A quick look at existing data management metamodels
If you are going to implement the data management function, you need to define what data management model you would like to use, and your first step is to look at existing industry reference guides. Your first choice will definitely be the DAMA-DMBOK Guide by DAMA International1. This is the most well-known and most frequently used guide in the data management community. If you work for a financial institution, your preference might go to DCAM by the EDM Council2. The third leading guide is TOGAF3, which is focused on Enterprise Architecture but also intercepts some data management areas like data, application, and technology architecture. All guidelines are not “off-the-shelf solutions,” though. To some extent, they are similar, but let us take a closer look at various conceptual differences.
The key conceptual differences between different DM metamodels
I have compiled the metamodels of the two guides I mentioned in the following figure: DAMA-DMBOK by DAMA international and DCAM by Enterprise Data Management Council.
Let’s briefly discuss each of the metamodels separately.
DAMA-DMBOK 2 defines the metamodel of data management in the form of DAMA Knowledge Areas, represented in the DAMA Wheel form. This model of data management is industry agnostic. In my article ‘DAMA-DMBOK in a nutshell,’ I briefly analyzed the guide and showed a few challenges that you could have used their model.
The DCAM model is organized into eight core capabilities. This model was developed primarily for financial institutions, but, in my opinion, the model is actually rather generic and can be used in other industries as well.
Comparing the DAMA-DMBOK 2 and DCAM metamodels
From my point of view, there are several conceptual differences between DAMA-DMBOK and DCAM data management models:
- The role of IT function.
DAMA-DMBOK considers data management as a part of IT, while DCAM separates data management from IT by recognizing IT as a part of a collaborative ecosystem.
- The building blocks of the metamodel.
DCAM describes data management as a set of business capabilities. At the same time, DAMA-DMBOK defines data management as a set of Knowledge Areas and uses the term’ business function’ to specify their content.
- The scope of data management function.
If you take a closer look at the two models, you will see that some functions and capabilities are very similar, while others are entirely different.
And this is just the beginning. If you dive into the DM business functions and capabilities content, you will face a lot of differences in interpretations and perspectives.
Differences in content: business functions and capabilities.
The figure above shows that some of the business functions/ capabilities seem to be quite similar. But is it really the case? A deeper analysis will show that, very often, these capabilities are only identical in their names but differ a lot when you look at their content and expected deliverables. Here are a few examples.
DAMA-DMBOK 2 and DCAM have pretty different opinions on the deliverables of Data Governance.
For example, the Data Management strategy, Business Case, and Program are considered deliverables of Data Governance in the DAMA-DMBOK 2 model. DCAM considers these elements as separate capabilities.
DCAM considers data domains, critical data elements, data domain taxonomies, data classification, and requirements as data governance tasks. In contrast, DAMA-DMBOK 2 considers data architecture and modeling elements.
Data architecture and Data modeling and design
The key deliverables of Data architecture, as per DAMA-DMBOK 2, are data flows and data lineage. Simultaneously, Data modeling is seen as a tool that could specify data requirements by developing conceptual, logical, and physical data models.
At the same time, DCAM recognizes the development of these models as an outcome of Data architecture.
Furthermore, DAMA-DMBOK 2 recognizes the business glossary as a deliverable of Data Governance, while DCAM sees it as a deliverable of Data architecture.
Is it a problem that different data management guides speak different languages? Of course, everyone has a right to free speech, but from my point of view, such differences could cause significant challenges, such as:
- Unclear communication between data management professionals due to the ambiguity of the interpretations of commonly used terms
- Complications in comparing the level of maturity of data management between different companies
- Difficulty in making choices that each company needs to make while setting up data management.
The examples I have presented in this article are only a few of the significant challenges I have found while comparing the two major data management works. There are lots of other interesting points of discussion within the two guides. Have any striking differences or similarities between DAMA-DMBOK 2 and DCAM caught your attention? Please share them in the comments section below!
For more insights, visit the Data Crossroads Academy site: //academy.datacrossroads.nl.
- DAMA International. DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition. Technics Publications, 2017.
- EDM Council. “Data Management Capability Assessment Model, DCAM 1.2.2. (Assessor’s Guide)” EDM Council, 2 Dec. 2018, www.edmcouncil.org/dcam.
- The Open Group. “TOGAF Version 9.1”, The Open Group Standard no. G116, 2011.
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