DCMM

DCMM Overview

DCMM (数据管理能力成熟度评估模型 - Data Management Capability Maturity Assessment Model) is China’s national standard GB/T 36073-2018 for evaluating data management capabilities. It provides a systematic framework to help organizations assess and improve their data management maturity.

Key Characteristics

• Standard: GB/T 36073-2018 “Data management capability maturity assessment model”
• Update: DCMM 2.0 (GB/T 36073-2025) released December 2025, effective July 2026
• Structure: 8 core capability domains, 28 process areas, 445 capability standards
• DCMM 2.0: Expands to 9 capability domains and 33 process areas, adds Data Assets domain
• Purpose: Guide organizations in developing comprehensive data management capabilities
• Application: Enterprise data management assessment and certification

Official Resources

• Official Assessment Platform - Certification queries, self-assessment tools
• Certification Authority - Standards and announcements

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Five Maturity Levels

DCMM classifies data management capability maturity into five levels, from low to high:

Level	Name	Description
Level 1	初始级 (Initial)	Basic data management with ad-hoc processes
Level 2	受管理级 (Managed)	Defined processes and project-level management
Level 3	稳健级 (Robust)	Standardized processes across organization
Level 4	量化管理级 (Quantitatively Managed)	Measurable, data-driven management
Level 5	优化级 (Optimizing)	Continuous optimization and innovation

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Eight Capability Areas

The DCMM framework comprises 8 core capability domains:

1. 数据战略 (Data Strategy)

• Strategic planning and alignment
• Data policy development
• Resource allocation and investment

2. 数据治理 (Data Governance)

• Governance structure and organization
• Roles and responsibilities
• Data stewardship and accountability

3. 数据架构 (Data Architecture)

• Data model design
• Data distribution and integration
• Technical architecture

4. 数据标准 (Data Standards)

The Data Standards capability area ensures data consistency and usability across the organization through standardized definitions, formats, and management practices. It comprises four key capability items:

4.1 业务术语 (Business Terminology)

• Definition: Approved descriptions of business concepts within the organization
• Components:
  • Chinese name (中文名称)
  • English name (英文名称)
  • Term definition (术语定义)
  • Business context and usage examples
• Purpose: Ensure consistent business language across departments and systems
• Key Activities:
  • Terminology collection and standardization
  • Cross-departmental review and approval
  • Maintenance and version control
  • Integration with system development

4.2 参考数据和主数据 (Reference Data & Master Data)

• Reference Data: Basic data used for classification, coding, and identification
  • Examples: Country codes, industry classifications, status codes
  • Characteristics: Relatively stable, shared across systems
• Master Data: Core business entity critical data
  • Examples: Customer, product, supplier, employee data
  • Characteristics: High-value, frequently accessed, shared across applications
• Management Focus:
  • Unified identification and coding rules
  • Data source management (single source of truth)
  • Data synchronization and consistency
  • Change control and impact analysis

4.3 数据元 (Data Elements)

• Definition: The smallest unit of data with specific meaning
• Components:
  • Object class (e.g., “Customer”)
  • Property (e.g., “Name”)
  • Representation (e.g., “Text string”)
• Standardization Requirements:
  • Naming conventions
  • Data type definitions
  • Length and format specifications
  • Value range constraints
• Purpose: Foundation for data standardization and system integration

4.4 指标数据 (Indicator Data)

• Definition: Data used to measure specific targets or objects in business analysis
• Components:
  • Indicator name (指标名称)
  • Time dimension (时间维度)
  • Numerical value (数值)
  • Calculation methodology
  • Business context and thresholds
• Examples:
  • Financial indicators: Revenue, profit margin, ROI
  • Operational indicators: Order fulfillment rate, customer satisfaction
  • Strategic indicators: Market share, brand awareness
• Management Requirements:
  • Unified calculation definitions
  • Data source traceability
  • Verification and validation processes
  • Regular review and updates

Data Standards Management Process

1. Planning: Identify standardization requirements and priorities
2. Development: Create standards through collaborative processes
3. Review: Validate standards with business and technical stakeholders
4. Approval: Formalize standards through governance processes
5. Implementation: Apply standards in systems and processes
6. Maintenance: Update standards to reflect business changes
7. Compliance: Monitor adherence and enforce standards

Business Value

• Consistency: Unified data language across the organization
• Integration: Foundation for system integration and data sharing
• Quality: Reduced data ambiguity and errors
• Efficiency: Streamlined data management and communication
• Analytics: Reliable basis for business intelligence and decision-making

5. 数据质量 (Data Quality)

• Quality rules and metrics
• Data quality assessment
• Quality improvement processes

6. 数据安全 (Data Security)

• Security policies and controls
• Access management
• Data privacy and compliance

7. 数据应用 (Data Application)

• Data analytics and BI
• Data services and APIs
• Value creation from data

8. 数据生存周期 (Data Lifecycle)

• Data collection and creation
• Storage and maintenance
• Archival and disposal

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Benefits of DCMM Assessment

• Identify gaps: Discover current data management status and existing problems
• Benchmark comparison: Compare with industry averages and best practices
• Targeted improvement: Receive specific guidance for capability enhancement
• Digital transformation: Support organizational data-driven transformation
• Government incentives: Access to subsidies and certifications (varies by region)
• Market recognition: Demonstrate data management capabilities to stakeholders

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Current Trends (2024-2026)

DCMM 2.0 Update (2025-2026)

• New Standard: GB/T 36073-2025 released December 2025, effective July 2026
• Major Enhancement: Expansion from 8 to 9 capability domains with addition of Data Assets domain
• Capability Items: Increased from 28 to 33 process areas
• Strategic Focus: Covers full data lifecycle from “resource → asset → element” transformation
• Market Alignment: Supports data要素市场化配置 reform and data asset accounting

2024-2025 Adoption Patterns

• Financial services: Banks and institutions actively pursuing DCMM certification
• Regional subsidies: Multiple Chinese provinces offer financial incentives (¥100K-¥500K)
• Integration: Growing alignment with international data management frameworks (DAMA-DMBOK)
• Enterprise adoption: Increasing recognition as critical benchmark for digital transformation
• Government requirements: DCMM certification becoming prerequisite for certain contracts and projects

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Related Concepts

• DAMA-DMBOK - International data management framework
• Data Governance - Organizational data management practices
• Data Maturity Model - General maturity assessment approaches

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References

Official Resources

• DCMM Official Assessment Platform - Certification queries, self-assessment tools
• DCMM Certification Authority - Standards and announcements
• GB/T 36073-2018 National Standard (Data management capability maturity assessment model)
• GB/T 36073-2025 National Standard (DCMM 2.0, effective July 2026)

Data Standards Resources

• DCMM Data Standards Process Areas - DCMM Level 2 data standards requirements
• Understanding DCMM Data Standards - Comprehensive guide to business terminology, reference data, master data, data elements, and indicator data
• DCMM Complete Interpretation - Full framework overview with 8 capability domains and 28 process areas

DCMM 2.0 Update

• DCMM 2.0 National Standard Upgrade - First inclusion of “Data Assets” domain, effective July 2026