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

December 7th, 2022

Data governance vs. data management is a frequent comparison as they are two similar practices that help you leverage data to its fullest potential.

Here let’s understand where the comparison stems from, understand the fundamentals of data governance and data management, and then explore how they’re related.


What is the difference between data governance and data management?

Data governance is a set of procedures and guidelines on data access, use, and management. Meanwhile, data management includes processes and tools to ingest, store, catalog, prep, explore, and transform data, so that you can use it for decision-making.

Here’s an analogy that’s often cited to explain the difference between data governance vs. data management. Data governance can be seen as the blueprint for constructing a new building, whereas data management is the act of construction.

Read more → What is data governance? and Data management 101

The author of the book titled “Non-Invasive Data Governance” Robert S. Seiner compares data governance vs. data management as follows:

Data governance focuses on what I refer to as the Bill of “Rights”. It’s all about getting the “right” people with the “right” knowledge working with the “right” data in the “right” way at the “right” time resulting in the “right” decision. Meanwhile, data management is the delivery of practices and processes targeted at successful business outcomes.


The overlap between the disciplines of data governance, data management, and information security, according to Robert S. Seiner. Source: TDAN

Meanwhile, the International Organization for Standardization (ISO) highlights the difference between data governance vs. data management as follows:

Data governance specifies which decisions are to be made in data management and who makes such decisions. However, data management ensures these decisions are made and actions are taken place appropriately.


Data governance vs. data management: In action

For instance, when you use AWS S3 to store your data or Fivetran to set up data pipelines, that’s an example of data management in action.

However, when you set up a business glossary with definitions of the data assets in AWS S3 or a platform to define S3’s access rules, you’re implementing data governance.

Does data management include data governance?

Yes. Data governance is all about how you manage your data. So, data management includes data governance as you must document the guidelines on dealing with data and how they affect your business goals.


Data governance vs. data management: 3 core differences

We can compare data governance vs. data management in terms of:

  • Processes
  • People
  • Technology

Data governance vs. data management #1: The processes

Data governance dictates “how organizations decide about using data”. So, the processes involved can be:

  • Setting up data quality checks
  • Defining data access policies
  • Complying with global, local, and organizational regulations
  • Setting up and maintaining a business glossary

Meanwhile, data management is all about “how organizations use data”. These processes follow the guidelines outlined in the data governance framework.

The processes involved can be:

  • Data transformations to maintain data in consistent formats
  • Data storage in warehouses, lakes, and more
  • Data exploration to solve operational and analytics use cases

Data governance vs. data management #2: The people

Data governance was traditionally seen as a function that concerned business and IT teams. So, implementing data governance would involve business managers, domain data owners, and other such business stakeholders.

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

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

Data governance vs. data management #3: The technology

So, data governance tools are used to document these rules and incorporate them for data assets across the organization. This includes data dictionaries and glossaries, data catalogs, etc.

Meanwhile, data management tools are more concerned with data storage, processing, and exploration.


Data governanceData management
What is it?A practice to increase the business value of data without compromising its security, integrity, or privacyProcesses and tools to support data consumption by following the data governance guidelines
What does it do?Defines policies and controls for storing, manipulating, and consuming dataDefines tools, procedures, and methods to manage the lifecycle of each data asset
What questions does it answer?1. Which data sources should you use for your business? 2. How will you classify the data originating from various sources? 3. How will you store sensitive data? 4. Who owns a data asset? 5. Who can access that asset? Who can make changes to it? 6. What are your data quality metrics?1. What tools will you use to store data? 2. How will you make data from various domains or applications interoperable? 3. How will you facilitate data sharing and collaboration? 4. How will you ensure the quality of data and keep it relevant, accurate, and updated?
Who owns it?The accountability lies with everyone, but primarily the business heads. For example, in a decentralized model, the data domains are responsible for governing the data they create.The accountability lies with the engineers and other technical members of your data team.

How do data governance and data management work together?

Data management without governance is like constructing something without a blueprint. Meanwhile, governance without management is just documentation. It’s crucial for data governance and data management to work in tandem so that you can extract value from your data.

Here are some examples of how that would look:

  1. Regulatory compliance
  2. Role-based access
  3. Data cataloging

Example #1: Regulatory compliance

GDPR guidelines help you define your data governance policy. For instance, the GDPR expects you to maintain records for at least seven years from the end of your tax year/accounting period. So, your data governance policy must ensure that you know which data should be retained for seven years, the format in which it must be stored, and the people who can access it.

In this scenario, data management would involve extracting the necessary data and performing the required transformations to organize, classify, and store it in the right format. Data management would also involve ensuring that this data is easy to discover and access for the right people.

Example #2: Role-based access

Another example is that of role-based access. The data governance program should identify user roles and their data access rights.

Data management would involve the execution and monitoring of role-based access within your organization.

Example #3: Data cataloging

Data governance requires you to ensure that all of your data assets are well-defined and offer the necessary context, such as asset origins, ownership, transformations, etc.

Data governance would involve setting up a data cataloging platform that leverages metadata from all data sources to surface asset descriptions, 360-degree data profiles, lineage mapping, and more. Meanwhile, data management would focus on organizing the data from various sources into data warehouses and lakes and connecting these sources with the cataloging tool.

Data governance and data management: Are they really that different?

Traditionally, identifying data governance and data management as two separate concepts made sense because governance was merely all about compliance. So, the tooling focused on access control and security.

Meanwhile, data management was all about executing the various processes involved in collecting, storing, and using data.

Today, those lines have blurred as data governance is crucial to helping data teams work better. It’s not a “data governance vs. data management” situation anymore.

Let’s look at how various organizations define it:

Data governance is about managing data as a strategic asset. Data governance practices are essential in helping ensure that data is optimized for any use—enabling deeper insights across our organizational and functional boundaries.” - Microsoft


Data governance reflects an organization’s strategic direction and desired outcomes in areas related to data management, including quality and metadata management, information security, architecture, and data modeling.” - Deloitte


The foundation of data governance is — balancing central oversight, proper prioritization, and consistency while ensuring that the employees creating and using data are the ones leading its management.” -McKinsey


Data governance drives an understanding of the challenges data teams face. Ultimately, data governance leads to a data-driven organization that will continue to compete in the modern marketplace.*” - Fivetran


Data governance is an organization’s management of its data availability, usability, consistency, and data integrity and data security.” - Snowflake


That’s why data governance and data management can be seen as different ways of looking at the same problems — two sides of the same coin. Better governance will lead to better management of your data assets, and effective data management is only possible when you achieve true governance.


Ready to make data governance effortless?

Try Atlan — Say goodbye to the complex, bureaucratic version of data governance. Say hello to data enablement — a simpler, community-centered approach, with privacy at its core.


Ebook cover - metadata catalog primer

Everything you need to know about modern data catalogs

Adopting a modern data catalog is the first step towards data discovery. In this guide, we explore the evolution of the data management ecosystem, the challenges created by traditional data catalog solutions, and what an ideal, modern-day data catalog should look like. Download now!

[Website env: production]