map them to L1‑L3 layers for conceptual, logical, and physical modeling.

7. Data Asset Deepening and Responsibility Assignment

After building the asset directory, responsibilities are assigned at appropriate layers (L3 or L2) to define owners for business data, system management, and data entry, forming a responsibility matrix.

8. Platform Support for Data Governance

Implementation requires an IT platform that supports data integration, governance (standards, quality, security), and data sharing. The article mentions a one‑stop data intelligence platform that provides these capabilities.

9. Practical Cases

Case 1 – A food processing company used report‑driven data governance to design a data‑governance system and an indicator framework, delivering both business value and a solid data foundation.

Case 2 – An automobile manufacturer started with a data‑asset inventory and governance framework, later creating a business panorama map to guide IT planning.

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