How to distinguish between data governance and data management? (Guaranteed version)

In a data-driven business environment, data governance and data management are two concepts that are often mentioned but easily confused. Although they are closely related, they carry different responsibilities and goals.

When talking about data governance, most people will think of data management, but what is the difference between these two words? It seems that it is not easy to explain clearly, and the two words are often used interchangeably, which seems to be no big problem.

This section mainly refers to some materials and combines my own understanding to talk about the difference between data management and data governance. As I have always said, the data field is a practical discipline. It does not pursue the complete unification of concepts and nouns. As long as there is a consensus within one's own organization, it will be fine.

"Data governance" and "data management", at first glance, these two words themselves are not particularly easy to understand.

For example:

If we say "develop a data platform", we know that this means making a tool product that allows the front-end and back-end to develop a data platform that supports data development.

If we say "sort out XX business processes and build data models", we know that we need to cooperate with XX business to sort out the business processes, sort out the table logic, and then build the data warehouse model for this business.

However, when we say "we need to conduct data governance and data management", it seems that we cannot clearly understand what this sentence is about. Govern data? Manage data? Govern what in the data? Manage what in the data?

Before distinguishing the concepts of "data governance" and "data management", let us first look at the conceptual difference between "governance" and "management".

1. What is the difference between governance and management?

In daily life, I don’t have a clear distinction between these two words. Here, I use the big model search to help make the distinction.

• Management: focuses on a series of activities such as planning, organizing, directing, coordinating and controlling the resources (including human, material and financial resources) within the organization to achieve the established goals, and often focuses more on the specific operation of affairs and the guarantee of orderly internal operation. For example, enterprise managers ensure that products are produced on time and with quality by formulating production plans, arranging personnel positions and supervising work progress.
• Governance: More emphasis is placed on coordinating and controlling public affairs or areas at a macro level through a series of institutional arrangements, rule-setting, and multi-party participation and interaction mechanisms to ensure their healthy, orderly, and fair development. For example, national governance involves the joint participation of multiple entities such as governments at different levels, social organizations, enterprises, and citizens to formulate laws, regulations, and policy frameworks to maintain social stability and promote economic development.

I would like to highlight the key points: Management: focuses more on the specific operation of things. Governance: is a series of institutional arrangements, rule settings and multi-party participation and interaction mechanisms.

Well, after summarizing this sentence, we can already distinguish most of them. In summary: one is to do specific things. The other is to formulate rules and regulations and guide people to do things.

Let's take a look at how other organizations define these two words.

II. Definition of DAMA

• Data Governance (DG) is defined as the exercise of authority and control in the process of managing data assets, including planning, monitoring and implementation.
• Data Management is the process of developing plans, systems, procedures and time activities, and implementing and supervising them throughout their life cycle in order to deliver, control, protect and enhance the value of data and information assets.

For a better understanding, the following passage in the DAMA book is also important:

The purpose of data governance is to ensure that data is managed correctly according to data management policies and best practices. The overall driver of data management is to ensure that the organization can derive value from its data.
Data governance focuses on how decisions are made about data and how people and processes behave with respect to data.
Data management will directly affect the data. Its direct goal is to improve data quality, and its ultimate goal is to realize the value of data.

It is still a bit difficult to read. But if we combine the difference between management and governance and only extract the key words, we can see the difference to some extent.

III. Definition of DGI

In the DGI Data Governance Framework, it is introduced that:

Broadly speaking, data governance is the process of making decisions about data.
In a narrow sense, data governance is a system of decision-making rights and accountability for information-related processes, implemented according to an agreed-upon model that determines who can take what action on what information, and when and under what circumstances.

DGI does not give a clear definition of data management.

However, from the definition of data governance, we can also see that the data governance defined by DGI also includes rules and systems such as decision-making power and accountability system.

4. Definition of IBM

Data Management: Data management is the set of practices that ingest, process, protect, and store an organization’s data, which is then used to make strategic decisions to improve business outcomes.
Data management is the set of practices for ingesting, processing, protecting, and storing an organization's data, which is then used for strategic decision-making to improve business outcomes.

Data Governance: Data governance is a data management discipline focused on the quality, security, and availability of an organization's data. Data governance helps ensure data integrity and data security by defining and enforcing policies, standards, and procedures for data collection, ownership, storage, processing, and use.
Data governance is the data management discipline that focuses on the quality, security and availability of an organization's data. Data governance helps ensure data integrity and data security by defining and implementing policies, standards and procedures for data collection, ownership, storage, processing and use.

If we look at it in detail, we can see that it is the same difference. It is just that the expression is not straightforward enough.

V. Summary

Not sure where I saw this summary or if it was just a personal summary. I think it's very good.

Data management: Through a series of management activities and measures, the value of data in promoting the development of enterprises towards informatization, digitalization and intelligentization can be fully utilized.

Note that this also includes data governance, which means that data governance is also part of data management. This can also be seen in the DAMA wheel diagram.

Data governance: a series of organizations, systems, and norms developed to better carry out data management activities.

Let me explain it in more detail.

Data management directly affects data, while data governance does not. When data management directly affects data, it is based on the content provided by data governance.

Data management can better play the value of data after actually operating data through activities. Data governance is a series of prerequisites determined for more standardized and process-oriented data operations.

6. Why are they often confused?

In my opinion, the two terms are used interchangeably because it is difficult to clearly distinguish between them. Another reason is that when conducting data governance, it is impossible to simply adjust the organization, release systems and regulations, etc., without further operating the data, that is, not doing data management.

Therefore, if there is a data governance project, it is likely that some data management actions will be included in the project, that is, formulating standards and adjusting the organization (data governance part), and (data management part) actually acting on the data, improving data quality, and thus realizing the value of data.

From this perspective, it is not entirely wrong to say that data governance and data management are often used interchangeably.

Without mixing them up, it should be pretty clear which word to use in which context. For example, the following statements:

“I want to ensure that the data management process is supported by the standards, organizations, and tools provided by data governance.”

"During the data governance process, the standards, organizations, and tools developed effectively ensure the standardized implementation of data management."

“By developing and executing a data governance work plan, companies can transform complex data management tasks into actionable, measurable, and specific activities, thereby maximizing the value of data and providing strong support for the sustainable development of the company.”

VII. Conclusion

Throughout this chapter, we have distinguished a conceptual distinction between data management and data governance.

Before making a detailed distinction, I thought these two concepts were not particularly easy to distinguish. After a thorough study, I found that they are quite easy to separate. One is doing things, and the other is the preparatory conditions before doing things. Data management is the entire process of doing things, and data governance is the organization, policies and other conditions before doing things. I will also consider tools, data and business as preparatory conditions later. I will talk about this in detail later.

In the next chapter, we will introduce a strategic goal of data governance, give an overall strategic goal map, and frame what data governance governs from a personal perspective.