Data Governance

1. Overview: An Authority-and-Accountability Framework for Managing Data as a Strategic Asset

    flowchart LR
    A["Data disorder<br/>(silos, non-standard)"] --"Establish principles,<br/>organization, process"--> B["Clarify data<br/>authority & accountability"] --"Apply metadata &<br/>standardization"--> C["A trustworthy<br/>data asset"]

    style A fill:#FFEBEE,stroke:#D32F2F,color:#000
    style B fill:#E3F2FD,stroke:#1976D2,color:#000
    style C fill:#E8F5E9,stroke:#388E3C,color:#000

Definition: A management framework that systematizes data management principles, governance organization, and operating processes, and clearly defines authority and accountability over data assets, in order to guarantee the availability, usability, integrity, and security of data within an organization.

Characteristics: (A business-asset perspective) A shift in perspective that treats data not as an IT resource but as an enterprise-wide business asset. (Separation of roles and responsibility) Separates the roles and responsibilities (R&R) of the data owner, steward, and custodian. (Data consistency) Ensures organization-wide data consistency through metadata management and data standardization.

2. Components of Data Governance

A. Principle, Organization, Process

    flowchart TD
    subgraph R1["　"]
        direction LR
        P["Principle<br/>Establish data management<br/>direction, criteria, policy"]
        O["Organization<br/>Define DGC, data owner,<br/>steward roles"]
    end
    subgraph R2["　"]
        direction LR
        PR["Process<br/>Operate data lifecycle<br/>management procedures"]
        T["Technology<br/>Data catalog,<br/>MDM, quality tools"]
    end

    style P  fill:#E3F2FD,stroke:#1976D2,color:#000
    style O  fill:#F3E5F5,stroke:#7B1FA2,color:#000
    style PR fill:#FFF3E0,stroke:#F57C00,color:#000
    style T  fill:#E8F5E9,stroke:#388E3C,color:#000
    style R1 fill:none,stroke:none
    style R2 fill:none,stroke:none

Component	Key Content	Core Deliverable
Principle	Define the organization’s basic policy on data quality, security, and privacy	Data policy document, standards guide
Organization	Form a Data Governance Council (DGC), establish owner/steward/custodian R&R	Governance org chart, role descriptions
Process	Operate standard procedures across the full data lifecycle — collect, store, use, retire	Data lifecycle process map
Technology	Platforms/tools that support governance execution (catalog, MDM, DQ tools)	Technology architecture roadmap

Governance Organizational Hierarchy

Role	Level of Responsibility	Key Duties
Data Governance Council	Strategy/policy decisions	Set data strategy, approve budget, decide on issues
Data Owner	Business ownership	Define data, set quality criteria, approve access rights
Data Steward	Operational management	Monitor data quality, register metadata, ensure standards compliance
Data Custodian	Technical management	DB operations, security controls, backup/recovery

B. Metadata Management and Standardization

    flowchart LR
    subgraph META["Types of Metadata"]
        direction TB
        M1["Technical metadata<br/>Tables/columns/data types<br/>DB schema information"]
        M2["Business metadata<br/>Data definitions/owners<br/>Business glossary"]
        M3["Operational metadata<br/>Processing history/lineage<br/>Usage frequency/quality metrics"]
    end

    CAT["Data catalog"]

    META --> CAT

    style M1 fill:#E3F2FD,stroke:#1976D2,color:#000
    style M2 fill:#F3E5F5,stroke:#7B1FA2,color:#000
    style M3 fill:#FFF3E0,stroke:#F57C00,color:#000
    style CAT fill:#1E3A5F,stroke:#1E3A5F,color:#fff

Standardization Area	Description	Application
Data terminology standard	Define common enterprise-wide business terms and manage synonyms/homonyms	Build a business glossary
Data model standard	Entity/attribute naming rules, domain/code-value standardization	Establish an enterprise standard data model and operate a registry
Data lineage	Track a data element’s origin, transformation, and movement	Trace root cause of analytical errors and support regulatory audits
Data catalog	An integrated portal that supports search, discovery, and understanding of enterprise data assets	Lay the foundation for a self-service data analytics environment

3. Expected Benefits and Practical Application of Data Governance

Category	Key Expected Benefit	Practical Application
Data reliability	Improved decision quality through accurate, consistent data	Establish a single source of truth
Regulatory compliance	Systematized response to GDPR, privacy law, and financial regulation	Automate records of processing activities (RoPA) and support audits
AI/analytics foundation	Improved AI model reliability through a high-quality data pipeline	Strengthen the feature store and MLOps integration
Operational efficiency	Reduced maintenance cost by eliminating data duplication/errors	Unify master data through MDM implementation

DAMA-DMBOK