During a meeting a few days ago, I have received a challenging request: explain in 15 minutes how you can implement data management using the well-known Data Management Body of Knowledge (DAMA-DMBOK2). At that moment I recalled the quote by Steven Hawking: ‘the greatest enemy of knowledge is not ignorance, it is illusion of knowledge’.

The main challenges of DAMA

I have been working in the area of data management for already 8 years now. The DAMA-DMBOK 2 guide is really a great source of knowledge of different data management related areas gathered and summarized by a strong professional team. The guide provides you with the famous ‘DAMA Wheel’ which explains the key eleven Knowledge Areas. The Environmental Factors Hexagon model is the foundation for describing each Knowledge Area and includes, for example, deliverables, activities, tools etc. But still, the DAMA data management model has its own Achilles heel, which consists, in my opinion, of the following:

  • If you dive into one knowledge area, you still might be able to find a way to get a feeling how to implement it in practice. But have you ever tried to implement the whole data management framework in your company from scratch by using DAMA-DMBOK2? The process will be similar to inventing the wheel or assembling a 100.000 piece puzzle.
  • Each one of the Knowledge areas belong to different categories, for example Metadata and Reference and Master Data are data-related, while Data Storage and Operations have poor technological and operational aspect.

So how should we deal with this?

Finding your own way to explain the DAMA model

This is my short 342-word story on DMBOK, including a semantic model of my understanding of DAMA.

There are three key pillars that support the DAMA data management model:

  1. Data management is a business function [1], the same as finance, sales etc.
  2. In the scope of DAMA, data management is considered to be part of Information Technology organization [2].
  3. Data management is cross-functional; it requires a range of skills and expertise [3].

The main subject of data management is data, shown as the central rectangle the diagram below. DAMA-DMBOK2 from content point of view classifies data in 4 categories: Reference, Master, Transactional, and Metadata. I have been always curious why DAMA-DMBOK considers Reference and Master data as a separate Knowledge Area and neglects Transactional data? Metadata being ‘data about data’ describes all other types. Each type of data has its quality. So, ‘Data Quality’ according to DAMA-DMBOK is also a Knowledge Area.

Data Architecture, and Data Modeling and Design are tools that in one way or another assist in describing data structure and processing. Data Architecture focuses on documenting data flows and data value chains. Data Modeling and Design is dedicated to setting up data requirements by delivering different data models.

Data can be also classified by the way it is stored. So, DAMA-DMBOK2 comes up with a classification of structured and unstructured data. Unstructured data gets its own spotlight in the Knowledge Area of ‘Document and Content management’.

Structured data is mentioned in three Knowledge Areas. ‘Data storage and Operations’ provides information on design, implementation and operation of data bases. Organization movement and consolidation of data is the core of ‘Data Integration & Interoperability’, and ‘DWH and BI’ focuses on data reporting.

If you look carefully, these three Knowledge Areas relate to technological and operational aspects of data processing. In the model, they all surrounded by the rectangle ‘Technology’

Data security is designed by Architecture and implemented by all technology related blocks.

Data governance is overarching and concerns processes, procedures and roles of data management business function.

A practical approach to data management

If you take a good look at the colors in my model, you will see three of them: grey, green and blue.

  • Grey symbolizes subjects which somehow did not get a specific Knowledge Area within DAMA.
  • Green parts relate to areas which are considered by most of the companies I dealt with as core data management areas.
  • The blue ones are functional areas which are in most of the cases relate to IT function.

The balance between dark- and light-green colored Knowledge Areas simply stresses the simple fact that DAMA considers data management as an IT function.

My own experience contradicts this vision. From what I have seen in various companies, is that data management is often put under the finance department, or as a separate business function, but rarely is it part of IT. In reality, many companies concentrate on other areas and leave only IT-related matters to IT department.

Implementing data management with DAMA

Once I succeeded in assembling the puzzle from DAMA pieces. From my practice, all data management set up follows the path of documenting of data value chain. You can find more practical examples from my experience in The Data Management Cookbook.


[1] DAMA-DMBOK1, p.4

[2] DAMA-DMBOK1, p.5

[3] DAMA-DMBOK2, p.22