Data Management vs Data Governance: 6 Key Differences

Photo of author
Written By Haisam Abdel Malak

About: Haissam is a digital software product manager with 15 years of expertise in developing enterprise content management solutions. His core capabilities encompass digital transformation, document management, records management, business process automation, and collaboration.

Spread The Love

Data management is not the same as data governance, and the difference between data management vs data governance can be difficult to understand. It’s important to know that the two concepts are not interchangeable terms, but what are their differences?

Data management is the process of organizing, storing, and making sense of data. While data governance is the process of managing data in such a way that it remains usable and accessible over time. DM can be seen as a subset of data governance.

Data management can be defined as “the process of ensuring that data is properly stored and managed”. It includes tasks such as managing data storage, backups, and security.

Data governance can be defined as “the process of managing the data lifecycle“. It includes tasks such as defining policies for storing and using data, setting standards for quality, and monitoring compliance with those standards.

The two processes are not interchangeable; they are two distinct entities with different goals in mind.

In this article, we will cover the differences between data management and data governance.

Data governance vs data management

What is data management?

Organizations have a lot of data that needs to be managed. This includes customer records, employee records, marketing campaigns, financial transactions and much more. Data management allows organizations to store all this information and make sure that it is accessible when needed by people in different departments within the organization.

It helps organizations to track their data and identify patterns. It also helps them to find new ways to use the data for their business.

Organizations that implement the perfect DM strategy and started using AI tools are capable of analyzing large amounts of data and identifying patterns within them. This has helped many organizations in finding new ways to use the data for their business.

For more information about this subject, check the below in-depth article.

What is Data Management and Why Is it Important? (

What is data governance?

Data governance is a process of managing data in order to reduce the risk of errors, improve data quality and ensure that data is available when it is needed. It helps an organization keep track of their information by ensuring that all processes are documented and followed.

Data governance has three main objectives:

– Maintain high levels of information quality

– Ensure compliance with laws, regulations, and policies

– Ensure availability of information

Data governance has become more important for organizations as the amount of data generated has increased significantly over the last few years. Organizations need to be able to maintain their data quality and consistency so that they can make better decisions and mitigate risks. It also helps in complying with regulations such as HIPAA and GDPR.

What are the differences between data management vs data governance?

We need to examine seven key procedures that each handle in order to acquire a broad understanding of the distinctions between data governance and data management.

1- Definition

Data governance and data management are two separate but highly related aspects of data management. Data governance is the process of ensuring that the business rules for data are established and followed. Data management is the process of making sure that all data is captured, managed, used, and disposed of properly.

The two work together to assure that data quality remains high no matter what situation it’s in.

2- Scope

One of the biggest differences between data governance vs data management is the scope for each discipline.

Data governance is a process that ensures the quality of data and the data management process.

The scope of data management is to ensure that all data have been collected, stored, and managed in such a way that it can be accessed by authorized users. The goal is to make sure that the information is accurate, relevant, accessible and secure. Data governance is a process which ensures these goals are in place.

3- Benefits

Data management provides many benefits to organizations. It improves efficiency, accuracy, and security of data. It also minimizes the risk of human error when handling large amounts of information.

Without proper data management policy in place, there is a higher risk for human errors and mistakes with big consequences for organizations like lost profits or even lawsuits.

Data governance helps organizations to be more efficient in their operations by providing a framework for managing data. The benefits of DG are that it ensures consistency in project deliverables and reduces errors related to using out-of-date or incorrect information.

It also provides an auditable trail of all decisions made about data, so there is less room for incorrect information. It can also help with compliance requirements by providing a framework for fulfilling these responsibilities.

4- Challenges

There are some obstacles when it comes to data governance. The first one is that it can be hard to know where you should start your data governance process. Another one is that there can be some resistance from employees who don’t want their work interrupted by new rules and regulations or who are not familiar with how the new system works.

There are many challenges that come with data management. One of the most common is that there is no one set standard for how data should be organized and stored. Another issue is that it can be difficult to find information when it needs to be retrieved from various sources and locations.

5- Best Practices

One of the most important differences between data management vs data governance is the implementation of best practices.

Some of the most important data management best practices include good file naming conventions, ensuring high quality of data, proper labeling, secure data storage, defining backup plans, and improving data security.

There are many best practices of data governance. The first one is the proper classification of data, which means using metadata to identify what kind of data it is, who owns it and what its purpose will be. Another best practice is the use of access control lists to grant or deny access to certain people or groups.

6- Technology

Acquiring the right technology is also among the top differences between data governance vs data management.

With the help of technology, data management has become easier. Data can be managed in a more organized way with the use of various tools and techniques.

Some of these tools are:

– Database Management Systems

– Data Mining Tools

– Data Visualization Tools

– Data Cleaning Tools

– Big data technologies

Technology can be used to help with data governance in a number of ways. For example, it can be used to automate processes like records management or to monitor user activity across systems. It can also be used to improve the effectiveness of current processes by automating manual tasks or providing insights into how the business operates.

Does data management include data governance?

Data management includes data governance as a subset. Data governance is a set of policies and procedures that are used to ensure that the data is managed in such a way that it can be trusted. Data management, on the other hand, is the process of managing data, including but not limited to data governance.

Leave a Reply