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2.2 Operating Models & Structures

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DAMA Certified Data Management Professional CDMP

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1 Chapter 1: Exam Overview & DMBoK Framework

1.1 CDMP Exam Details & Strategy 1.2 The DMBoK Framework 1.3 Data Management Organization & Roles

2 Chapter 2: Data Governance (Part A)

2.1 Data Governance Principles & Drivers 2.2 Operating Models & Structures 2.3 Policies, Standards, and Procedures

3 Chapter 2: Data Governance (Part B)

2.4 Data Stewardship 2.5 Governance Metrics & Implementation

4 Chapter 3: Data Modeling and Design (Part A)

Section 3.1: Conceptual, Logical, and Physical Models Section 3.2: ERD Notations & Relational Modeling Section 3.3: Normalization

5 Chapter 3: Data Modeling and Design (Part B)

3.4 Dimensional Modeling 3.5 Naming Standards & Modeling Best Practices

6 Chapter 4: Data Quality (Part A)

Section 4.1: Data Quality Dimensions Section 4.2: Data Quality Policy & Rules Section 4.3: DMAIC & DQ Improvement Lifecycle

7 Chapter 4: Data Quality (Part B)

4.4 Root-Cause Analysis & Remediation 4.5 DQ Metrics, Tools, and Reporting

8 Chapter 5: Metadata Management (Part A)

5.1 Business, Technical, and Operational Metadata 5.2 Metadata Lineage & Impact Analysis 5.3 Repository Architectures

9 Chapter 5: Metadata Management (Part B)

5.4 Standards and Semantics 5.5 Metadata Governance & Integration

10 Chapter 6: Master and Reference Data Management

6.1 Master vs Reference Data 6.2 MDM Repository Architectures 6.3 Match-Merge & Identity Resolution 6.4 Reference Data Governance & Standards

11 Chapter 7: Data Warehousing and Business Intelligence

7.1 DW Architectures: Inmon vs Kimball 7.2 Data Warehousing Storage & Load Patterns 7.3 BI Delivery & OLAP 7.4 Data Marts & Enterprise Data Warehouses

12 Chapter 8: Data Architecture

8.1 Enterprise Data Architecture & Zachman Framework 8.2 Data Flow Diagrams & Value Chains 8.3 Architecture Frameworks & Standards

13 Chapter 9: Document and Content Management

9.1 Records Management & ECM 9.2 Retention, Disposal, and E-Discovery 9.3 ARMA GARP Principles & Taxonomies

14 Chapter 10: Data Integration and Interoperability

Section 10.1: Integration Patterns & Technologies Section 10.2: Replication & Virtualization Section 10.3: Canonical Models & Integration Standards

15 Chapter 11: Data Security

Section 11.1: Security Principles: CIA Triad & Privacy Section 11.2: Access Control & Encryption Section 11.3: Security Regulations & Standards

16 Chapter 12: Data Storage and Operations

Section 12.1: Database Technologies & DBA Roles Section 12.2: Backup, Recovery, and High Availability Section 12.3: Performance Tuning & Operations

17 Chapter 13: Big Data and Data Science

13.1 Big Data V's & Technologies

18 Chapter 14: Data Ethics

14.1 DMBoK Data Ethics Framework

19 Chapter 15: Data Management Process and Maturity

15.1 Maturity Assessment Frameworks

2.2 Operating Models & Structures

Key Takeaways Explain This Topic AI

The federated (hub-and-spoke) operating model balances enterprise consistency (the hub) with local business unit agility and ownership (the spokes).

The Data Governance Council (DGC) represents the highest strategic decision-making authority, establishing enterprise policy and resolving cross-functional disputes.

Data Owners are business leaders with ultimate accountability for specific data domains, including access approval and data quality thresholds.

The Chief Data Officer (CDO) is a strategic business role that bridges corporate strategy and technical data execution.

Last updated: July 2026

  • Operating Models and Structures in Data Governance
  • 1. The Importance of Data Governance Operating Models

An operating model defines how decision-making authority, accountability, and roles are distributed across an enterprise. Designing an effective operating model is one of the most critical steps in establishing a sustainable Data Governance (DG) program. There is no 'one-size-fits-all' model; the choice depends on corporate culture, geographic distribution, regulatory requirements, and the degree of business unit autonomy.

The operating model determines how Data Stewards , Data Owners , and the Data Governance Council (DGC) interact to define, monitor, and enforce data policies.

2. Four Core Operating Models

The DAMA DMBoK2 describes several primary models for organizing data governance:

Operating Model Structural Description Key Advantages Major Disadvantages Typical Use Case

Centralized A single central team (Data Governance Office) defines all rules, policies, and standards for the entire organization. • High consistency; • Clear accountability; • Standardized processes • Can become a bottleneck; • Slow response to local needs; • High risk of business resistance Monolithic organizations, highly regulated mid-size entities.

Decentralized (Distributed) Individual business units or departments govern their own data independently with little to no central coordination. • Highly responsive to local needs; • Low central overhead; • High local buy-in • Severe data silos; • Inconsistent definitions; • Duplicate tools and efforts Diversified holding companies or highly fragmented startups.

Federated (Hub-and-Spoke) A central council (Hub) defines enterprise standards and policies, while business units (Spokes) execute them locally. • Balances consistency with local autonomy; • Scalable • Complex to coordinate; • Requires continuous alignment and mediation Large, multinational enterprises with shared customer/product data.

Hybrid Combines centralized policy-making with decentralized execution, often using dual-reporting lines for data stewards. • Maximizes local domain knowledge; • Centralized oversight • Dotted-line reporting conflicts; • Ambiguous authority lines Large enterprises transitioning from decentralized to centralized structures.

3. The Governance Hierarchy: Roles and Committees

Data Governance is structured across three primary levels of management: strategic, tactical, and operational. +-----------------------------------+

  • | Strategic: Executive Sponsor |
  • | & Data Governance Council (DGC) |
  • +-----------------------------------+
  • |
  • +-----------------------------------+
  • | Tactical: Steering Committee |
  • | & Lead Data Stewards |
  • +-----------------------------------+
  • |
  • +--------------------------------------+--------------------------------------+
  • | | |
  • +---------------------------+ +---------------------------+ +---------------------------+
  • | Operational: Data Owner | | Operational: Data Steward | | Operational: Custodian |
  • | (Accountable for Domain) | | (Business Defs & Rules) | | (IT Systems & Storage) |
  • +---------------------------+ +---------------------------+ +---------------------------+
  • Executive and Strategic Level

Executive Sponsor : A C-suite leader (such as the Chief Executive Officer or Chief Financial Officer) who champions the program, secures funding, and aligns data governance with corporate strategy.

Chief Data Officer (CDO) : The executive lead responsible for the strategic vision of data management. The CDO typically chairs or co-chairs the DGC and acts as the bridge between business strategy and IT execution.

Data Governance Council (DGC) : The highest decision-making authority in the governance structure. Composed of senior business leaders (division heads, vice presidents) and the CDO. The DGC is responsible for:

Defining the enterprise data strategy.

Approving corporate data policies and standards.

Prioritizing major data initiatives and resolving funding requests.

Serving as the final escalation point for cross-functional data disputes.

Tactical Level

Data Governance Steering Committee : Composed of mid-level management representatives and lead data stewards. They review drafted policies, coordinate data quality initiatives, and manage the enterprise data glossary.

Data Governance Office (DGO) : A dedicated team of full-time governance professionals who facilitate meetings, maintain documentation, provide training, and track governance KPIs (Key Performance Indicators).

Operational Level

Data Owners : Business leaders accountable for a specific data domain (e.g., Customer, Product, Asset). They have the authority to authorize data access, approve definitions, and make final decisions regarding data quality thresholds.

Data Stewards : Subject matter experts (SMEs) from the business units who define data elements, write data quality rules, and work to resolve operational data issues.

Data Custodians (Technical Stewards) : IT professionals (database administrators, data engineers, security administrators) responsible for the physical storage, lifecycle operations, and technical security of the data assets.

The Data Governance Office (DGO) Facilitation Role

The DGO does not make policies; it facilitates their creation, adoption, and tracking. Think of the DGO as the program management office (PMO) for data governance. It provides templates, manages tools (such as metadata repositories or data catalogs), conducts data management maturity assessments, and reports compliance status to the DGC.

Escalation Path and Conflict Resolution

A critical function of the operating model is the escalation path. For example, if the Sales division defines 'Customer' differently than the Finance division, and the lead stewards cannot reach agreement at the tactical level, the conflict escalates to the DGC. The DGC, representing enterprise-wide business leaders, makes the final, binding decision. This structure prevents gridlock and ensures alignment with the corporate strategy.

Exam Tip: The CDO establishes the vision, the DGC sets the policy, the Data Owner is accountable for domain-specific data, the Data Steward defines business rules, and the Data Custodian manages the physical implementation. The CDO is a strategic business role, not a technical IT role.