Data Governance vs. Data Management: What's the Difference in 2025?

Updated December 05th, 2024

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The difference between data governance and data management lies in their scope and purpose within an organization.

Data governance focuses on setting policies, procedures, and standards for data access, security, and compliance. It ensures that data is protected, accurate, and used responsibly.

Data management, on the other hand, deals with the day-to-day processes of storing, organizing, and utilizing data to meet business needs. It includes tools and practices that manage the data lifecycle, ensuring it is accessible, accurate, and usable for decision-making.
See How Atlan Simplifies Data Governance – Start Product Tour

While governance defines the rules, management enforces them operationally, making both essential for effective data management and organizational success.

Table of contents #

  1. Difference between data governance and data management
  2. Data governance vs. data management: In action
  3. Data governance vs. data management: 3 core differences
  4. How do data governance and data management work together?
  5. Data governance and data management: Are they really that different?
  6. How organizations making the most out of their data using Atlan
  7. FAQs about data governance vs data management
  8. Data governance vs. Data management: Related reads

Difference between data governance and data management #

The main difference between data governance and data management is 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. See How Atlan Simplifies Data Governance – Start Product Tour

Here’s a quick comparison table between data governance and data management to help you out.

 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.

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 → Data Governance | Data management 101

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.

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.

Data Governance vs. Data Management

The overlap between the disciplines of data governance, data management, and information security 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.

Data governance vs. data management: In action - A Demo of Atlan

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:

  1. Processes
  2. People
  3. Technology

Difference #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

Difference #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).

Difference #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.

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:

  1. Microsoft

”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.”

  1. Deloitte

“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.”

  1. McKinsey

“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.”

  1. Fivetran

“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.”

  1. Snowflake

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

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.

Also, read → Roadmap for data and analytics governance | Data governance approach that enables business outcomes | Modernize data management to drive value | Elevating master data management in an organization

How organizations making the most out of their data using Atlan #

The recently published Forrester Wave report compared all the major enterprise data catalogs and positioned Atlan as the market leader ahead of all others. The comparison was based on 24 different aspects of cataloging, broadly across the following three criteria:

  1. Automatic cataloging of the entire technology, data, and AI ecosystem
  2. Enabling the data ecosystem AI and automation first
  3. Prioritizing data democratization and self-service

These criteria made Atlan the ideal choice for a major audio content platform, where the data ecosystem was centered around Snowflake. The platform sought a “one-stop shop for governance and discovery,” and Atlan played a crucial role in ensuring their data was “understandable, reliable, high-quality, and discoverable.”

For another organization, Aliaxis, which also uses Snowflake as their core data platform, Atlan served as “a bridge” between various tools and technologies across the data ecosystem. With its organization-wide business glossary, Atlan became the go-to platform for finding, accessing, and using data. It also significantly reduced the time spent by data engineers and analysts on pipeline debugging and troubleshooting.

A key goal of Atlan is to help organizations maximize the use of their data for AI use cases. As generative AI capabilities have advanced in recent years, organizations can now do more with both structured and unstructured data—provided it is discoverable and trustworthy, or in other words, AI-ready.

Tide’s Story of GDPR Compliance: Embedding Privacy into Automated Processes #


  • Tide, a UK-based digital bank with nearly 500,000 small business customers, sought to improve their compliance with GDPR’s Right to Erasure, commonly known as the “Right to be forgotten”.
  • After adopting Atlan as their metadata platform, Tide’s data and legal teams collaborated to define personally identifiable information in order to propagate those definitions and tags across their data estate.
  • Tide used Atlan Playbooks (rule-based bulk automations) to automatically identify, tag, and secure personal data, turning a 50-day manual process into mere hours of work.

Book your personalized demo today to find out how Atlan can help your organization in establishing and scaling data governance programs.

FAQs about data governance vs data management #

1. What is the difference between data governance and data management? #


Data governance focuses on setting policies, procedures, and standards for managing data, ensuring its security, quality, and compliance. Data management involves the operational aspects, such as storing, organizing, and maintaining data.

2. How do data governance and data management work together? #


Data governance provides the framework and rules, while data management executes these rules operationally. Together, they ensure data is reliable, secure, and aligns with organizational goals.

3. How do data governance and data management impact compliance? #


Data governance ensures that data policies align with regulatory requirements, while data management implements these policies in day-to-day operations. This synergy helps organizations maintain compliance with data protection laws such as GDPR and HIPAA.

4. What are the roles of data governance vs. data management? #


Data governance involves establishing policies, accountability, and compliance measures, while data management includes data processing, integration, and storage to meet these governance standards.

5. How do organizations implement both data governance and management frameworks? #


Organizations often adopt frameworks like DAMA-DMBOK for data management and establish data governance committees to set and enforce policies, ensuring cohesive implementation.

6. What challenges arise when aligning data governance with data management? #


Key challenges include ensuring stakeholder buy-in, maintaining data quality across silos, and aligning governance policies with evolving data management technologies.

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