This article analyzes the terms of Governance vs. Management in various contexts.
Part 1 of this series of articles explored the challenges associated with defining the concepts of data and information. Part 2 will delve into the various approaches to defining management and governance in a general linguistic context and the specific context of data management.
Governance
According to the Merriam-Webster dictionary, governance is “the act or process of governing or overseeing the control and direction of something.” In its turn, the verb govern means “to control, direct, or strongly influence the actions and conduct of [something].”
By synthesizing the definitions of “governance” and “governing,” we can define governance as the act or process of exercising authority, controlling, or overseeing the control and direction of an entity or a business capability.
In a business context, governance tasks can be understood as follows:
Exercising authority: Making and enforcing high-level decisions that shape the organization’s mission, vision, and values.
Controlling: Managing activities to ensure adherence to those decisions by implementing policies and regulations to mitigate risks and maintain compliance with laws and ethical standards.
Overseeing control: Monitoring and supervising management activities to ensure the effective use of resources and the achievement of organizational objectives.
Management
Let’s repeat this exercise for the definitions of “management” and “managing” from the Merriam-Webster Dictionary.
Management is the act of exercising executive, administrative, and supervisory direction of the business or conducting business.
Simply put, management is the business capability of leading and overseeing all aspects of a business. This capability focuses on making strategic decisions (executive), organizing and coordinating activities (administrative), and supervising employees (supervisory) to ensure the business operates effectively and achieves its goals. The summarized and simplified definition can look like this: the management capability plans, organizes, directs, and controls an organization’s resources and activities to achieve organizational objectives effectively and efficiently. Management tasks mean the following:
Planning: Setting goals and determining the best course of action to achieve them.
Organizing: Allocating resources, assigning tasks, and establishing procedures for accomplishing the plans.
Directing: Leading and motivating employees to perform their tasks effectively.
Controlling: Monitoring performance, comparing it with established standards, and adjusting as needed.
One of the key challenges in defining governance and management is that these two words are considered synonymous, even by the Merriam-Webster dictionary that provides the above-discussed definitions.
Our next step will be to examine how leading industry guidelines define data management and governance.
Data Management and Governance in the Eyes of Industry Guidelines and Authorities
Data Management Definitions
We will consider the opinions of DAMA-DMBOK2 by DAMA International, DCAM®2.2 and CDMC™ by the Enterprise Data Management Council, and Gartner.
DAMA-DMBOK2
DAMA-DMBOK2 defines data management as “the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles.” Figure 1 summarizes this definition in the form of a concept map.
Figure 1: The concept map of data management and governance by DAMA-DMBOK2.
So, we can see that the DAMA’s definition of management shares much with the general definition of management discussed above.
DCAM® 2.2. and CDMC™
The EDM Council offers two different frameworks: DCAM® 2.2. (Data Management Capability Assessment Model) and CMDC™ (Cloud Data Management Capabilities).
Fortunately, nowadays, EDMC has taken over the definition of data management with that provided by DAMA-DMBOK2, which was not the case several years ago.
Gartner
Strangely enough, I could not find the definition of data management in Gartner’s Glossary.
Data Management Structure
Even if the frameworks discussed above agreed on the definition of data management, they have pretty different viewpoints on the structure and content of data management.
DAMA-DMBOK2 explores the DAMA-DMBOK2 Data Management Framework (The DAMA Wheel), while the EDM Council develops two frameworks: DCAM® and CDMC™. Figure 2 demonstrates these frameworks’ original visualizations with references to the sources.
Figure 2: The original visualizations of DAMA-DMBOK2, DCAM®, and CDMC™ frameworks.
Figure 3 represents the O.R.A.N.G.E. DMF model of data management capabilities and is used to compare the three frameworks mentioned above.
Figure 3: Differences in data management components between DAMA-DMBOK2, DCAM®, and CDMC™.
Let me briefly explain this model and the meaning of the figure. Data management is a business capability that can be broken down into lower-level capabilities. These lower-level capabilities can be split into three layers depending on their role in business value delivery.
Core Capabilities
Capabilities at this level focus on delivering core business value to data management stakeholders, including customers, organizational owners, and management. The data lifecycle capability is central to this layer, ensuring the transformation of raw data into meaningful information and its delivery to stakeholders based on their requirements. Notably, the three frameworks—DAMA-DMBOK2, DCAM®, and CDMC™—present significantly different capabilities related to the data lifecycle. Even the two frameworks developed by the EDM Council (DCAM® and CDMC™) differ in their listed capabilities, highlighting varied approaches to addressing the data lifecycle within data management practices.
Strategic Capabilities
Strategic capabilities provide the foundational direction for data management, defining strategies, and overseeing implementation. Examples include business architecture and governance. Among the frameworks, only DCAM® explicitly incorporates business architecture as a strategic capability within its data management framework. While governance is a component of all three frameworks—DAMA-DMBOK2, DCAM®, and CDMC™—the specific content and focus of this capability vary across them.
Supporting Capabilities
Supporting capabilities serve to enable both strategic and core capabilities. Examples include enterprise architecture, security, analytics, and quality. However, the three frameworks diverge significantly in their lists of supporting capabilities, reflecting their differing priorities and areas of focus within data management. This lack of agreement highlights the tailored nature of each framework to address specific organizational needs or contexts.
Those interested in deepening their understanding of these differences can consult my series of articles, DAMA-DMBOK2 vs. DCAM® 2.2. I will elaborate in-depth on the differences between these frameworks in my new book, tentatively titled “Harmonizing Governance Frameworks for Data and AI Management,” which I plan to publish in 2025.
Data Governance Definitions
DAMA-DMBOK considers data governance as one of the Knowledge Areas of data management: “Data governance is the exercise of authority, control, and shared decision-making (planning, monitoring, and enforcement) over the management of data assets.”
It is worth noting that the DAMA-DMBOK2 stresses that data governance governs data management, not data itself.
However, the EDM Council does not define data governance, but the Data Governance Function, which is “The function that defines and implements the standards, controls and best practices of the data management initiative in alignment with strategy.”
Unfortunately, Gartner’s Glossary does not define data governance at all.
Data Governance Structure
Figure 4 shows that the deliverables of the data governance capability differ between the three frameworks discussed above.
Figure 4: Difference in defining the data governance components and deliverables.
At first glance, DAMA-DMBOK2 and DCAM® appear to align to some extent on the core deliverables or components of data governance, such as an operating model, principles, policies, roles, and accountabilities.
However, Gartner presents a notably different perspective on data governance. Unfortunately, there is no single source that fully outlines Gartner’s approach. What is evident, though, is that the governance-enabling activities and capabilities required for a data governance platform in Gartner’s model differ significantly from the classical approach to data governance as outlined by DAMA-DMBOK2 and DCAM®. It seems that Gartner tends to substitute the term “management” with “governance,” blurring the distinction between the two. However, a deeper analysis of this substitution can only be conducted by examining how core data management capabilities are applied to data, metadata, and information across various levels of abstraction. Part 3 will focus on this topic.
Data Management and Governance in the Data Management Community
In my experience, every organization tends to adapt industry guidelines or create its framework to align with its specific understanding of data management and governance. This leads to a wide variety of interpretations and, in some cases, extreme deviations from standard models.
For instance, I once encountered an organization whose framework mirrored the DAMA Wheel entirely, except for one significant change—it referred to the DAMA Wheel, a data management framework, as a data governance framework. Similarly, I’ve seen many simplified variations of the DAMA Wheel, which apply the same approach and substitute management with governance.
I suspect this substitution occurs for several reasons:
- People see the data governance capability at the center of the DAMA Wheel and interpret it as if governance included the surrounding capabilities.
- The title of the framework is not carefully read or understood, leading to misinterpretation.
- Influential authorities, like Gartner or IT solution vendors, promote their visions, which may not align with established industry frameworks, contributing to further confusion.
This misalignment highlights the need for clear communication and understanding of the intended purpose and scope of industry frameworks.
Takeaways
Any organization that plans to establish a new or adjust an existing data management framework should:
Clearly distinguish between governance and management: Ensure the organization explicitly defines and differentiates governance and management. Governance should focus on exercising authority, oversight, and control, while management should focus on planning, organizing, directing, and controlling resources and activities to achieve goals.
Adapt frameworks thoughtfully: Avoid substituting “management” with “governance” without thorough analysis. Understand the purpose and scope of industry frameworks such as DAMA-DMBOK2, DCAM®, and CDMC™ before adapting them. Ensure alignment between your organization’s terminology and the original intent of the frameworks to prevent confusion.
Analyze frameworks’ compatibility with an organizational structure: Carefully assess the capabilities and deliverables of different frameworks. Identify how they align with your organization’s data and information management practices. Avoid a one-size-fits-all approach, as even frameworks from the same authority (e.g., DCAM® and CDMC™) can differ significantly in their capabilities.
Part 3 will demonstrate the applicability of various data management capabilities to data at multiple abstraction levels and different stages of a data lifecycle.
