Data Governance: Framework and 7 Best Practices

Businesses collect large amounts of data from their operations. Using business intelligence (BI) tools, they can transform this data into insight. Businesses also have employees that want to access this data. Across an organization, teams can use data for many different purposes to improve many different aspects of operations.

The problem, though, is that not every employee and team should have access to all of a business’s data. This is for many very good reasons. Often, it’s a matter of confidentiality; businesses don’t want to make sensitive information public to every employee.

However, it’s also a matter of usability. At large businesses, there may be hundreds or thousands of different data sets to manage. Unless there’s some way to narrow this data, there’s no hope that someone will ever manage to access the data that pertains to them. That’s where data governance comes into play. 

What is data governance?

Data governance is the practice of controlling access to data. Different strategies are used to make sure data is secure, available, and of quality. The practice of data governance helps to ensure data is accessed and managed in a consistent way throughout an organization. Without a data governance strategy and toolkit, organizations face insecure, inaccurate, and poor-quality data. Modern BI tools can be leveraged as robust, flexible data governance tools, helping business leaders ensure the right data reaches (only) the right people. 

What is a data governance framework?

A data governance framework is a set of policies, procedures, and processes used by an organization to practice data governance. This framework is used to protect, access, manage, and utilize data; its purpose is to provide an organized method of doing so. 

The individual components of a data governance framework depend on the organization and its goals. However, it typically includes the following:

• Policies and procedures: In other words, ground rules to govern how data is accessed, used, managed, and updated in the organization.
• Responsibilities: Assigned roles for taking on the quality, accuracy, and compliance of the data. These roles may include data owners and stewards
• Security: Means of protecting data and ensuring compliance. 

• Risk management: Methods of managing risk associated with data handling.

• Quality management: Methods of keeping track of improving the quality of the organizational data. 

• Metadata: Managing the information about the data. 

Why is data governance important?

So, why does data governance matter? At times, in business, the practice may seem more like more trouble than it’s worth. However, the reality is anything but. Data governance is the difference between an organization that loses millions of dollars to data breaches and one that doesn’t. Having a data governance framework is the difference between data that is accessed by rogue employees and data that is only seen by the correct people. Data governance is a key element of any organization as well as any modern BI tool integration. 

Managing data at scale

One of the major challenges of managing data at a large scale is making sure that data is getting to the right people. A business may collect hundreds or thousands of different data streams, but any given employee will most likely only need a few to do their job. So, when building a data governance strategy, it’s important to consider how you’ll manage large amounts of data and get it to the right people through the correct channels.

Timely access to data

Like wise, data managers don’t want employees to have to sift through all of a company’s data to try to find the information that they need. Finding valuable data should be easy, so that employees don’t have to spend a lot of time navigating their BI tool to get data.

Governance is an important element of narrowing the data that’s visible to an employee. Using data governance tools, data managers can limit access to data sets that are irrelevant to an employee.

For example, a regional sales manager doesn’t need to see company-wide sales data to do their job. It’s much more useful for them to see the data that only relates to their region. A data manager can restrict this manager’s access to other regions’ sales data so that they can focus on their own region.

Data governance is also important to businesses that want to communicate data to their clients. If a client logs into their BI tool, they should only be able to see their data, not those of other clients. With data governance solutions, clients can only see their own data. 

7 Data governance best practices

When building a data governance strategy, there are some best practices to follow for the greatest results. These techniques will help you ensure that your data remains accessible, secure, and of high quality. 

1. Lead with Your Goals and Objectives

Start by considering your goals and objectives for data governance. Are you wanting to improve data security? Are you concerned about compliance? Is your organization’s data out of data and poor quality?

No matter your goal, start by focusing on your main motivations for building a data governance framework. For best results, align your goals with your company’s high-level initiatives. 

2. Establish a data governance framework

Next, establish your data governance framework. It’s important to consider how you’ll organize this system. For some, a centralized approach makes sense, meaning that there’s a central authority that manages data governance for the entire organization. Often, this is handled by a business’s IT team or data science professionals.

Other organizations use a more decentralized approach. In this framework, departments and teams have more control over their own data governance. It’s often more agile, and is useful for those without dedicated data professionals, but it can be more prone to error.

3. Get buy in

A data governance strategy is going to cause change within the organization. Whenever you have change, you’ll face resistance and pushback. To get buy-in across the organization, start by getting buy-in at the top. Focus on attaining an executive sponsor.

This sponsor will help to evangelize and champion the initiative from the top down. People are less likely to resist a new initiative when it has support from leadership. 

4. Build a Data Governance Council

After you have your executive sponsor, build out your data governance council. This council will include members from each main department in your organization. The purpose of this council is to build out the framework, enforce policies, and adjust operations as necessary. As you create this council, assign roles such as data owners and stewards. 

5. Create Policies and Procedures for Management

One of the key responsibilities of the data council will be to create management related policies and procedures. You’ll need to consider how to monitor data access, measure and improve data quality, manage metadata, and protect data according to compliance, security, and privacy standards.

6. Focus employee’s data views

Next, consider each employee’s data view. You may wonder whether it’s better to give employees a narrow data view or a broad data view. The answer is that it depends on an employee’s role and how much a business expects them to act on their own initiative.

For many employees, a narrower data view will be the best choice. With a narrow view, employees are only able to access the information that’s most important to them specifically. They don’t have a lot of opportunity to look at data sources that don’t apply to them.

With this approach, employees aren’t getting distracted by other data and can focus fully on the tasks that you’ve assigned to them. However, it can limit out-of-the-box thinking and discourage collaboration.

Some employees will need a more broad data view. A broad view usually means that an employee has access to more general data and data sets that don’t specifically apply to only them. This allows employees to use more data to make their decisions and also gives them more tools to build their own, personalized dashboards and visualizations. However, some employees might get overwhelmed and need a more focused view.

Regardless, the important thing to remember is that employees should have focused data views. They should only see data that’s important to their role. Even if a sales manager has a very broad view of sales data, they still probably don’t need access to IT data or HR records.

7. Use tools consistently

Modern BI tools have many tools that affect data governance in one way or the other. Some of these are very simple, like admin privileges and sharing cards, and others are more complex. A business will probably use a blend of different solutions to build its data governance solution.

Since there are so many different options for restricting or granting access to data, it’s very important that data managers roll out their data governance solutions in a consistent way.

If a data manager uses one method to grant or restrict access to a data set in one situation, they should aim to use that same method in similar situations. For example, if a business uses modern BI tools to restrict access through an embedded portal, then they shouldn’t also give clients modern BI tools credentials to log in and see PDP-restricted data.

This way, if there’s an issue with data governance, data managers don’t have to check every system to figure out what’s gone wrong. Instead, they can check the system that would govern data in that case and see if there are any issues.

Some tools are better for governing how data is shared between individuals and teams. For example, users can share cards and datasets with other users, and give them access to data that they might not have access to otherwise. Users can also export data out of modern BI tools and share it that way.

Other tools are better for managing teams and clients. PDP, which stands for Personalized Data Permissions, allows admins to apply data permissions programmatically to large groups of users at once. Businesses can also set up embedded portals using modern BI tools, and control login credentials that way.

The bottom line

Data governance is an important element of any business’s data strategy. At its simplest level, it prevents users from seeing and accessing sensitive or irrelevant data. Used correctly, data governance frameworks can shepherd users towards better uses of data by focusing their data view.

To ensure that their data governance strategy is effective, businesses need to follow a few best practices including focusing on key objectives, creating a data governance council, and developing related methods, measures, and processes. Data managers should familiarize themselves with modern BI tools’ data governance options. Modern BI tools have data governance tools for basically every possible use case, but they need to be applied consistently for best results.

While data governance takes time, it’s worth the effort. Data is the lifeblood of organizations today and managing this information carefully provides a competitive advantage over businesses that don’t do so.