Lately, I’ve seen a lot of discussions on LinkedIn about the similarities and differences between data management (DM) and data governance (DG). These two ground terms look like the most unaligned definitions in the data management community. I discussed this issue in a series of articles: Data Management & Data Governance 101. However, lately, I have made some new discoveries on this topic.
In this short article, I discuss the following:
- The linguistic definitions of “management” and “governance”
- The similarities and differences of updated DM and DG definitions by industry guides
- Challenges associated with choosing DM and DG software
The linguistic definitions of “management” and “governance”
I investigated several trustful dictionaries like Oxford, Merriam-Webster, and Cambridge.
I searched for the linguistic definitions of two words: governance or govern and management or manage. Figure 1 demonstrates the results of my investigation.
Figure 1: A concept map of “govern” and “manage.”
You can see that the word “govern” is associated with words like “direct,” “regulate,” “oversee control,” and direction. So, you can conclude that “governance” means providing directions and controlling.
Then I did the same with the words “manage” and “management.”
These words are associated with “organizing,” “achieving a purpose,” “administration,” and so on.
So, you get the impression that management is more about the organization and performing actions to achieve goals.
But then, I asked myself a simple question: are these two words synonymous? I was amazed.
For many years, I believed they were not. Even if the series of articles about data governance and management that I published several years ago, I stated it. However, two sources, the Thesaurus, and the Merriam-Webster dictionary, confirmed that management and governance ARE synonymous.
It is a very challenging discovery as we get used to separating these two terms in the data world.
After discovering that “govern” and “manage” are synonymous, I have two questions.
- Does a differentiation between data management and data governance make sense?
We, data management professionals, operate with these two terms. Some of us use them interchangeably, while others see many differences in the content of these concepts. - If we still differentiate these two terms, how can we distinguish them?
The similarities and differences of updated DM and DG definitions by industry guides
We have two leading industry guidelines in data management: DAMA-DMBOK2, the Data management body of knowledge by DAMA International, and DCAM, the Data management capability assessment model by the Enterprise Data Management Council.
Several years ago, I investigated the differences between these two guides and published the result in the series of articles “DAMA-DMBOK2 vs DCAM® 2.2 – which Framework to Choose?”
It was a challenging task. DAMA is an open society. You can pay a feasible amount to purchase the DAMA-DMBOK book. With DCAM, it is much more complicated. They are a closed society.
You can get information about its methodology only by being a member. Earlier, some materials were still open to the public-for example, the content of their assessment. A couple of weeks ago, I checked their renewed site. I realized that they closed access to the content. However, they published a glossary. And I assume that they use these terms in their methodology.
I must admit that a couple of years ago, the definitions of data management and governance between the DAMA-DMBOK and DCAM differed significantly. Now, DCAM almost reused the DAMA terminology. However, there are still some differences.
Figure 2: Concept map of “data management” and “data governance” definitions.
I prepared a concept map to demonstrate the essence of these definitions. Data and information assets and their lifecycle are the core subject of data management. Data management policies and programs assist in delivering, controlling, protecting, and enhancing data assets.
Data management develops, executes, and supervises policies, programs, and plans.
Regarding data governance, we have some differences between the definitions of these two guidelines. DAMA-DMBOK2 says that data governance controls and decides over data management. DCAM says that data governance defines and implements standards and practices that define data management. I don’t know your feelings about these definitions; I had difficulties decoding and understanding these definitions.
So, I interpreted these definitions in the way described in Figure 3:
Data management says: “I decide how and when I will deliver, control, protect, and enhance data.”
Data governance says: “Great, but I decide how You can decide, and then I will control how you do it.”
So, you can try to solve this puzzle on your own.
Despite the fact that DAMA-DMBOK2 and DCAM have almost identical definitions of data management and governance, the content of their models differs greatly.
For those interested in understanding these differences, I recommend reading the series of my articles on this topic.
Figure 3: The interpretation of “data management and “data governance” definitions.
In my practice, I use the simplified view on the relationships between data management and governance, explained in Figure 4.
Data management is a business’s ability to safeguard data assets, get value from them, and manage a data assets lifecycle.
Figure 4: Concept map of key data management capabilities.
Data and its lifecycle are the core subject of data management. Data management is multidisciplinary. A data quality capability manages the quality of data and information. Enterprise architecture describes, classifies, models, and designs data and IT assets and their security. An Information technology capability manages IT assets and enables the data lifecycle. A Security capability ensures the security of data and IT assets. Data governance is a component of data management. Data Governance defines how data management must be set up. So, it gives a direction and controls how data management follows this direction. Because data management consists of multiple capabilities, data governance must define the set of data management capabilities. Data management defines what, when, and how it will manage data and organizes the data lifecycle. Data governance is a data management capability that designs a data management operating structure by specifying related regulations and roles and coordinates and controls data management performance and maturity.
Challenges associated with choosing DM and DG software
I want to briefly share some challenging results of my investigations of 168 data management-related IT tools. I’ve chosen several categories of IT tools: data management, data governance, data fabric, data lineage, knowledge graphs, and metadata management.
The first challenge is that 28% of these tools had multiple classifications/labels, as shown in Figure 5.
Figure 5: The distribution of IT tools with multiple labels.
Some products have 5 labels, others 4,3, and 2. These multiple labels may create a challenge in choosing an IT tool. For example, complex tools will have multiple integrated functionalities.
Some of these functionalities can be leading and some simply enabling the core functionalities.
If you are looking only for a data lineage solution, you must be aware of whether data lineage functionality is a core or supporting functionality. Otherwise, you may face some limitations in the required functionality.
The second challenge is that some IT tools offer functionality not expected from their labeling. Figure 6 illustrates the analysis of the following functionalities: business processes, enterprise architecture, data governance limited to stewardship and policy, data quality, master and reference data, risk, security, compliance, and unstructured data management.
The orange-colored blocks represent data governance tools, and yellow-colored – data management.
Referring to DAMA-DMBOK2, data quality, master and reference data, and security are different data management knowledge areas. But you can see that multiple so-called data governance software provide these functionalities.
Figure 6: Various functionalities provided by data management and governance IT tools.
The third challenge is that own IT tools classification does not correspond to third-party classification.
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