During a meeting a few days ago, I 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 an 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 solid professional team. The guide provides 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 of how to implement it in practice. But have you ever tried implementing the whole data management framework in your company from scratch using DAMA-DMBOK2? The process will resemble inventing the wheel or assembling a 100.000-piece puzzle.
- Each one of the Knowledge areas belongs to different categories; for example, Metadata, Reference, and Master Data are data-related, while Data Storage and Operations have poor technological and operational aspects.
So how should we deal with this?
Finding your way to explain the DAMA model
This is my short 342-word story on DMBOK, including a semantic model of my understanding of DAMA.
Three key pillars support the DAMA data management model:
- Data management is a business function , like finance, sales, etc.
- In the scope of DAMA, data management is considered to be part of an Information Technology organization .
- Data management is cross-functional; it requires a range of skills and expertise .
The main subject of data management is data, shown as the central rectangle in the diagram below. DAMA-DMBOK2, from a content point of view, classifies data into 4 categories: Reference, Master, Transactional, and Metadata. I have always been curious why DAMA-DMBOK considers Reference and Master data separate Knowledge Areas 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 are dedicated to setting up data requirements by delivering different data models.
Data can also be classified by the way it is stored. So, DAMA-DMBOK2 comes up with a classification of structured and unstructured data. Unstructured data gets spotlighted in the ‘Document and Content management Knowledge Area.’
Structured data is mentioned in three Knowledge Areas. ‘Data storage and Operations’ provides information on database design, implementation, and operation. Organizational movement and data consolidation is the core of ‘Data Integration & Interoperability,’ and ‘DWH and BI’ focus on data reporting.
If you look carefully, these three Knowledge Areas relate to technological and operational aspects of data processing. In the model, they are 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
Looking at the colors in my model, you will see three: grey, green, and blue.
- Grey symbolizes subjects that did not get a specific Knowledge Area within DAMA.
- Green parts relate to areas considered by most of the companies I dealt with as core data management areas.
- The blue ones are functional areas that are, in most cases, related to the IT function.
The balance between dark- and light-green-colored Knowledge Areas 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, 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 the IT department.
Implementing data management with DAMA
Once I succeeded in assembling the puzzle from DAMA pieces. From my practice, all data management setup follows the path of documenting the data value chain. More practical examples from my experience in The Data Management Cookbook can be found.
 DAMA-DMBOK1, p.4
 DAMA-DMBOK1, p.5
 DAMA-DMBOK2, p.22[/vc_column_text][/vc_column][/vc_row]
For more insights, visit the Data Crossroads Academy site: //academy.datacrossroads.nl