When comparing data management and data governance, all kinds of questions emerge.
Which is more important? How, exactly, are they different? Do they complement each other?
In reality, the two concepts can have different definitions, depending on who you ask. This muddles the answers to these questions, and it only adds to our general confusion.
We’ll give you a clearer understanding of data management and data governance and break down their differences by anchoring our knowledge in the most important measure: how both generally play out in the real world.
Related: The definition of data mapping and an example that brings the concept to life
What is data governance?
It’s the end-to-end process an organization takes in using data on a business level. This involves identifying and defining data, entering it into systems, and then consolidating, cleansing, and leveraging it.
By following a strong data governance process, your organization is less likely to be plagued by data quality issues that cause misalignment and result in poor decision-making.
Here are just two examples that illustrate this point:
1. Your data governance team (or data stewards) requires a name for every customer record.
This means that whenever someone tries to add a record without a name, a data quality check blocks it from being added successfully. In addition, the data governance team gets notified, allowing them to rectify the situation quickly.
2. To ensure that data is consistent and isn’t missing in the apps it’s meant to appear in, the data governance team performs a data synchronization check. This can take the form of dumping raw data from various apps into a data warehouse or data lake, where they can check the data.
Alternatively, they can build an automation in an integration-led automation platform that works as follows: any time data is added to a specific system, the platform checks to see if that data also exists in other predefined systems (if it doesn’t, it gets added/updated). The platform can even perform a weekly check across systems to double-check that their data is in sync.
What is data management?
It’s the approach an organization takes in managing its data. This includes deciding where to store their data and how to integrate it across their systems.
Take product usage data, for example. Your data management team could decide to store it in your data warehouse (where it’s also collected), CRM, marketing automation tool, and ITSM tool—allowing all of your customer-facing employees to access and leverage the data. To execute this, the data management team would implement integrations between the data warehouse and each of these tools—via an integration-led automation platform—allowing the data to move to these respective apps in real time.
What are the differences between data governance and data management?
Here are a few key differences to keep in mind:
| Aspect | Data Governance | Data Management |
| Who Manages It | Business teams, such as RevOps employees, are focused on data quality. | Targets technically-savvy individuals like business technology/IT employees. |
| Focus Areas | Ensures high-quality data in systems. | Focuses on data architecture and data warehouse/lake strategies. |
| Toolset Requirements | Requires tools for controlling, maintaining, and monitoring data (e.g., ownership, quality rules). | Needs platforms that integrate apps to move and sync data. |
| Importance | Ensures data accuracy, consistency, security, and compliance across the organization. | Enables efficient data processing, integration, storage, and access to support business operations. |
| Frameworks | Often based on standards like DAMA-DMBOK, COBIT, or DCAM. | Typically follows ITIL, TOGAF, or enterprise data architecture frameworks. |
| Best Practices | Define data ownership, implement data stewardship, maintain data catalogs, and enforce policies and standards. | Maintain clean data pipelines, ensure high data availability, optimize ETL processes, and automate data flows. |
| Roles and Responsibilities | Best suited for data stewards, data owners, governance councils, and compliance officers. | Involves data engineers, database admins, IT architects, and business technology teams. |
| Compliance and Regulations | Focuses on GDPR, CCPA, HIPAA, SOX, and other data privacy and handling regulations. | Ensures secure data access, encryption, backup, and disaster recovery aligned with compliance requirements. |
| Challenges | Cultural resistance, lack of data ownership, inconsistent definitions, and enforcing policies. | Integrating legacy systems, managing data quality at scale, and maintaining performance and uptime. |
Related: How to implement automation governance successfully
Learn how Workato can help you implement data management effectively
Workato, the leader in integration-led automation, offers a low-code/no-code UX that allows employees in IT and across lines of business to integrate their apps, databases, legacy systems, etc., and automate their workflows end-to-end.
The platform also accelerates your time to market—both for implementing integrations and automations—by providing pre-built connectors for hundreds of applications and thousands of automation templates (which we refer to as “recipes”).
Final thoughts
While data management and data governance are often intertwined, understanding their distinct roles is crucial for building a scalable and compliant data strategy. Data management focuses on the operational side—storing, processing, and integrating data—while data governance ensures that the data is accurate, secure, and used responsibly.
In practice, both must work hand-in-hand. Without governance, managed data lacks trustworthiness; without management, governed data lacks utility.
Platforms such as Workato can play a supporting role by automating key data workflows and ensuring consistent policy enforcement across systems—helping organizations bridge the gap between strategy and execution.
