Data management vs data governance: the key differences between the two

Data management vs data governance

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 their 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 couple of key differences to keep in mind:

The employees who manage each likely differs

Data governance can consist of business teams who are focused on having high-quality data in their systems. Employees who work in RevOps are a great example of this.

Data management, on the other hand, can be made up of more technically-savvy individuals who are focused on the data architecture, and the data warehouse and data lake strategies. This often consists of employees who work in business technology/IT.

The toolset used for each can also vary

Data governance requires a tool that’s specialized in controlling, maintaining, and monitoring the data. This includes defining data ownership, data quality rules, etc.

Data management, however, requires a platform that can integrate apps in order to move and sync data across them.

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”).

To learn more about Workato, you can schedule a demo with one of our automation experts.

About the author
Jon Gitlin Content Strategist @ Workato
Jon Gitlin is the Managing Editor of The Connector, where you can get the latest news on Workato and uncover tips, examples, and frameworks for implementing powerful integrations and automations. In his free time, he loves to run outside, watch soccer ( matches, and explore local restaurants.