This article discusses the news: potential alignment between DAMA-DMBOK and DCAM®
In May 2025, I had the privilege of delivering a workshop at the DGIQ and EDW2025 conference—one of the most prominent global events in the data management community, hosted by Dataversity.
In a series of upcoming posts, I will share key takeaways from the conference. However, today’s article is dedicated to one exceptional moment: a milestone discussion between Dama International and EDM Council on the potential alignment of two foundational frameworks—DAMA-DMBOK and DCAM®.
This conversation represents a significant development for the global data management community. The potential alignment between DAMA-DMBOK2 and DCAM® appears to be part of the natural evolution of the data management discipline—an effort grounded in incremental improvements and practical adaptations. However, the impact of this alignment could be revolutionary. By establishing a common foundation across two of the most influential frameworks, it has the power to reshape industry practices, reduce fragmentation, and accelerate the professionalization of data management worldwide.
This article will discuss the following topics:
- The importance of the alignment of these frameworks for:
- The whole community
- Me personally
- The meaning and approach of the alignment:
- The goal of each framework
- The challenges to aligning frameworks
- The role of the DM community
The Importance of Alignment
The Data Management Community and Framework Landscape
DAMA-DMBOK is one of the products of DAMA International, “a not-for-profit, vendor-independent, global association of technical and business professionals dedicated to advancing the concepts and practices of information and data management.” DAMA-DMBOK serves as an entry point into the field and forms the foundation for obtaining the Certified Data Management Professional (CDMP) certification.
DCAM® (Data Management Capability Assessment Model), developed by the EDM Council, is another widely recognized framework. The Enterprise Data Management Council is the member-driven trade association dedicated to elevating data management and analytics. While DCAM was initially developed for the financial industry, its structured, pragmatic approach has led to its adoption across a broad range of sectors.
Both frameworks are designed for the same primary audience: business and data management leaders and professionals dedicated to advancing the concepts and practices of information and data management and leveraging data and analytics to achieve better outcomes.
In practice, organizations within the data management community often face the choice between DAMA-DMBOK, DCAM®, other frameworks, or developing a custom internal framework. These frameworks are typically used to define the scope, design, and implementation of a data management function and to measure its maturity and performance.
While DAMA-DMBOK and DCAM® approach data management from different perspectives, they are frequently used to achieve similar goals. Despite their differences, both frameworks serve as tools for establishing and maturing data management capabilities.
I have been tracking the trends in the use of these frameworks since 2021. Figure 1 below illustrates their application in the design and implementation of data management frameworks within organizations.
Figure 1: Trends in using industry frameworks for setting up data management functions.
The DAMA-DMBOK2 has consistently remained the most widely adopted framework, with its usage showing only minor fluctuations over the years. In contrast, DCAM® has retained a more niche position, with relatively limited adoption across the same period.
However, interpreting these results requires caution. Even when organizations report using a particular industry framework, they frequently tailor it to meet their specific needs. Such adjustments often involve modifying or limiting the scope of capabilities. As a result, even those who formally adopt DAMA-DMBOK2 typically implement a version that is significantly adapted to their context.
Similar trends can be observed in the use of these frameworks for conducting maturity assessments, as illustrated in Figure 2.
Figure 2: Trends in using industry frameworks for measuring data management maturity.
The use of data management maturity models has shown clear shifts in recent years. DAMA-DMBOK2 continues to be widely adopted, with steady growth in usage from 2021 to 2025, reaching its peak in the most recent year. In contrast, DCAM® has experienced a gradual decline in adoption, indicating a decreasing preference among organizations.
Notably, the use of internally developed maturity models has grown significantly, surpassing all other options by 2025. This trend reflects an increasing move toward customized, organization-specific approaches for measuring data management maturity.
The statistics presented above highlight several essential challenges for the data management community:
- Benchmarking across organizations remains difficult, as DAMA-DMBOK2 and DCAM® are based on fundamentally different models, and no shared benchmarking database currently exists.
- Organizations that choose to work with both frameworks face additional complexity in aligning their structures, terminology, and assessment methods.
In this context, the potential integration or alignment of DAMA-DMBOK2 and DCAM® holds significant promise. Such an effort could help address, at minimum, these two critical challenges, offering greater clarity, consistency, and comparability for organizations implementing data management frameworks.
My Personal Professional Reward
I began my data management career 16 years ago, building on a strong foundation in both finance and IT. Over the years, my journey has been closely connected to the evolution and application of industry frameworks like DAMA-DMBOK and DCAM®. Below are several milestones that illustrate this path:
- Implementing a Governance Framework for Data Management
When I first set out to implement a data management governance framework, I had no prior knowledge, experience, or internal support. The DAMA Wheel served as my guiding reference. At that time, I was unaware of the DAMA-DMBOK publication and spent six years developing and implementing core data management capabilities through self-guided research, mostly relying on online resources and developing own methodology.
- Mapping DAMA-DMBOK, DCAM®, and Other Models
In 2019, I conducted my first mapping of DAMA-DMBOK and DCAM®, presenting the results at the Enterprise Data World Conference. Later, I expanded this work by comparing these frameworks at the artifact level. This exercise led me to several important conclusions:
- The frameworks reflect fundamentally different perspectives and lack a unified view of data management.
- Using multiple models for different purposes requires prior effort to align them, ensuring consistency and reliable results.
- None of the existing frameworks offered a fully integrated implementation methodology.
- Developing the O.R.A.N.G.E. Data Management Framework
To address these gaps, I developed the O.R.A.N.G.E. Data Management Framework, which introduces several innovations:
- Alignment of a data management model and a maturity model based on the business capability approach from the TOGAF® Standard
- An integrated implementation method that explicitly connects core data management capabilities such as governance, architecture, data quality, and metadata management
- A metamodel for a data management artifact knowledge graph, built upon a metamodel of data lineage
The O.R.A.N.G.E. framework is designed to be framework-agnostic and can support the implementation of any industry data management model.
For these reasons, the current initiative from DAMA International and the EDM Council to explore alignment between DAMA-DMBOK and DCAM® is particularly meaningful to me. It validates many of the issues I have addressed in my work and acknowledges the impact of my publications on this initiative.
The Meaning and Approach of the Alignment
The Goal of Each Framework
One of the key outcomes of the session at the EDW2025 was a more precise articulation of the respective goals of DAMA-DMBOK and DCAM®. This clarification is essential for understanding how the two frameworks can complement one another rather than compete. The distinction in their focus areas and intended use cases forms the foundation for any potential alignment.
Figure 3 presents a comparative overview of the core goals of each framework.
Figure 3: The goal of various frameworks.
According to DAMA International, the DAMA-DMBOK is a principle-based framework that defines the knowledge areas and best practices of data management. In essence, it describes WHAT data management should look like.
DCAM®, on the other hand, is designed to assess the maturity of core data management capabilities. Its primary strength lies in enabling benchmarking, both internally and at a global scale. It also helps identify the key artifacts needed to achieve a desired level of maturity. In this sense, DCAM® answers the question of WHY certain data management initiatives are necessary.
However, the most pressing question for practitioners—HOW to scope, design, and implement a data management framework—often remains unanswered by either model. This is where practitioner-developed frameworks, such as the O.R.A.N.G.E. Data Management Framework (DMF), help close the gap by offering practical implementation guidance.
The Challenges of and Potential Approaches to Aligning Frameworks
The initiative to align two leading industry frameworks—DAMA-DMBOK and DCAM®—holds tremendous promise for the data management profession. However, to realize its potential, we must acknowledge and address several critical challenges:
- Fundamentally different data management models
DAMA-DMBOK defines data management through a set of Knowledge Areas, reflecting a conceptual and practice-based structure. In contrast, DCAM® adopts a business capability model, organized into seven core components, comprising 31 capabilities and 106 sub-capabilities. These structural differences make direct alignment complex and require thoughtful mapping.
- The presence of other frameworks and guidelines
DAMA-DMBOK and DCAM® are not the only models in the landscape. For example, the EDM Council also promotes the Cloud Data Management Capabilities (CDMC™) framework, which follows a different structure than DCAM®. Additionally, Gartner has substituted the concept of data management with the term “data governance,” introducing further inconsistency in how foundational concepts are defined. Aligning DAMA-DMBOK and DCAM® could serve as an essential first step in creating a shared language for the industry.
- Adoption of customized internal models
Many organizations have already adapted either DAMA-DMBOK, DCAM®, or both to suit their specific needs. As a result, introducing an integrated perspective will require not only technical alignment but also considerable change management to foster acceptance and adoption across the community.
The Role of the Data Management Community
Across industries and geographies, data management professionals face similar challenges and apply similar technologies. However, the lack of a unified language often hampers collaboration and slows progress. This fragmentation, what could be described as a “Babylonian” state of terminology, limits our ability to share lessons learned and build collective knowledge.
Recognizing data management as a foundational enabler for AI and other digital transformation initiatives should motivate the global community to support and contribute to the alignment of industry frameworks. A harmonized approach will not only promote consistency but also help practitioners focus on what matters most: delivering business value through trusted data.
