Are you interested in getting familiar with the discrepancies between DAMA-DMBOK and DCAM?

CHECK OUT THE PREVIOUS ARTICLE OF THIS SERIES: Data Management & Data Governance 101: The Yin and Yang Duality

Although the terms ‘data governance’ and ‘data management’ are used in different industry reference guides, such as DAMA-DMBOK1 and DCAM2, in different contexts and sources, they have different definitions, meanings, and relationships with each other.3I chose these guides for comparison as they are the most trustworthy and most frequently used sources in the data management professional community.

Those who would like to have some  ‘fun’ and jump into the jungle of definitions provided by other prominent data-related organizations are welcome to look at the attachment at the end of this article.

In this article, I aim to explore the more profound discrepancies between DAMA-DMBOK and DCAM in the approaches and to discuss possible reasons for that.

You might ask: why is it so important? Assume two data management specialists talk to each other about data governance. One bases his opinion on the DAMA-DMBOK model, and the other – on DCAM. If they do not align their language upfront, their conversation will bring the same results as those achieved by the builders of the tower of Babel.

Let us look at the key discrepancies between DAMA-DMBOK and DCAM data management and governance models.


Discrepancy no. 1:  The nature of building blocks of data management models

According to the DAMA-DMBOK model, data management is a business function4, which scope is made up of different Knowledge Areas5. Unfortunately, I could not find any definition of ‘business function’ in any of DAMA’s publications. DAMA-DMBOK uses the DAMA Environmental Factors Hexagon model to describe the Knowledge Area. So, I assume that the key components such as ‘people, process, and technology’and their relationships are key components of the business function in the DAMA context. Yet, these are my assumptions.

DCAM bases its model on the concept of ‘capability.’ I could not define what ‘capability’ is and which components constitute a ‘capability.’ ‘The Data Management Strategy (DMS) is Specified and Shared’7 is one of the examples of a DCAM capability. Such a representation of a business capability does not correspond to a definition of a business capability provided by the Open Group: ‘a particular ability or capacity that a business may possess or exchange to achieve a specific purpose or outcome. Critically, a business capability delineates what a business does without attempting to explain how, why, or where the business uses the capability. […] The correct naming convention involves expressing the business capability as a noun (“this is what we do”) as opposed to a verb […]’8.

So, the DCAM ‘capability’ clearly does not fit the definition, and a ‘capability’ definition remains a mystery.

Conclusion: DAMA-DMBOK and DCAM base their data management models on the concepts of ‘Knowledge Area’ and ‘Capability’ correspondingly. These concepts are not clearly defined and are not compatible with each other.


Discrepancy no. 2: The list of data management model building blocks and their content

Continuing with further comparison, let us compare the components that constitute DAMA-DMBOK and DCAM data management models.

DAMA-DMBOK represents data management in the form of 11 Knowledge Areas. In Figure 1, you can see the famous DAMA Wheel9.

Figure 1. The DAMA Wheel.


The latest version (v2) of the DCAM model includes 7 components:10

Figure 2. Components of the DCAM model.


You can see that some components have similar names, such as ‘Data Governance,’ ‘Data Architecture,’ and ‘Data Quality (Management).’ Some of them are similar: for example, DCAM’s Data and Technology Architecture matches specific Knowledge Areas of DAMA-DMBOK, such as Data Integration & Interoperability, Data Warehousing & Business Intelligence. The rest of the components differ.

The differences go deeper into the content and deliverables of the data management components. Even if both models: DAMA-DMBOK and DCAM, have a component with the same name, this component has an entirely different meaning in the models. Look, for example, at the component ‘data governance.’

DAMA-DMBOK considers data (management) strategy, business case, and data management programs as deliverables of the data governance function. DCAM recognizes them as separate components of data management11. So, DAMA-DMBOK and DCAM discrepancies exist.

Conclusion: components of the data management model of DAMA-DMBOK and DCAM are only similar to some extent, and the nature of this conceptual difference is unclear.


Discrepancy no.3: The relationship between data management and IT function

DAMA-DMBOK, in its first edition, clearly stated that a data management professional organization was part of an Information Technology (IT) organization.12 The DAMA-DMBOK data management model fully reflected this assumption and did not change in the second edition. I recognize this as the fact that DAMA-DMBOK looks at data management from the viewpoint of the enterprise.

DCAM, in my opinion, propagates the viewpoint of data management professionals. The model describes tasks to be done by data management professionals. The IT function belongs to the data control environment. Data management professionals should ensure collaboration with IT professionals.

Conclusion: At least two perspectives on data management depend on the relationship between data management and the IT function. The first is the broad perspective: from the enterprise on the lifecycle of data circulating in a company. This is the approach taken by the DAMA-DMBOK model. The second perspective is the narrow one: from the viewpoint of tasks to be done by data management professionals. The DCAM model follows this approach.


Discrepancy no.4: The relationship between data management and data governance

In my first article of the series, I have already discussed the discrepancy between definitions of ‘data management’ and ‘data governance’ in DAMA-DMBOK and DCAM models.13 The key conclusions of the analysis were:

  • According to the DAMA-DMBOK model, data governance exercises authority, control, and shared decision-making about data management. Data management develops, executes, and supervises plans, policies, and programs regarding the maintenance of data assets and supporting their lifecycle.

The DCAM model votes that data governance sets up rules for data management. In turn, data management implements these rules into practice.

The key difference in approaches is that in the DAMA-DMBOK vision, the data management function develops and implements rules. In contrast, in the DCAM view, data governance sets the practices for data management.

  • There are no clearly aligned definitions of data governance and management, their roles, and relationships in the data management community. The definitions, roles, and relationships between these two concepts change in different contexts.

Now, as I have described all discrepancies regarding data management and data governance between the DAMA-DMBOK and DCAM models, I would like to share with you the question that bothered me a lot:

Suppose data governance establishes the rules and the framework in which data management operates. Why do DAMA-DMBOK and DCAM models consider data governance a constituent component of data management, not a separate function?

But then I realized that such a relationship best demonstrates the Yin-Yang dualism between data governance and management. The essence of this dualism is in the following statement.

Conclusion: Any general talk about data governance or its consideration as a separate function has no sense. Data governance deliverables, such as rules and roles, depending on the definition, scope, and constituent components of data management.




  1. DAMA International. DAMA-DMBOK: Data Management Body of Knowledge, Second Edition. Bradley Beach, N.J.: Technics Publications
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  3. /2019/09/22/data-management-data-governance-101-the-yin-and-yang-duality/
  4. DAMA International. The DAMA Dictionary of Data Management, Second Edition: Technics Publications, 2011, p.78.
  5. DAMA International. DAMA-DMBOK: Data Management Body of Knowledge, Second Edition. Bradley Beach, N.J.: Technics Publications, p.35.
  6. DAMA International. DAMA-DMBOK: Data Management Body of Knowledge, Second Edition. Bradley Beach, N.J.: Technics Publications, p.35.
  7. //
  8. The Open Group. Open Group Guide. Business Capabilities. Prepared by the Open GroupArchitecture Forum Business Architecture Work Stream. The Open Group, March 2016, p.2.
  1. DAMA International. DAMA-DMBOK: Data Management Body of Knowledge, Second Edition. Bradley Beach, N.J.: Technics Publications, p.35.
  2. //
  3. DAMA International. DAMA-DMBOK: Data Management Body of Knowledge, Second Edition. Bradley Beach, N.J.: Technics Publications, p.69.
  4. Mosley, Mark., and Michael Brackett. First Edition is the DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK Guide). Bradley Beach, N.J.: Technics Publications, 2010, p.5.
  5. /2019/09/22/data-management-data-governance-101-the-yin-and-yang-duality/


P.S. Here is the attachment that I mentioned before in the very beginning:

For more insights, visit the Data Crossroads Academy site: //