Data Governance vs. Data Management: What's the Difference?

May 23rd, 2022

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Is it data governance vs. data management? Or, should it be data governance with data management?

It’s not uncommon to assume that a data management program will take care of governance or vice versa. Unfortunately, several organizations are in the habit of using these terms interchangeably or considering one realm redundant.

However, it shouldn’t be “data governance vs. data management”. The question isn’t which of these two realms your organization needs. Because it needs both.

What is data management?

Data management is the management of data throughout its lifecycle in an organization — from data ingestion and discovery to data analysis and governance.

The DAMA international(DMBoK) defines data management as:

Data management is 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 life cycles.

Gartner defines data management as follows:

Data management (DM) consists of the practices, architectural techniques, and tools for achieving consistent access to and delivery of data across the spectrum of data subject areas and data structure types in the enterprise, to meet the data consumption requirements of all applications and business processes.

Naturally, that includes a few key components such as:

  1. Data architecture: Architecture is the 30000-foot view that gives you insights into your technology stack, tools, blueprints, and dependencies.
  2. Data integration: Ingesting petabytes of data from diverse sources and formats and storing it in a data warehouse or a data lake
  3. Data preparation: Cleaning and transforming data into valuable information and getting it ready for analytics
  4. Data catalog: Cataloging the information gleaned from diverse sources and adding context to make it discoverable, usable, and shareable
  5. Data security management: This includes all processes, workflows, people, and policies to provide secure authentication and access to data assets.
  6. Data governance: Ensuring data quality, integrity, and security with policies, procedures, and rules that govern the use of data within an organization

Wait, what? Is data governance a part of data management?

Yes, it is. But before we explore how the two realms are connected, let’s learn a little more about data governance.

The data management landscape in the modern data stack

The data management landscape in the modern data stack

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What is data governance?

While there are several other definitions out there, we’d like to refer to our version from a previous article:

Data governance is a set of policies, processes, and standards to collect, manage, and store data for better decision-making. It helps organizations ensure the relevance, integrity, availability, security, and usability of their data.

Some of its core components include (according to the DAMA DMBOK model):

  1. Data quality: Measures that define bad or insufficient data, differentiate it from credible data, and map the processes data must go through to be considered trustworthy
  2. Data security: Standards that outline how an organization will safeguard its data without compromising its integrity or privacy
  3. Metadata: Policies for metadata that outline its definitions, properties, characteristics, storage format, and cataloging
  4. Data storage and operations: Rules that define the right way to store, retain or delete data as per compliance requirements

Depending on the data governance framework you choose, the components of data governance may vary. But at its heart, it’s all about the quality, security, and operability of organizational data.

With the definitions out of the way, let’s see how the two realms — data governance and data management — are connected.

Is data governance a part of data management?

Now, connected doesn’t mean they’re the same. If you search for data governance vs. data management, you’ll see several articles mentioning how they’re different using a popular construction analogy:

Data governance acts as a blueprint for constructing a new building. Whereas data management is the act of construction.

So, data governance is part of data management, as you need a blueprint before beginning construction. Otherwise, the data being used for decision-making won’t be trustworthy or compliance-ready.

And that leads to chaos, much like the illustration below.

Chaos in the absence of data governance and data management

Data chaos that the humans of data face because of poor governance and bad management. Image by Atlan

That’s why both realms are essential for an organization to become data-driven. Also, one doesn’t replace the other. So, let’s further elaborate on their differences by focusing on three crucial elements: processes, people, and technology.

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What is the difference between data governance and data management?

Data governance vs. data management #1: The processes

Data governance dictates “how organizations decide about using data”. So, it’s all about overseeing all the processes involving data and making sure that they don’t break any laws (i.e., security and compliance) or corrupt the organization’s data (i.e., data quality).

As a result, the processes that follow are frameworks with rules and guidelines that ensure efficient oversight.

Meanwhile, data management is all about “how organizations use data”. So, it deals with storing, integrating, cataloging, prepping, exploring, and transforming to extract value from data.

That’s why the processes that follow are procedures (workflows or algorithms) that ensure that data is being used as outlined in the governance framework.

Data governance vs. data management #2: The people

While data governance is considered to be a business function, data management is seen as an undertaking for the IT or technical teams. Here’s why.

Most data governance frameworks expect organizations to set up a data management council or steering committee that oversees the governance program. Such councils are made up of the leadership or senior management involved in business roles. That’s because the data governance program objectives must impact the overall business goals of the organization.

So, a business manager could be in charge of overseeing a governance program for a specific data domain.

Meanwhile, data management is all about execution — implementing the governance framework and influencing the organization’s business goals. This involves everything from defining rules for data storage to setting up access rights and controls.

So, it involves more technical roles, such as a data engineer, architect, or database administrator (DBA).

Data governance vs. data management #3: The technology

The technology is for data management. Organizations define the goals or objectives for their data governance initiatives, along with the rules, standards, policies, and procedures for using data. That’s why this aspect is considered to be philosophical in nature.

Whereas implementing the program is all about data management, and that’s when technology comes into play. So, technology supports the implementation of a data governance platform.

One more thing — depending on the objective of your data governance program and the availability of resources, the roles can overlap too.

Those are the three key differences. However, even though the two realms are different, they complement each other in making data truly valuable. Let’s see how.

How do data governance and data management work together?

All information is data; (but) not all data is information. Information is data that is readily applied to business processes and which generates value. To arrive at (that) information, data must undergo a rigorous governance process and a number of key measures are implemented (i.e., data management) to make useful data trusted and used as information.


As mentioned earlier, management without governance is like constructing something without a blueprint. Meanwhile, governance without management is just documentation.

Example #1: Regulatory compliance

Organizations need data governance and data management to extract real value from their data while ensuring regulatory compliance.

For instance, while the GDPR doesn’t specify the duration for which organizations must retain their data, a good practice is to maintain records for at least seven years from the end of their tax year/accounting period.

Here, the data governance framework must identify the data to be retained for seven years and the format in which it must be stored and audited according to GDPR. Data management would then be responsible for retrieving the necessary data and cataloging it in the right format within an organization’s storage systems.

Example #2: Role-based access

Another example is that of role-based access. The data governance program would establish data access rights so that the right people can get the right data with no delays or bottlenecks by involving IT. The data management program would implement the role-based system and monitor the same to ensure compliance with the governance framework.

These are just a few examples. If you ask us, every data domain requires data governance and data management to work in tandem for seamless data-driven operations.

With that, we come to the how — how can organizations make data governance and data management work in tandem?

The answer: technology (we know, we know... that was obvious. But still, it had to be said!)

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From data governance vs. data management to data governance with data management

Let’s recall the previous examples of data retention and data access. Regulations require organizations to maintain detailed records of:

  1. What data gets stored?
  2. How is it accessed and used?
  3. Who accesses and modifies or uses the data?

And so on. Organizations can establish the required processes or rules and easily implement and monitor them with access logs by using a single platform for governance and data management.

How Atlan implements data management

Here’s how users achieve it using Atlan:

Users can define rules for automatic storage or deletion of these logs as per the compliance standards.

How Atlan implements data governance - Access logs

Preview of access logs in Atlan

Now let’s look at access. You can use a single dashboard to monitor access and grant or deny access rights immediately.

How Atlan implements data management - Manage Users

User groups based on functions or roles, and governance allowing actions via policies using Atlan

As a result, your managers don’t have to wait for weeks to get access to essential customer data informing their business decisions.

Technology also makes it easier to implement governance programs with automation. For instance, if a particular data asset is labeled as sensitive, then every transformation, algorithm, or report using that asset will also inherit the same classification and security controls.

Here’s how Atlan propagates governance policies through lineage.

Automatic propagation of access policies through lineage in Atlan

Propagation of policies through lineage in Atlan

There are endless use cases of how using one platform for governance and data management can make them effortless and democratize data for teams across the organization. That’s how organizations can go from data governance vs. data management to effective data governance with data management.

Ready to make data governance effortless?

Try Atlan — Auto-construct data lineage and deploy best-in-class data access governance without compromising on data democratization.

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