This article explores differences in the DAMA-DMBOK and DCAM approaches to the data management definition.



In the first article of the series, I explored the Yin-Yang type of relationship between data management and data governance. In this article, I would like to take a closer look at data management, as it is the ‘Yang force’ in this duality, meaning it has an active and leading role within the relationship.

The two following statements express the idea of the Yin-Yan duality between data management and data governance definition.

  1. To move on with your data management/data governance initiative, the company must first specify the definition, scope, and constituting components of data management (source).
  2. The data governance framework has to conform to the specification of data management (source).

The first statement forms the basis for this article. I will give some practical advice, most relevant to companies that have either already started their data (management) initiatives or plan to (re)assess their current approach. Let us consider the key decisions regarding data management a company should make. The decisions are listed in the order in which they should be taken.


Decision 1. Consider data management as a business capability.

As discussed in the previous article, the DAMA-DMBOK and DCAM models describe data management as a business function or capability. Unfortunately, I could not find clear definitions of these concepts in either of the guides.

My approach entirely relies on the Open Group definitions of a business capability and a business function.

Let’s begin with defining ‘business capability:

‘A business capability is 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.’ 1

Following this approach, data management is a business capability. Specific purposes of this capability are:

  • to be in control of data and information resources
  • to optimize data and information value chain
  • to ‘deliver, control, protect, and enhance the value of data and information assets.’ 2

The choice of the purpose of your data management capability is in your hands.

A business capability only answers the question: of what a business should do. The following practical decision to make is how.


Decision 2. Specify your viewpoint on the data management capability.

In the previous article, I stated that ‘there are at least two view perspectives on data management that are dependable on the relationship between data management and IT function. The first one is the broad perspective: from the enterprise viewpoint 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.’

So, before proceeding, a company should specify which viewpoint they choose. The choice will influence the specification of the constituent components of the data management capability. Each of these constituent components is also business capabilities on a lower detailed level. Generally speaking, each business capability can be split into a set of capabilities on a lower level. For convenience, let’s call them ‘sub capabilities’ of data management.


Decision 3. Layout the key components of the data management capability.

Once, I received a challenging request: to explain in 15 minutes how to implement data management using the well-known Data Management Body of Knowledge (DAMA-DMBOK2). With all due respect to the great job performed by the guide’s authors, I found it quite challenging to explain the logic behind the model. So, to deal with the situation, I analyzed different data management and governance models(source). This analysis inspired me to create a new, simple, and compact (meta) data management model, called the ‘Orange’ model3, that explains the essence of data management. You can see its last version in Figure 1.

The universal metamodel of data management

Figure 1. The universal metamodel of data management

The core object of data management and the metamodel are data and information resources.

The ‘Orange’ standard metamodel highlights the data management business sub-capabilities from two perspectives: the ‘broad’ and the ‘narrow’ ones. The ‘narrow’ perspective involves data management sub-capabilities to be performed by data management professionals. These sub-capabilities are data management framework and data quality. Enterprise architecture includes business, data, application and technology architecture, and data modeling sub-capabilities. Data architecture, data modeling, and partly application architecture belong to the ‘narrow’ perspective. The ‘broad’ perspective is extended with sub-capabilities that usually belong to the domain of information technology (IT), security, and some parts of enterprise architecture, such as business, application, and technology architecture. You can learn more on the subject you can consult in my book, The “Orange” Model of Data Management.

Once the decision about the viewpoint on the data management capability is made, you already know which sub-capabilities will form data management.

The model also clearly demonstrates the duality between data management and data governance. The model supports my statement in the previous article: ‘All deliverables of data governance such as rules and roles are dependable on the definition, scope and constituent components of data management.’

Now let us evaluate the key value proposition data management will deliver to different stakeholders.


Decision 4. Outline the key value propositions of data management and link them to components of the data management capability.

Data management’s key aim is to deliver value propositions to groups of stakeholders, such as internal management or external authorities. Each value proposition can be provided by implementing a data and information value chain. Such a value chain becomes operational when data management sub-capabilities enable and support it. A schematic example of such an approach can see in Figure 2.

Figure 2. An example of a relationship between data management value proposition, information value chain, and enabling components of data management capability

One of the customers of data management is internal management. Data management delivers the following value propositions to this stakeholder: enhancing decision-making, boosting customer acquisition, and accelerating new product development. The key partners are parties that provide information, data management, and IT professionals that enable data transformation. The data and information value chain should be in place and operational to provide these value propositions. Four general actions constitute the value chain:

  1. Specify the stakeholders’ information and data requirements.
  2. Design and/or optimize data and information value chain.
  3. Process data into information.
  4. Analyze and use information.

Some data management sub-capabilities from the ‘narrow’ (green rectangles) and ‘broad’ perspective (grey rectangles) enable the value chain.

Readers interested in explaining the method can find it in The “Orange” Model of Data Management.

As soon as you are ready with the design of the data management capability, it is time to implement it in the form of a data management function.


Decision 5. Establish a data management function.

According to the TOGAF9.2, a business function ‘delivers business capabilities closely aligned to an organization, but not necessarily explicitly governed by the organization.’ (source) Eurostat provides the following definition of a business function: ‘business functions are the activities carried out by an enterprise.’ (source) At, a business function is a ‘process or operation that is performed routinely to carry out a part of an organization’s mission.’ (source)

To align all these definitions, I will say the following: to implement data management capability, a company must set up a data management business function. One of my solid professional beliefs is that the data management function should be independent. Unfortunately, I have not seen it in my practice yet. I did experience the following two situations: the first when the data management function is a part of the IT domain, and the second when data management becomes part of finance. Finance often takes on complete accountability for data as they are critical information delivery units.


Decision 6. Outline the goals of the data management function at each organizational level.

As I mentioned in the previous articles, there is no aligned definition of data management (see attachment 1). But the diversity of these definitions has led to an interesting observation. There are three key reasons to implement data management as data management professionals have seen it:

  • to enhance decision-making
  • to be in control of the data
  • to ensure data lifecycle.

There are also different perceptions of what data management is:

  • concepts
  • practices
  • programs
  • policies
  • processes
  • plans

If you map these opinions to organizational levels, you get a handy tool to analyze your data management function, as shown in Figure 3.

Figure 3. Data management goals and tools at different organizational levels.

This approach allows you to establish data management ambitions and goals pragmatically.

As soon as you find the place for the data management function in your organizational structure, data governance finally starts playing its role in this Yin-Yang duality we have discussed.

The following article will look closer at the key deliverable of data governance: data management framework.

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



  1. The Open Group. Open Group Guide. Business Capabilities. Prepared by the Open GroupArchitecture Forum Business Architecture Work Stream. The Open Group, March 2016: p.3
  2. DAMA International. DAMA-DMBOK: Data Management Body of Knowledge, Second Edition. Bradley Beach, N.J.: Technics Publications, 2017: p.17.
  3. Steenbeek, Irina. The “Orange” Model of Data Management. Data Crossroads, 2019.