Practical Reconstruction of a Data Management Governance Framework Based on DMBOK and Huawei Practices

Introduction – After reading the DMBOK, the author finds its theory comprehensive and recommends it as a blueprint for data management governance. Combining DMBOK with Huawei's data practices and more than ten years of hands‑on experience, the author restructures the framework from a practical perspective.

Practical Re‑construction of the DAMA‑DMBOK Framework – The original DAMA‑DMBOK wheel diagram is modified and re‑ordered to better suit real‑world implementation. Reference materials for each knowledge area are collected and will be continuously updated; readers are invited to share feedback and improve the framework together.

Re‑ordered Practical Management and Governance Framework – Visual diagrams (included in the original article) illustrate the new sequence and structure of the framework.

12 Knowledge Domains (Partial)

1. Data Management – Data Governance : Serves as the overarching domain for global documents, standards, and Huawei‑specific materials.

2. Metadata : Focuses on identifying enterprise data assets to enable effective management and governance.

3. Data Warehouse and Business Intelligence : Covers storage of data for business services and aligns data with business requirements.

4. Architecture : Involves designing data architecture after clarifying user needs and data scope.

5. Reference and Master Data : Identifies the most valuable master and reference data after architecture design.

6. Data Modeling and Design : Details the development‑oriented modeling and design activities.

7. Data Storage and Operations : Discusses database selection, installation, and configuration.

8. Data Processing Ethics – Data Security : Emphasizes security design once data resides in databases.

9. Data Integration and Interoperability : Addresses data movement, interaction, and transmission.

10. Data Quality : Highlights quality issues throughout the data lifecycle and methods for improvement.

11. Data Intelligence : Focuses on mining algorithms and predictive analytics.

12. Digital Data Value : Stresses that all governance activities aim to create data value.

Conclusion and Resource Sharing – The author asks readers to share the compiled materials, offers to provide links to each domain via a public account or a knowledge‑sharing community, and provides contact information for further collaboration.