What Is Data Governance?

Business professionals value data as a key source of insight that helps their organizations create effective strategies and achievable goals. This means a business needs to employ data governance to protect and process this important information. However, when you use, store, or manage data incorrectly, it can become a liability. This potential issue is a reason for your business to implement data governance for protection and to ensure the quality and security of the data your company utilizes every day. 

The right data governance model is key to helping your business feel confident in the value and accuracy of its data, which is why taking the time to create an informed, specialized data governance model to fit your business is so important. Read more to learn about data governance, the different data governance models businesses use, and possible careers in the data field, such as a data governance analyst.

What is data governance?

Data governance is the set of policies a company uses to manage the data it utilizes from its acquisition to the end of its lifecycle. These policies include roles, processes, and responsibilities for managing different data types as they move through your company. Data governance focuses on preserving the quality, security, and accessibility of your institution’s data. It meets this goal by assigning specific data owners within your business, implementing security processes, and labeling each data set with an intended use case.

Data governance provides many different benefits to your business. First, setting these policies allows you to feel confident in the accuracy and quality of the data your business uses. Establishing policies to ensure proper gathering, labeling, and storage protocols reduces the chance that your business might utilize poor-quality data, resulting in misinformed strategic planning or incorrect insights. Secondly, data governance makes it easier for your business to comply with federal or global compliance laws, such as the Health Insurance Portability and Accountability Act (HIPAA), that aim to protect consumers and their data.

Data governance examples

Some examples of the way professionals implement data governance effectively at different types of businesses include:

• Creating data literacy training to increase the quality of data interactions company-wide

• Centralizing scattered data sources to improve accessibility

• Streamlining data sources to create one unified, trustworthy source

Data governance models

Data governance models are typically fashioned after four different structures. You can review and explore each of these models below to see which works best for your organization or needs.

Top-down models

Suppose you decide to use a top-down data governance model. In that case, you’ll select a team of data professionals responsible for creating and controlling the data used by other company members. The structure is similar to a business’s typical organizational chart, where a lead authority sets decisions and policies and then passes them down to the other team members with little to no collaboration. While this structure can be effective, it might lead to issues caused by someone at the top making an incorrect decision, which will filter down through the rest of the model.

Bottom-up models

Bottom-up data governance models work by vetting and cleaning data immediately after it’s been gathered or created. Individuals within your company working with the data take the opportunity to set standards as part of their daily tasks, such as naming conventions. These standards then travel up through the other teams. Because a single team does not inspect the data with this approach, you can sometimes have trouble ensuring the quality of the data.

Center-out models

Center-out data governance models utilize a central hub of experts, sometimes within each department, who create informed protocols and standards for handling your company’s data. These hubs work together to successfully spread data governance policies across a company for everyone to know and use. This structure is a middle ground between the bottom-up and top-down models.

Silo-in models

With a silo-in data governance model, you can invite experts from across your company to provide input into creating data governance policies. This provides a truly collaborative experience that helps create policies addressing multiple needs, goals, and viewpoints.

What is data governance used for?

For your business, the primary use for data governance is ensuring data stays accessible, secure, private, and compliant with laws. The policies implemented under data governance help your business meet several important goals, including minimizing risks to your business, increasing data value, and improving both internal and external communication.

Who uses data governance?

Data professionals in various industries typically use data governance. A data governance analyst is one such example of a role that uses data governance. As a data governance analyst, you are responsible for supporting a business’s data use by creating, recommending, and implementing different data governance policies. 

Data governance analysts might seem similar to data analysts. Still, the former role is typically responsible for creating the governance policies that manage the data, and the latter typically works with and analyzes the data itself to produce valuable insights. If you decide to become a data governance analyst, this position typically requires a bachelor’s degree and some experience with data management. A data governance analyst earns an average salary of $90,023 [1], which will vary based on your company and location, skills, years of experience, and other factors.

How to start a data governance plan

To create a data governance plan, the first step is to identify the needs of your specific business. An effective data governance policy needs to solve problems and deliver stated outcomes. Discerning the problems and outcomes for your business is key to setting up the right kind of data governance model. 

Then, look at how your business currently gathers, stores, and manages its data. This allows you to see how data governance policies might help to improve your data’s quality, security, and accessibility, which can lead to better decision-making, lower costs, and less risk.

Once you identify how this system can help your business, it’s time to design the data governance policies to extract the most from your data. You’ll want to choose a governance model type that best fits your situation and then write out how you want your team members to interact with the data. You can assign different roles and responsibilities as needed.

How to work in data governance

If you want to work in data governance, develop your data management skills. A bachelor’s degree in computer science, data management, or information technology is one way to achieve this—not to mention that a four-year degree tends to be a requirement for this role—or you might find opportunities in your current position to practice data management and gain real-world experience. Additionally, a range of positions in which you could use data governance, depending on your experience, are options. Some of these include the following, along with their average annual base salaries:

• Data quality analyst: $74,072

• Data steward: $80,492

• Data governance consultant: $128,100

• Data policy analyst: $105,657

• Data governance project manager: $112,359

• Data governance lead: $112,093

• Master data manager: $97,343

• Data governance architect: $132,154

*All average annual salary data is sourced from Glassdoor as of March 2024 and does not include additional pay, such as commission and benefits.

Learn more with Coursera.

Sharpen your data management skills and learn about how data governance might benefit your business with courses and Professional Certificates on Coursera. With options such as Erasmus University Rotterdam’s Digital Governance or IBM’s Introduction to Data Engineering, you’ll have the opportunity to learn about foundational knowledge and key skills to either help introduce a data governance program into your business or start your own career working within data governance.