1.3 Data Management Organization & Roles

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

1.3 Data Management Organization & Roles

Key Takeaways Explain This Topic AI

Data Stewards represent the business and focus on data quality, glossary definitions, and rules, whereas Data Custodians represent IT and focus on storage, security, and backups.

The Chief Data Officer (CDO) focuses on data content and strategic business value, differing from the Chief Information Officer (CIO) who focuses on systems, hardware, and infrastructure.

The Data Governance Steering Committee (DGSC) is a cross-functional group of business leaders responsible for policy approval and conflict resolution.

The Federated (hub-and-spoke) organizational model is recommended by DAMA for large enterprises because it balances enterprise consistency with local business flexibility.

Last updated: July 2026

1.3 Data Management Organization & Roles

Implementing an effective data management capability requires establishing clear organizational structures, formalizing data governance bodies, and defining precise operational roles. Without clearly assigned responsibilities, data quality degrades, compliance risks rise, and business units resolve conflicts ad hoc. The DAMA-DMBOK2 framework emphasizes that data management is a shared responsibility across the entire organization, requiring a partnership between the business (who understands the data's meaning and value) and IT (who manages the physical systems).

Data Stewards vs. Data Custodians

The most critical role distinction tested in the CDMP exam is the difference between a Data Steward and a Data Custodian (often referred to as a technical custodian or data provider).

Data Stewardship : The formal accountability for managing data assets on behalf of others to ensure the data is fit for purpose, compliant, and accessible. Data stewards represent the business.

Data Custodianship : The technical administration of the physical databases, pipelines, and infrastructure that store and move data. Data custodians represent IT.

Dimension Data Steward (Business) Data Custodian (Technical/IT)

Primary Focus Business context, data definitions, and data quality. Systems, physical database design, and storage architecture.

Key Activities - Defines data quality rules and thresholds.; - Documents terms in the Business Glossary .; - Resolves data definition conflicts across departments. - Implements database schemas and storage tables.; - Conducts database backups and disaster recovery.; - Enforces data access control lists and encryption.

Organizational Alignment Aligned with business business units (e.g., Finance, HR, Operations). Aligned with IT departments, Database Administrators (DBAs), and Security.

Core Accountability That data is accurate, complete, and defined consistently. That data is secure, highly available, and physically protected.

Exam Tip: Remember that business units do not "own" data. In the DAMA framework, data is an enterprise asset. Therefore, a Data Steward does not "own" the data; they steward it on behalf of the entire enterprise.

Key Governance Bodies & Executive Roles

Data governance requires structured bodies to approve policies, resolve conflicts, and secure resource funding. The DAMA framework identifies several critical roles and committees:

1. Chief Data Officer (CDO)

The Chief Data Officer is the executive leader responsible for the organization’s overall data strategy, data governance program, and maximizing the business value of data assets. Unlike traditional IT roles like the Chief Information Officer (CIO), the CDO focuses on the content and value of the data , rather than the systems and hardware that run the data. The CDO typically reports to the Chief Executive Officer (CEO), Chief Operating Officer (COO), or in some organizations, the CIO.

2. Executive Sponsor

The Executive Sponsor is a high-level business leader (often the CEO or a C-suite executive) who champions the data governance program at the executive level. The primary responsibilities of the Executive Sponsor are to secure budget and resources, align the program with the enterprise business strategy, and provide the political support necessary to drive adoption of data management standards across resistant business units.

3. Data Governance Steering Committee (DGSC)

The Data Governance Steering Committee is a cross-functional governing body composed of senior business leaders (often vice presidents or directors representing Finance, Marketing, Operations, and IT). The DGSC acts as the legislative branch of data governance. Its primary responsibilities include:

Setting the strategic priorities for data management projects.

Approving data policies, standards, and metrics.

Acting as the final escalation point to resolve cross-departmental data definition or priority conflicts.

4. Data Governance Office (DGO)

The Data Governance Office is the operational department that runs the day-to-day operations of the data governance program. Led by a Data Governance Director or Manager, the DGO coordinates the activities of Data Stewards, maintains the central business glossary and metadata tools, tracks governance metrics, and reports progress to the DGSC.

Organizational Models for Data Management

When structuring data governance, organizations choose from three primary structural models based on their size, culture, and regulatory requirements:

1. Centralized Model

In a Centralized Model , a single enterprise data office and a central team of stewards manage data definitions, quality standards, and policies for the entire organization.

Pros : Ensures high consistency, eliminates redundancy, and simplifies policy enforcement.

Cons : Can create operational bottlenecks, slows down department-level decision-making, and may lack the deep domain knowledge required to govern specialized business data.

2. Decentralized (Distributed) Model

In a Decentralized Model , individual business units or departments manage their data independently with no central coordination.

Pros : Highly responsive to local department needs, fast execution, and utilizes specialized domain knowledge.

Cons : Creates severe data silos, leads to inconsistent definitions (e.g., "customer" defined differently by sales and finance), and results in duplicate software tools and effort.

3. Federated (Hybrid) Model

The Federated Model (often called the hub-and-spoke model) is the most widely recommended approach for large enterprises. A central Data Governance Office (the hub) sets overall policies, standards, templates, and metrics. Individual business units (the spokes) execute these policies using local data stewards who adapt the central standards to their specific business contexts.

Pros : Balances corporate consistency with departmental flexibility. Scalable and maintains business domain relevance.

Cons : Requires complex coordination and clear communication channels to prevent conflict between the hub and spokes.