DCMM 2.0: Data Governance Shifts from Management to Asset Operations

The Data Management Capability Maturity Assessment Model (DCMM) 2.0, standardized as GB/T 36073-2025, took effect on July 1, 2026. While retaining the core framework of capability domains, capability items, and five maturity levels (Initial, Managed, Defined, Quantitatively Managed, Optimizing), the revision reorients data governance goals from "managing data" toward assetization, businessization, intelligence, and trustworthiness — making data a confirmable, evaluable, operable, and circulatable production factor.

Core Structural Changes

DCMM 1.0 contained 8 capability domains and 29 capability items. DCMM 2.0 adjusts to 9 domains and 33 items. The key changes are:

Capability domain count: 8 domains → 9 domains (data governance scope expanded)

New capability domain: No separate Data Asset domain → New Data Asset domain (data shifts from management object to asset object)

Data Application: Data Application → Data Application Circulation (from internal use to internal/external circulation)

Data Governance Communication: Data Governance Communication → Data Culture Building (from policy dissemination to organizational culture)

Data Security: Data Security Strategy, Data Security Management → Data Compliance Management, Data Security Protection (from internal management to compliance and risk protection)

Data Standards: Reference data and master data merged → Master data and reference data split (finer granularity in foundational data governance)

New capability items: No ownership, valuation, operations → Ownership management, value assessment, asset operations, external data management (data assetization and circulation become focal points)

Major Change: New Data Asset Capability Domain

DCMM 2.0 introduces a dedicated Data Asset domain with three capability items:

Ownership management

Value assessment

Asset operations

This is the most significant change. In DCMM 1.0, data was primarily an object to be governed, managed, analyzed, and served. In 2.0, data is explicitly positioned as an asset. Enterprises must now demonstrate not only that data is well-managed, but also answer:

<ol>
<li>哪些数据可以成为资产？</li>
<li>这些资产归谁管理？</li>
<li>价值怎么评估？</li>
<li>如何授权使用？</li>
<li>如何运营并释放价值？</li>
</ol>

Traditional governance activities — standards, quality, metadata, master data, lineage, security — remain important but become foundations supporting assetization and value release rather than endpoints.

Data Application Upgraded to Data Application Circulation

DCMM 1.0's Data Application domain covered data analysis, open sharing, and data services. DCMM 2.0 renames it to Data Application Circulation with items: data application, external data management, data opening, data services. This reflects real-world needs: enterprises increasingly require supply-chain data, public data, third-party data, and ecosystem partner data. The new external data management item addresses legal acquisition, quality evaluation, standard integration, and fusion with internal data. The focus shifts from building more reports or opening more APIs to enabling data to form services, products, and circulation capabilities under security and compliance premises.

Security and Compliance as Prerequisites for Circulation

DCMM 1.0's Data Security domain included data security strategy, management, and audit. DCMM 2.0 restructures to data compliance management, data security protection, and data security audit. This mirrors the evolving regulatory landscape: personal information protection, important data protection, cross-border data transfer, public data opening, and data product circulation scenarios have multiplied. Data security is no longer just internal policy, permission assignment, and audit — it is a precondition for data circulation and asset operations. Enterprises must prove data processing activities are compliant, secure, auditable, and responsive. As data opens, shares, transacts, and becomes service-oriented, clarity on source, classification, authorization boundaries, de-identification, encryption, access control, audit trails, and risk response becomes essential. Trustworthiness enables circulation rather than burdening it.

Other Notable Changes

From Communication to Culture

Data Governance Communication becomes Data Culture Building, indicating governance must foster data-driven decision habits, collaboration modes, and innovation awareness, not just transmit policies and standards.

Finer Data Standards

Reference data and master data, merged in 1.0, are split into separate items. Master data covers core business entities (customers, products, organizations, personnel, equipment); reference data covers classification enums (regions, industries, statuses, types). The split aligns with actual governance practice.

Maturity Evaluation Emphasizes Value Release

The five maturity levels remain, but 2.0 adds evaluation content on data asset awareness, data catalog, ownership registration, value assessment, authorized operations, data circulation application, AI technology application, and data industry ecosystem.

Terminology Updates

New terms include data culture, data asset, data asset operations, data catalog, key data elements, data processing, important data, data product, data destruction — confirming the shift from traditional data management to assetization, circulation, productization, and compliance.

Implications for Enterprises

DCMM 2.0 is not a checklist to add a few more policies or processes. It forces re-examination of governance objectives:

From Platform Delivery to Capability Formation

Many enterprises have built data platforms, mid-platforms, governance platforms, and accumulated metadata, standards, quality rules, and indicator systems. If these capabilities do not enter business processes, data products, and asset operations, they risk remaining at the platform construction layer.

From Managing Data to Operating Data

The new Data Asset domain requires answering identification, ownership, valuation, authorization, operations, and revenue distribution. These are not purely technical problems, nor can the data department solve them alone.

From Supporting Analysis to Supporting AI

AI raises governance requirements. AI-oriented governance must make business objects, relationships, rules, indicator definitions, data ownership, quality, and usage boundaries system-understandable, model-callable, and agent-executable. This aligns with ontology, industry semantic platforms, and AI Agent discussions.

Summary

DCMM 2.0's biggest change is not additional capability items but a goal shift:

From data management capability building

To data assetization, businessization, intelligence, and trustworthiness

Version 1.0 asked whether an organization could manage data. Version 2.0 asks whether it can identify data as assets, circulate data as factors, operate data as products and services, and continuously release value under security and compliance. Enterprises still following the old mindset of building standards, quality, metadata, and mid-platforms may find that insufficient. The signal is clear:

<ol>
<li>数据治理的下一阶段，不只是把数据管好；</li>
<li>而是让数据成为可以确权、评估、运营、流通和持续创造价值的资产。</li>
</ol>