What Is Data Governance and Why Is It Important for Businesses?

What Is Data Governance?

Data governance is not a set of technologies but rather an organizational discipline, says Stewart Bond, vice president of IDC’s data integration and intelligence software service. Data governance is a set of processes and policies for how an organization’s data is secured, cleaned, managed and used.

“If you don’t know where your data is, what it looks like, where it’s flowing, how it’s flowing through the organization, where it came from, where it’s going, who owns it, and if you’re not assigning accountability, you can’t even begin to govern it,” Bond says.

Why Is Data Governance Important for Businesses?

Data governance is crucial to ensuring organizational compliance with industry, national and potentially international regulations on how users’ data is protected, stored and used.

“It really comes back to how you are managing and governing your data,” Schulz says. “That really gets back to how you are protecting it. How are you making sure that you are compliant — that not just your data but your applications that are using that data are compliant?”

It’s critically important for businesses to have intelligence about their data, Bond says, because “if they don’t have that, what are they even governing?”

Data governance is also a crucial tool for balancing between data security and efforts to innovate data usage, he says. Bond notes that data analysts often spend about 80 percent of their time looking for or preparing data for analysis and 20 percent performing analytics and gleaning insights.

Data governance can change that ratio. “If they could, analysts would spend less time looking for data and they’d spend less time preparing data,” Bond says. Having access to all the relevant data, as Bond explained, also helps analysts see all the information in context enabling them to make informed business decisions. 

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The Role of Data Governance in a Modern Data Platform

A modern data platform allows organizations to not only store data securely but to democratize access to the data across the business.

A modern data platform is either cloud-based or hybrid, makes it easy for users to discover data and break down silos, gives data insights to nontechnical users, and uses modern tools, such as artificial intelligence and machine learning, to analyze data.

Bond notes that it is important to apply the same rigor of data governance no matter the architecture of an organization’s data repository. However, he says, in a cloud-based architecture, it is often easier to share, work with and consume data.

Still, Bond adds, “if you make more data more accessible to more people, you increase the risk, so you need to make sure you have the appropriate controls in place around what they can and cannot do with that data.”

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Data Governance Use Cases: Accountability and Compliance

Data governance is an essential element of ensuring that businesses comply with laws and regulations. Data governance is the umbrella architecture that helps organizations implement policies via defined processes, Schulz says. Those can be specific to an industry, such as the Payment Card Industry Data Security Standard for retailers, or the European Union’s General Data Protection Regulation.

Data governance processes and policies give organizations visibility into where data is and how it is flowing, as well as how it is being used, via behavioral metadata, Bond notes. That includes which applications are using the data, how often it is being accessed and which data is being used most often. A data’s lineage shows not only where it is coming from but where it is going to.

If there is a need to perform an audit or compliance check for regulators, that intelligence can, in many cases, be used to “get those audit reports and understand how and where and when that data is being used,” Bond says.

Data Governance Use Cases: Improving Data Quality

Data governance also helps organizations improve the quality of the data they store. It can help define what data will be kept and for how long, as well as how often the data will be cleaned or scrubbed to remove erroneous data, Schulz says.

Additionally, data governance can help organizations determine if they are working with raw, unstructured data or data that has been labeled and processed. Data governance rules often allow organizations to apply metadata information to their data so that it can be more easily characterized and found.

Within organizations, the role of the data steward is to be accountable for the quality of the data and to prioritize which data needs to be cleaned up. “Not only do you know the quality levels of that data, you also know the criticality” and how the data is “being leveraged in the business to make strategic and operational decisions,” Bond says.

“So, you focus in on the most critical data elements to the business, understand your levels of quality and prioritize where you do those cleanups,” he says.

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Data Governance in the Cloud: Key Considerations

There are several key things businesses should consider when thinking about data governance in the cloud. One is that putting data in the cloud simply introduces another set of locations for data to be stored, managed and secured, Schulz says. The cloud also introduces new kinds of data — from Microsoft Teams interactions, for example — that was not being generated before.

It may be easier for organizations that have all their data on-premises, or all with one cloud provider. Data governance becomes more complex in a hybrid or multi-cloud environment, Schulz says.

“Where is the authoritative copy of the data, and what are the subservient copies?” he says. “And for your compliance, for your governance, do you even need to be concerned with that?”

Before businesses move data to the cloud, they should collect all the metadata and intelligence they can, and then “decide where and how and when that data needs to move based on the use case that they’re working on,” Bond says. Other options include using a mesh or fabric approach and data virtualization or federation, where data stays at its origin but a virtual view of it is created.  

Further, Bond says, organizations are not only “accountable for the original version of every single piece of data that they create and manage, they’re also accountable for every copy of the data that they make.” When data is loaded into a cloud and then transformed to solve a business problem, that copy needs to be governed with all of the same rules as the original, he notes.