Introduction

The Data Management Capability Assessment Model defines the scope of capabilities required to establish, enable, and sustain a mature data management discipline. It addresses the strategies, organizational structures, technology, and operational best practices needed to successfully drive Data Management. It addresses the tenets of Data Management based on an understanding of business value combined with the reality of operational implementation.

Overview

Why Does Data Management Matter?

Data management is a critical capability for any organization that is striving to become data-centric and building a data-driven future. For those new to the concept, this introduction provides an overview of what data management entails, along with key industry trends, such as the growing role of artificial intelligence.

At its core, data management aims to ensure that data is high-quality, well-governed, and trusted, supporting business operations, decision-making, and compliance. While traditionally driven by defensive needs like regulatory requirements, data management today plays an increasingly offensive role helping organizations reduce costs, improve operational efficiency, and unlock value through data analytics.

To succeed, data management leaders must both demonstrate and defend the value of their initiatives through consistent, measurable outcomes. They also need to foster a culture of shared responsibility and engagement to build and maintain trusted data.

Looking ahead, advancements in data technologies, including all forms of AI, promise to reshape and automate core infrastructure processes, including data management itself. As data volumes grow exponentially, scalable and automated data management will become a necessity, grounded in strong foundational principles.

Diagram 0.1: Building a Data-Driven Future

What is Driving Data Management Change?

Since the release of DCAM v2, several significant changes and emerging considerations have reshaped the data management landscape. One of the most impactful shifts is the rapid evolution of Artificial Intelligence. Organizations across industries are responding in various ways to the explosion of Artificial Intelligence capabilities and increased computing power. While the long-term implications for data management remain uncertain, key areas such as data discoverability, provenance, and trust are already proving vital in supporting advanced analytics. The implementation of DCAM capabilities continues to provide a solid foundation, but organizations must remain attentive to the evolving AI environment and its influence on data practices.

Regulation remains another major force in the evolution of data management. Originally propelled into prominence by the global financial crisis of 2007–2008 and subsequent concerns over data quality, data management has since expanded beyond compliance to focus on business value creation. However, renewed regulatory focus, particularly around risk data aggregation, data security and privacy, ethical data access, use and outcomes including AI, demands continued vigilance from data leaders.

A noteworthy trend is that analytics now not only consume data but also generate new data, often automatically. This data, whether created through machine learning, training of large language models, predictive analytics, or other artificial intelligence practices, must still adhere to data management and data quality standards. DCAM best practices remain fully applicable across these areas, ensuring consistent governance and integrity.

As data volumes continue to grow and demands on Data Management teams expand, manual processes are becoming unsustainable. Data Management “at scale” or the ability to leverage “automation” in support of data management efforts is increasingly evident. Innovative technologies and architectures, such as data fabric, data mesh, large language and small language models, real-time data processing, metadata solutions, and data self-service tools, offer opportunities to streamline and automate enhanced data management activities. Generative AI holds promise for tasks like data discovery, data quality assessment, metadata and architecture management, and even education and training in data and data management. Critically, all advancements must be prioritized based on their ability to deliver measurable value to the organization.

Data sharing and data self-service are also gaining momentum, supported by emerging technologies that allow simultaneous data and metadata access by both humans and machines. This dual-access model may become a central goal in improving data usability and accessibility.

Finally, data ethics has evolved to become a foundational data requirement. This shift emphasizes the need for data management capabilities to align closely with the organization’s ethics framework regarding the access, use, and consumption of data, as well as the consideration of their outcomes. Chief Data Officers must ensure that ethical considerations are embedded in all aspects of data management, and that data is addressed thoughtfully within broader organizational values.

Challenges for Data Management

Data management faces a range of complex and persistent challenges that require strategic vision, leadership, and sustained organizational commitment. One of the most significant hurdles is the sheer scale and complexity of legacy data assets, which are often sprawling and fragmented. Addressing these issues requires strong sponsorship from senior leadership, adequate budget allocation, and visible support for a clear data strategy, a defined Data Management operating model, and a compelling value proposition.

Because data management programs must be permanent and sustainable, Data Management Organizations must carefully manage expectations, particularly overoptimistic demands for quick results or simplistic solutions. Change management is a key component of any data management program, yet many falter due to a lack of human capability across all levels of the organization. This includes insufficient leadership support, weak change enablement, and a lack of commitment to best practices in data roles, accountability, and cross-organizational data sharing.

A sustainable data ecosystem relies on the active involvement of everyone in the organization, both permanent and temporary staff. The Data Management Organization must therefore cultivate a shared understanding of data responsibilities and foster a positive, inclusive data culture. However, many organizations struggle with severe data fragmentation, where vast amounts of data are delivered by hundreds of internal and external sources, stored in isolated systems, or aggregated informally with limited visibility or data control. This leads to many data challenges including but not limited to data that is not trusted, complete, or requires complex mapping, cross-referencing, and data reconciliation.

In parallel, understanding legacy business processes and demystifying highly complex data environments is an ongoing necessity. Data Management Organizations must focus on delivering incremental, measurable value rather than pursuing overly ambitious goals or unattainable perfection. Developing essential partnerships, especially with the Business, Technology and associated architecture functions, can encounter resistance, but collaboration and consensus are non-negotiable for effective design and implementation of data solutions.

Another significant challenge lies in achieving alignment around the business meaning of data within its usage context. Reaching agreement on terminology and the governance process for maintaining definitions can be particularly difficult, especially when critical business applications rely on multiple existing systems. Rather than enforcing a universal naming convention, a more practical approach is to harmonize data definitions based on legal, contractual, or business meanings. It is also more effective to focus on defining business concepts clearly, documenting transformation logic, and capturing real-world relationships between data. Once these foundations are established, existing systems, glossaries, dictionaries, and repositories can be cross-referenced to a common understanding.

Establishing a data risk and controls framework that aligns with the organization’s broader risk management strategy is another vital but complex task. This involves creating and applying a consistent data risk taxonomy. Moreover, organizations must grapple with the integration of vast volumes of both legacy and newly generated unstructured data. Managing this data is essential to mitigate privacy, regulatory, and legal risks, and often requires advanced tools to identify and remove stale or inappropriate data

Finally, enforcing data ownership and accountability can be a difficult cultural shift when the concepts are new and poorly understood. Successful adoption demands sustained communication, strong education programs, and visible leadership support to embed these principles across the organization.

Why Do I Need Data Management

Data management is essential because data is a foundational asset that drives an organization’s business capabilities, operations, and governance processes. The goal of data management is to maximize the measurable value of data while ensuring that everyone across the organization understands its importance. Data Management creates value by establishing governance structures, policies, and standards that make data knowledge accessible and support data-related activities throughout the organization. The Data Management Organization, often led by a Chief Data Officer, is responsible for enabling a collaborative and effective data ecosystem, where individuals understand both their own data responsibilities and how their roles interact with others. To support this, the Data Management Organization must develop the right strategy, roles, governance, communication, and a strong data architecture foundation. However, while it oversees and orchestrates the data ecosystem, the Data Management Organization is not the sole owner or expert of all data. That responsibility lies with the Business teams who create, transform, and use the data daily. Without coordinated data management, organizations risk fragmentation, where inconsistent terminology, vague definitions, and isolated data models from separate systems lead to confusion, inefficiency, and lost value.

Using a framework like DCAM provides a structured, industry-recognized foundation for establishing and assessing data management practices. DCAM defines what best practice data management looks like, offering a comprehensive view of the capabilities needed to manage data effectively, without prescribing exactly how each organization should implement those capabilities. It supports organizations in building a consistent, sustainable approach to data governance, quality, and strategy, while remaining adaptable to evolving technologies and industry demands, such as the rise of artificial intelligence. By using DCAM, organizations can benchmark their data management practices, identify gaps, and prioritize improvements, ultimately enabling more trusted, accountable, and valuable use of data across the enterprise.

DCAM: A Framework for Sustainable Data Management

A complex set of data management capabilities is required to achieve a data control environment. The Data Management Capability Assessment Model is a framework for executing a robust, sustainable Data Management function. It is also an essential tool for ongoing assessment and benchmarking of an organization's data management capabilities.

Diagram 0.2: DCAM Framework

The DCAM Framework consists of seven core components and one optional component. The first component, Data Strategy, Data Management Strategy and Data Management Business Case and the second component, the Data Management Program & Funding, are
foundational to the other five core components.

The next four core components of the DCAM Framework, Architecture – Business, Data & Technology, Business Data Knowledge, Data Quality Management, and Governance - Data & Data Management Program are the execution components.

The final core component is the collaboration activity, Data Management Operations, Risk and Control. It is here that the execution components are put into operation by the data producer to bring a defined set of data into control and make it available to data consumers at a point in time that is either real-time or a period end.

The seven core components include 28 capabilities and a total of 80 sub-capabilities. The definition and scope of each component are presented below.

The eighth component, Analytics Management, is optional and is relevant where an organization would like to assess the capabilities of the Analytics function along with Data Management. This component has 6 capabilities and 21 sub-capabilities.

DCAM: The Scope of the Eight Components

1.0 Data Strategy, Data Management Strategy & Data Management Business Case

The Data Strategy, Data Management Strategy & Data Management Business Case component defines how data and data management capabilities are linked to top level business objectives and embedded into the operations of the organization. It articulates the long-term value and vision for data, the data management capabilities, and identifies the stakeholders that must be aligned to achieve the organization’s business objectives with data.

• Establish and maintain a documented Data Strategy, specifying the data content and usage, and Data Management Strategy needed to support the business, leveraging DCAM to articulate data management capabilities.
• Align the Data Strategy and Data Management Strategy with the business strategy, objectives, and priorities, including prioritization of data based on its criticality to the business.
• Engage with key stakeholders to articulate and define the current state and future state target for data management capabilities using an assessment tool such as DCAM.
• Prioritize the goals of the Data Management Strategy and establish a deployment roadmap and timeline for implementation.
• Establish the justification and business rationale for managing data through the organization's comprehensive data management initiative. The business cases should highlight the value of data and advocate investment in data management capabilities, whether for improvement projects or ongoing operations and practices.

2.0 Data Management Program & Funding

The Data Management Program & Funding component is a set of capabilities to manage the Data Management Organization. These organizational structures include resource requirements and a full range of Program Management Office activities such as execution of program management, stakeholder management, funding management, change activities, communications, training, and performance measurement. The Funding Model within the Data Management Program is designed to provide the mechanism to ensure the allocation of sufficient capital needed for program implementation, its long-term success, and sustained and adoption. It also defines and describes the methodologies used to measure both the costs and the organization-wide benefits derived from the Data Management initiative.

• Establish a Data Management Program function to implement the Program Management Office capabilities within the Data Management Organization.
• Facilitate the design and implementation of sustainable business-as-usual Data Management processes and tools across the components and their capabilities.
• Establish roles and responsibilities related to the Data Management capabilities that align with the organizational structure and execute in a Data Management Organization.
• Define the Funding Model, secure and monitor funding, and institute cost, and benefits tracking aligned to the Business Case.
• Establish the Data Management execution roadmap with supporting project plans to build upon the high-level Data Management Strategy roadmap.
• Engage each stakeholder across the data ecosystem as appropriate to their roles in resource alignment, funding, communications, training and skill development.
• Manage the Data Management initiative by monitoring and socializing Data Management performance metrics.
• Ensure that the Data Management Program governance is integrated into Data Governance structure and process.
• Identify organizational roles and responsibilities and develop engagement activities to support data management strategies/objectives.
• Develop Data Management communication and change management abilities to affect organization-wide behavior and cultural change.

3.0 Architecture – Business, Data & Technology

The Architecture – Business, Data & Technology component focuses on establishing an integrated architecture as a foundation for best practice data management. Ensuring collaboration among business, data, and technology architectures is essential for achieving business objectives.

• Establish architecture policy and standards to guide business, data, and technology architecture as a foundation for data management across the organization.
• Establish the roadmap for structured and integrated architecture processes (business, data and technology) to support data management execution across the organization.
• Secure the alignment of Data Management with business, data and technology architecture, and strategy.
• Design and implement sustainable data architecture processes with due collaboration to address the data and data management requirements as defined as outputs from the business architecture process.
• Establish the data architecture approach to define and design data architecture to achieve the business objectives and needs for data.
• Identify and inventory the organization’s data needed to support the business requirements.
• Develop and validate data domains, data models, authoritative sources, and provisioning points.
• Ensure that Business Architecture, Data Architecture and Technology Architecture governance is integrated in the Data Governance structures and aligned with business and technology governance activities.

4.0 Business Data Knowledge

Business Data Knowledgeaddresses methods and practices for building and continuously maintaining an organization’s data awareness and shared understanding of its data. The desired result is a unique data ecosystem supporting a positive data culture embraced by the organization’s employees, each with a clear understanding of their own and others’ responsibility for data.

• Establish a formal data education and training program for Data Management.
• Collaborate with Data Management stakeholders to design and implement sustainable processes for defining business terms aligned with Data Architecture blueprints and models.
• Establish a sustainable metadata management approach and program.
• Establish a permanent repository of information and data knowledge accessible by the enterprise.

5.0 Data Quality Management

Data Quality Management component encompasses a set of capabilities to implement data profiling, business-driven quality evaluation, quality control rule development, monitoring, defect management, root cause analysis, and data issue remediation. These capabilities allow the organization to execute data quality processes across the data life cycle to ensure and control that data is fit for its intended purpose.

• Establish a Data Quality Management function.
• Work with Data Management Program Management Office to design and implement sustainable business-as-usual processes and tools for Data Quality Management.
• Perform Data Quality Management processes against the organization’s prioritized data including profiling & grading, rule building, ongoing measurement, defect management, root cause fix, remediation.
• Ensure that Data Quality Management and Data Quality Management Governance are integrated into Data Governance.
• Ensure that Data Quality Management and Data Quality Management Governance are integrated into Data Governance.

6.0 Governance – Data & Data Management Program

The Governance – Data & Data Management Program component is a set of capabilities that codify the structure, lines of authority, roles and responsibilities, escalation protocol, policy and standards, compliance, and routines for managing and facilitating processes across Data Management functions. This ensures authoritative decision-making at all levels of the organization.

• Establish a Data Governance function.
• Design and implement sustainable business-as-usual processes and tools for Data Governance.
• Define clear roles, responsibilities, and accountabilities for Data Governance resources.
• Establish a Data Governance structure with clear authority, decision-making responsibilities, stakeholder engagement, oversight, and ethical data use and outcomes.
• Monitor issue management (data, Data Governance, Data Management).
• Create and maintain Data Management policies, standards, rules, and procedures.
• Monitor adherence to policies, standards, rules, and procedures.
• Ensure that Data Governance aligns and collaborates with other relevant Data Management functions.

7.0 Data Management Operations, Risk & Control

The Data Management Operation, Risk, and Control component consist of capabilities that form a data management environment to oversee and control an organization’s data assets. It incorporates consistent life cycle practices, data risk and controls management, and necessitates organizational collaboration, accountability, and alignment with strategic objectives.

• Establish consistent data development life cycle practices to enable a sustainable business-as-usual environment for data management.
• Align the people, processes, technologies, and Data Management practices across the organization to achieve a coherent, end-to-end data ecosystem.
• Align Data Management with the organization’s over-arching risk management to establish data risk management approach and plan.

8.0 Analytics Management

The Analytics Management component is an integral part of data management. It is a set of capabilities required to structure and manage the Analytics activities of an organization. The capabilities align Analytics Management with Data Management in support of business and functional priorities. They address the culture, skills, platform, and governance required to enable the organization to obtain business value from analytics.

• Develop an Analytics Strategy that aligns with the overarching Business Strategy.
• Ensure the Analytics Strategy is aligned with the Data Management Strategy.
• Establish the Analytics Management function.
• Ensure clear accountability for the analytics created and for their uses throughout the organization.
• Work with Data Management to align analytics with all DCAM components, especially Data Architecture and Data Quality Management.
• Establish an analytics platform that provides flexibility and controls to meet the needs of the different stakeholder roles in the Analytics Operating Model.
• Design and deploy effective governance over the data analysis life cycle including tollgates for model reviews, testing, approvals, documentation, release plans, monitoring, and regular review of processes, adjustments and retiring.Monitor adherence to policies, standards, rules, and procedures.
• Ensure that Analytics follows established guidelines for privacy, data ethics, regulatory compliance, model bias, and model explainability requirements and constraints.
• Manage the cultural change and education activities required to support the Analytics Strategy.

DCAM Uses Cases

DCAM has multiple uses within an organization:

• As a framework
• As an assessment tool
• As an industry benchmark

DCAM as a Framework

When an organization adopts the standard DCAM framework they introduce a consistent way of understanding and describing data management. DCAM is a framework of the capabilities required for a comprehensive Data Management initiative presented as a best practice paradigm. DCAM helps to accelerate the development of the Data Management initiative and make it operational. The DCAM Framework:

• Provides a common and measurable Data Management framework
• Establishes common language for Data Management
• Translates industry expertise into operational standards
• Documents Data Management capability requirements
• Proposes evidence-based artifacts

DCAM as an Assessment Tool

To effectively use DCAM as an assessment tool requires the definition of the assessment objectives and strategy, planning for the assessment management, and adequate training of the participants to establish a base understanding of the DCAM Framework.

The assessment results translate the practice of Data Management into an objective measurement. The benefits afforded an organization from such an assessment include:

• Baseline measurement of the Data Management capabilities in the organization compared to an industry standard
• Quantifiable measurement of the progress the organization has made to operationalize the required capabilities
• Identification of Data Management capability gaps to inform a prioritized roadmap for future development aligned to the organization’s business requirements for data and Data Management
• Focused attention to the funding requirements of the Data Management initiative

DCAM as an Industry Benchmark

The EDM Association conducts Data Management industry benchmark studies every two to three years (as of the time of publication of the current document, in 2015, 2017, 2020, and 2023). Each benchmark is based on the capabilities defined in DCAM and thus can be used in comparison analysis for organizations conducting a DCAM assessment.

The industry benchmark is not restricted to organizations that are using DCAM. Input from a broader range of Data Management industry practitioners affords an enhanced perspective on the state of the Data Management industry.

DCAM Scoring Guide

The actual scoring guide used throughout DCAM is as follows. It is designed to assess the phase of capability attainment. It is not an assessment of the maturity or scope to which an organization has applied its capabilities. By design, the scoring is an even number to force a conscious decision by the rater and avoiding the tendency to select the midpoint of the scoring scale.

Table 0.5: DCAM Scoring Guide

How to use this Document – Anatomy of DCAM

The DCAM is organized into seven core components and one optional component. Each component is preceded with a definition of what it is, why it is important, and how it relates to the overall data management process. These definitions are written for business and operational executives to demystify the data management process. The core components are organized into 28 capabilities and 79 sub-capabilities, and the optional component is organized into 6 capabilities and 21 sub-capabilities. The capabilities and sub-capabilities are the essences of the DCAM Framework. They define the goals of data management at a practical level and establish the operational requirements that are needed for sustainable data management. Finally, each sub-capability has an associated set of measurement criteria. The measurements are used in an assessment of your data management journey.

• Component – a logical area of data management capabilities; used as a reference tool by the data practitioners who are accountable for executing the activities within that area
  • Introduction - high-level context for the component; used as a background for developing an understanding of the component by data practitioners
    • Definition – formal description of the component; used to support common data management understanding and language
    • Scope – a set of statements to establish the guardrails for what is included in the component; used to understand and communicate reasonable boundaries
    • Value Proposition – a set of statements to identify the business value of delivering the data management component; used to inform the varied business cases for developing the Data Management initiative
    • Overview – more detailed context and accounting at a practical level to establish an understanding of the operational execution required for sustainable data management; used as a guide by the respective data practitioners
    • Core Questions – high-level but probing inquiries; used to direct exploration of the data management component
    • Core Artifacts - things that are required to support the execution of the component; used for reference and to link to supporting best practice material when available
• Capability – a specification for an ability to perform a particular function used as a reference tool by the data practitioners who are accountable for the execution of the activities
  • Description - brief aggregate explanation of what is included in the sub-capabilities required to achieve the capability; used in the assessment process to inform the respondent of the scope of what they are rating
  • Description - brief aggregate explanation of what is included in the sub-capabilities required to achieve the capability; used in the assessment process to inform the respondent of the scope of what they are rating
• Sub-Capability – a specification for a task that is required to satisfactorily perform a specific capability; used as a reference tool by the data practitioners who are accountable for the execution of the activities
  • Description - identified goals or desired outcomes from executing the sub-capability; used as a basis for defining requirements for the data management process design
  • Objective - high-level context for the component; used as a background for developing an understanding of the component by data practitioners
  • Advice - more detailed but casual insight on the best practice how to execute the sub-capability with an audit review perspective; used by the data practitioner
  • Questions - inquiries to direct interrogation of the capability/sub-capability current-state; used by the data practitioner to inform a perspective of the assessment scoring
  • Artifacts - Required things or evidence of adherence; used for assessment and audit reference and to link to supporting best practice material when available
  • Scoring - insight for defining an assessment score; used when completing an assessment survey

What DCAM Measures

• Readiness: The status of the organization’s fitness to perform a specified capability (Not Initiated, Conceptual, Developmental, Defined, Achieved, and Enhanced)

What DCAM Does Not Measure

• Competency: A measure of the organization’s performance in satisfying stated objectives
• Maturity: The measure of the organization’s ability to integrate data management competencies across a specified scope that will sustainably achieve its objectives

DCAM Business Glossary

The EDM Association has developed a DCAM Business Glossary, which contains ~250 data management term names and definitions. DCAM v3.1 has applied these terms consistently across the document. Where there are terms defined in the glossary, the word or phrase is italicized and underlined in the text.

Acronym Glossary

DCAM Release Notes Summary

DCAM v3.1 – July 2025

The DCAM v3.1 release is considered a major release in that it has structural change. This release includes new capabilities, enhancements, and updates to v2 capabilities and some movement and consolidation of capabilities across the model. However, the core framework structure of components, capabilities and sub-capabilities, and the associated scoring model structure remain consistent. This allows the consumers of DCAM to map their v2.2 assessments to DCAM v3.1.

The intent is that there will not be a structural change to DCAM more often than once every 24 months, which will align to the two-year cycle on refreshing the industry benchmark data. In between major releases, there is the potential for one or more a minor release no more often than every six months. A minor release could include changes such as language, clarification, glossary alignment, and enhancements to the Component Introduction and reference to related EDMA best practice materials. Basic grammar or spelling mistakes will be fixed as uncovered and will not constitute a formal release.

The EDM Association will always support one version backward from the current major version (v3.x). In other words, v2.2 will be supported until such time that v3.2 is released.

The objective of the DCAM v3.1 refresh includes the following:

• Changes to the Framework Components to better present a logical flow of the capabilities
• Introduction of new concepts that have requirements for data and Data Management
• Enhancement to the content to support clarity, usability, and consistency in language, format, and presentation style

Changes to Framework Components

1.0 Data Strategy, Data Management Strategy & Data Management Business Case

• Clarified the concepts of Data Strategy (data content and use based on business requirements) and Data Management Strategy (how Data Management will support the Data Strategy)
• Placed business data requirements into a standalone capability to eliminate duplication

2.0 Data Management Program & Funding

• Clarified the funding and funding plan concepts for data management
• Consolidated stakeholder engagement concepts into data management roadmaps
• Introduced new capability, Data Management Change and Enablement
• Introduced new capability, Data Management Communications
• Training program concept to new component

3.0 Architecture – Business, Data & Technology (revised component)

• Combined DCAM v2 components, 3.0 Business & Data Architecture and 4.0 Data & Technology Architecture together focusing on the importance of collaboration across these disciplines

4.0 Business Data Knowledge (new component & content)

• Introduced three new capabilities, 4.1 Data Education Program, 4.2 Business Glossary and 4.3 Metadata Management

5.0 Data Quality Management

• General updates and clarification across the component
• Rearranged some capabilities and sub-capabilities presenting a more logical order

6.0 Governance – Data & Data Management Program

• Consolidated capability for policy and standards into a single sub-capability eliminating redundancy and conflict with the scoring model
• Restructured “Govern the Data” capabilities (v2 6.4, 6.5 and 6.6) to refocus on what data is governed and what the data is governed against (e.g., requirements, use, access, policy, standards)

7.0 Data Management Operations, Risk & Control

• Updated to include Data Management Operations including three new sub-capabilities, Data Development Life Cycle, Data Requirements Approach and data Provider Management
• Updated Data Risk as a capability

8.0 Analytics Management

• Consolidated v2 8.4 Analytics is Aligned with Data Quality into v3 3.1.
• Updated v2 8.7 consolidating several sub-capabilities and refocusing the capability on education and change management in v3 8.6.
• Clarified that this component is optional for an assessment. To be considered a full DCAM assessment, components 1.0 through 7.0 must be included. Organizations may choose to include component 8.0 if they wish to evaluate their analytic practices. However, it is not recommended to assess component 8.0 in isolation, as it is designed to evaluate how well data management and analytics functions align and collaborate within the organization.

Introduction of New Concepts

DCAM v3.1 introduced a total of eight new concepts and laid the foundation for future additions to DCAM. The eight new concepts that were added as capabilities or sub-capabilities in the model are:

• Data Management Change & Enablement
• Data Management Communications
• Data Education
• Business Glossary
• Metadata Management
• Data Development Life Cycle
• Data Provider Management
• Data Requirements Management

These additional concepts, combined with existing best practices, form the foundation for upcoming enhancements like Data Culture, which are planned for future releases.

Enhancements to the Content

• Scoring Redundancy and Inconsistency - In DCAM v2, several instances were identified where redundancy or inconsistency made the model potentially confusing or required assessors to interpret it subjectively. To address these issues and ensure a comprehensive review, the DCAM v3 Working Group conducted a thorough analysis of the model’s capabilities and sub-capabilities related to the scoring framework. As a result, they resolved areas of confusion, streamlining and clarifying the model to support more consistent interpretation.
• New terms added to the Data Management Business Glossary - Along with the rewrite and updates to the DCAM framework document, the DCAM v3 Working Group and teams identified new terms or update to terms that are contained in the EDM Association Data Management Business Glossary (DMBG). The DMBG is available in the EDMAs member Knowledge Portal, Data Management Business Glossary – EDMA Knowledge Portal. In this document the terms located in the DMBG are underlined and italicized in the text.