1. Data Management Capability Assessment Model
Intersection of data management best practice and the reality of financial services operations
Synthesis of research and analysis among practitioners since formation of EDM Council (case studies, regulatory pressure, collaborative research)
Socratic method to ensure that DCAM is practical and in line with the core principles of data management (philosophy meets practical reality)
Aligns with organizational mandate (practical, works in real world, understandable by non-specialists, based on collaboration, structured for continual improvement)
Standard criteria for evaluation of capability … linked to benchmarking … providing evidence of adoption of control environment
Created based on practical experience
Capabilities orientation: Not done; In process (low, medium, high); Capability achieved; Capability enhanced
Each Category is defined by a set of Capabilities and Sub-Capabilities
Each Sub-capability is evidenced by a series of capability objectives
Component (8)
Capability (36)
Sub-Capability (112)
Objectives (306)

2. DCAM Construction Data Management Capability Assessment Model
Sub-Capability
Objectives
Artifacts
Required
Capability Type
Data-related (identification, common language, classification, data quality, lineage, compounding process)
Organization-related (governance, funding, data management program)
Ecosystem-related (IT, operations, security, privacy, finance)
Organizational
Purpose Statement
(what is and why important)
Management Context
(definition and introduction)
Elaboration
Specific Goals
Core Questions
Informative

3. DCAM Scoring

4. Data Management Capability Assessment Model
Data Governance
Component
Capability
Data governance structure is created.
Content governance is defined
Policy/standards are written and approved
Program governance controls are in place
Technology governance is aligned
Cross-organizational enterprise data governance is aligned
Program governance is operational
Policy and standards have been reviewed and approved by senior executive governing bodies
Policy and standards have been reviewed and approved by relevant program stakeholders
Policy and standards are written and complete
Objectives
Sub-
Capability
Policy/standards are developed in collaboration with (business, technology and operations)
Policy and standards are complete and verified
Policy and standards are in alignment with Data Management Strategy
Policy and standards have been shared and reviewed by relevant stakeholders.
Feedback from stakeholders incorporated into the final version of the policy and standards
Policy and standards have been validated and approved
Policy/standards submitted to the organizational governance mechanism for evaluation
Policy and Standards have been approved
Sample Artifacts
Documented policy and standards (cross-border, privacy, data acquisition, entitlement, access, data retention, quality control process, training, data content, data format)
Sample Artifacts
Distribution lists, thread (stakeholders and governance committee), stakeholder approvals, meeting notices and minutes, governance committee approval, internal memos

5. DCAM Applied Components
Advice from an Audit Perspective
Sub-Capability Statement
Essential Questions
DCAM Description and Objectives
Required Artifacts as Evidence of Adherence
Scoring Guidance

6. Data Management Capability Assessment Model (DCAM)
DCAM is structured to define and measure capability (i.e. the definition of precisely what is required to develop, implement, govern and sustain data management). It has been organized into eight categories, 36 capability areas, 112 sub-capabilities and 306 objectives:
Data Management Strategy: defines the framework for the data management program including the goals, objectives and scope; why it is important; how it will be organized, funded, governed and practically implemented
Data Architecture: focuses on the core concepts of “data as meaning” and how data is defined, described and related; the identification of logical domains of data; identification of the underlying physical repositories; and the governance procedures necessary to ensure the control and appropriate use of data
Business Case & Funding Model: provides the justification for the data management program including the rationale for the investment; the costs, benefits, risks and expected outcomes; the mechanism used to ensure sufficient allocation of resources; and the approach used to measure costs and contributions from implementation of the data management program
Technology Architecture: addresses the relationship of data with the physical IT infrastructure needed for operational deployment including how data is acquired, stored, distributed and integrated across the organization
Data Quality: establishes the concept of fit-for-purpose data; defines the processes associated with establishing data control; and addresses the implementation of governance mechanisms for management of the data manufacturing chain of supply
Data Management Program: identifies the organizational requirements needed to stand up a sustainable data management program including the operational framework to ensure sustainability and authority as well as the mechanisms to establish and confirm stakeholder engagement related to program implementation
Control Environment: defines the data lifecycle process and how data content management is integrated into the overall organizational “ecosystem”
Data Governance: defines the rules of engagement necessary for program implementation including the definition of policies, procedures and standards as the mechanisms for alignment among stakeholders

7. Organizational: Data Management Strategy
Blueprint defining the goals of the data management program and how the organization will approach implementation
Establish the framework for the data management program including how it will be defined, organized, funded and governed
Clearly define the goals, objectives and scope of the program to ensure alignment and commitment from the critical stakeholders
Articulate how the program will be practically implemented including how it aligns with business value and organizational objectives

8. Organizational: Data Management Business Case & Funding Model
The justification for the data management program including how it is funded and evaluated
Rationale for the investment in data management including the costs, benefits, risks and expected outcomes
Mechanism to ensure allocation of sufficient capital and methodology for cost allocation
Approach used to measure costs and contributions from the data management program

9. Organizational: Data Management Program
Establish the data management function and ensure that it operates effectively within the culture of your organization
Establish and confirm stakeholder engagement along with the process to ensure that the data management program is implemented
Clear understanding of the meaning of data “content” management and why it is essential to the operations of the organization
Establish an operational framework to ensure sustainability and authority of the data management program

10. Organizational: Data Governance
Rules of engagement necessary for implementation of the data management program
Establish the structure and operating model needed to establish lines of authority and integrate the DMP into the corporate hierarchy
Define, implement and enforce data management policies, procedures, guidelines and standards
Measure and monitor the effectiveness of the data management program and ensure sufficient resources have been allocated

11. Data Content: Data Architecture
Design, management, implementation and maintenance of the precise meaning of the information
Governance procedures to ensure the maintenance, control and appropriate use of data and its harmonization to business meaning
Identification of (and agreement on) the logical domains of data
as well as location of the underlying physical repositories
Definition of the precise contractual meaning of all data attributes including how they relate to one another and their expression as metadata

12. Data Content: Integration into Technology Architecture
Physical architecture and how data is acquired, stored, distributed and integrated across the organization
Strategy, architecture, roadmaps and governance on the physical IT infrastructure needed to support data management objectives
Definition of the allowable tool stacks (i.e. BI, ETL, storage) needed to support implementation of the data management strategy
Strategies and approaches to address operational risk, business continuity and disaster recovery

13. Data Content: Data Quality Program
Approach to ensure that data consumers trust and have confidence in the data as fit for their intended purpose
Create a profile of the current state of data quality, identify areas that need remediation and cleanse against approved business rules
Implement data quality governance mechanisms and processes for management of the data manufacturing chain of supply
Establish the goals, approaches, dimensions and plans of action to ensure data content supports the objectives of the organization

14. Ecosystem Coordination: Data Control Environment
Coordination of the components of the data lifecycle across the data ecosystem of the organization
Identification and documentation of the end-to-end data flows and reverse engineering of data compounding processes (formulas, derived calculations, etc.)
Integration of data management into the organizational “ecosystem” and alignment with other enterprise control functions
Objectives and capabilities of the data management program are embraced and adopted throughout the organization – creating a control environment

15. EDM Council’s 20 Rules of Data Management
Copyright © 2015 EDM Council Inc.
EDM Council’s 20 Rules of Data Management
Confidence Rule: We have an endorsed data management strategy that is meaningful to business users
Owner Rule: We have a senior executive (with authority) in charge of the data management program
Alignment Rule: Stakeholders understand (and buy into) the need for the data management program
Communication Rule: We do a good job communicating the value proposition and operational implications of the data management program
Business Case Rule: The business case and funding model for the data management program is established and operational
Metrics Rule: We do a good job of measuring the costs and benefits of the data management program
Policy Rule: The data management program has the authority to enforce adherence to policy
Resources Rule: The data management program has enough resources to be sustainable
Accountability Rule: The data management governance and accountability structures are operational
Responsibility Rule: The roles and responsibilities of data “owners” and “stewards” are assigned
Enforcement Rule: Data policies and standards are implemented and enforced
Ontology Rule: The business meaning of data is defined, governed and harmonized across repositories
CDE Rule: Critical data elements are identified, verified and managed
Data Domains Rule: Logical categories of data have been defined and catalogued
Capability Rule: The data management program is aligned with technical and operational capabilities
Profiling Rule: Data in existing repositories has been profiled, analyzed and graded
DQ Control Rule: Data quality control procedures, business rules and measurement criteria are operational
Root Cause Rule: The root cause of data quality problems are identified and corrected
Lineage Rule: End-to-end data lineage has been identified across the full data lifecycle
Ecosystem Rule: Data management collaborates with existing enterprise control functions

16. Six Areas of Excellence
All Data has been Profiled, Analyzed and Graded (fit-for-purpose)
Critical Data Elements are Identified and Managed
Resources Needed for Sustainability Approved and Acquired
Logical Data Domains have been Declared and Sanctioned
Data Quality Control Points and Business Rules are Implemented
IT Governance is Aligned with the Objectives of the Data Program
The Data Program is Aligned with IT and Operational Capabilities
ODM is Effectively Collaborating with other Control Functions
Owners & Stewards are in Place with Defined Roles and Responsibilities
End Users are Adhering to the Policies and Standards
Stakeholders Understand the Need for the Data Management Program
Business Meaning of Data is Defined, Harmonized & Governed
Policies & Standards are Documented, Implemented and Enforced
Endorsed Strategy and Meaningful to Business Users
Selling the Value Proposition & Operational Implications
Funding Model Established and Sanctioned
Costs & Benefits are Measured and Reported
Governance Structure & Authority is Implemented and Communicated
Root Cause Analysis is Performed and Corrected
Program Established with Authority to Enforce Adherence
End-to-End Lineage is Defined Across the Full Data Lifecycle
Stakeholder Commitment
Governance Implementation
Content Infrastructure
Data Quality Control
Working With IT
Data Management Program
Eco-system
S T R A T E G Y
C O M M U N I C A T I O N
A U T H O R I T Y
A L I G N M E N T
F U N D I N G
M E T R I C S
R E S O U R C E S
E C O S Y S T E M
O R G A N I Z A T I O N
S T E W A R D S
P O L I C Y
A D H E R E N C E
M E A N I N G
C D E
D O M A I N S
L I N E A G E
P R O F I L I N G
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R O O T – C A U S E
A L I G N M E N T
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