What Is Data Governance? (A Comprehensive Guide)

The sales team inputs all kinds of valuable insights about common customer feedback into your CRM. Your marketing automation system tracks engagement across a slew of digital channels. Customer service managers monitor key performance indicators, such as call resolution and handling time.

Even with all this data at your company’s disposal, there’s still one question that should always be top of mind — is everyone using the data the right way? The only way to be sure is to have a proper data governance strategy in place.

Data governance takes that concept and applies it to the information that companies of all sizes collect, manage and store.

Here’s what you’ll learn: 

• Data governance defined
• Data governance parameters
• Why data governance is important
• How data governance works 
• Data governance is an ongoing conversation

Data governance defined 

Governance is broadly defined as the rules or policies by which companies are run and how decisions are made. Large companies in particular have had to be mindful about governance, especially if they are subject to industry regulations.

Data governance involves the management and regulation of data assets to ensure they are used effectively, efficiently, and responsibly within an organization. It encompasses the processes, policies, standards, and metrics that ensure the integrity and security of the data used across various business units. (Back to top.)

Data governance parameters

Whether you operate in a sector like retail, manufacturing, or some other vertical market, the general principles of data governance are largely the same. The policies or rules you establish need to cover the following five areas:

• Access: Who is authorized to view, edit or use the data?
• Accuracy: What provisions are in place to ensure the data is free of error and up to date?
• Privacy: How does the way data is handled ensure it remains private (especially in the case of customers’ personally identifiable information, or PII)?
• Security: What safeguards are in place to ensure the data will not be threatened by cybersecurity attacks from external third parties, or even rogue actors from within the company?
• Retention: How long does the company need to hold on to this data based on legal or business requirements? Once it’s no longer needed, how can it be disposed of in a safe way? (Back to top.) 

Why data governance is important

The governance of a company’s data matters because it’s core to how companies deliver winning customer and employee experiences with trust at the heart of every interaction.

When the data is well-governed, for instance, employees and customers know they can entrust the company with their data, and can continue to provide additional data with confidence.

For the business itself, meanwhile, good data governance helps avoid disconnects that cause inefficiencies, jeopardize customer relationships or even lead to costly compliance penalties and reputational harm. On the other hand, it can empower companies to know their customers better, and personalize every interaction.

What’s important is that data governance is well understood by everyone in the company, and that it is applied consistently and with attention to detail. (Back to top.) 

How data governance works 

Data governance works by establishing clear guidelines and procedures for data management. This includes: 

• defining who can access data, 
• how it can be used, 
• and who is responsible for maintaining its accuracy and security. 

It involves collaboration across different departments to align data management with organizational goals and compliance requirements.

If you haven’t tackled data governance before, here are some of key action items:

Identify all data assets and their current state

To get a clear picture of all your data assets and their current state, spend some time conducting internal research. 

You’ll need to identify all the different types of data your enterprise collects. This could include everything from customer data and sales data to customer service, maintenance asset data, and more. 

Next, assess the current state of this data. Is it scattered across different systems? Is it up-to-date and accurate? Are there any gaps or inconsistencies? By understanding what data you have and its condition, you can start to see where improvements are needed and prioritize your efforts.

Define or adopt the appropriate standards

What does quality data mean to your company? This could vary depending on your use of it, but there are five major things you should prioritize. 

First, accuracy. In the age of generative artificial intelligence (AI), data quality and accuracy can make or break an AI app. Your data should be correct and free from errors. This means regular checks and updates to ensure everything is up-to-date. 

Next, consider completeness. Make sure all necessary data is collected and nothing important is missing. Consistency is also key. Your data should be uniform across all systems and platforms, avoiding any discrepancies. Timeliness is another important factor. Data should be available when needed, without delays. 

And, most importantly, security. Protect your data from unauthorized access and breaches. Focusing on these standards ensures the reliability of your data. And the more your teams trust your data, the more they’ll use it and get value from it.

Train and empower employees 

The success of your data governance strategy depends on how well your employees understand and take action on it. They should know how your strategy is realized through their daily data handling and the importance of compliance. Ensure that all your teams are well-equipped with resources such as articles, training materials, and certifications.

Encourage active engagement within communities for ongoing support and insights on data governance, security, and integration. This approach will not only inform but also involve them in maintaining high standards of data governance and security.

Establish a cadence for data governance reviews and updates

As your business grows and technology evolves — take generative AI as a prime example — your approach to data governance needs to adapt. 

Set a regular schedule for reviewing and updating your data governance strategies. This could be quarterly, bi-annually, or annually, depending on your business needs. Use these meetings to assess the effectiveness of current policies and to ensure that your policies reflect new technological advancements or regulatory requirements. 

Keeping your data governance proactive rather than reactive not only ensures compliance but also enhances operational efficiency.

Commit to a set of data governance metrics

What does governance success look like in your organization? Consider metrics such as the reduction in data errors, improvements in data quality scores, or even cost savings related to more efficient data management. 

By setting these benchmarks, you can monitor progress and make informed decisions to continually refine your data governance practices. This commitment to measurable outcomes will help ensure that everyone in the organization understands their role in data stewardship and is aligned towards common goals. (Back to top.)

Data governance is an ongoing conversation

A great employee experience means there should be no question marks around data governance.

Think of data governance not just as a set of rules but as a vibrant, ongoing conversation across your organization. To ensure everyone is clear and confident about how to handle data, it’s crucial to keep the lines of communication open. Develop a communications plan that reaches everyone, from your C-suite to your newest hires. 

Regular updates through an employee newsletter, intranet posts, or discussions on a dedicated Slack channel can help maintain clarity and cohesion. By making data governance a part of the daily conversation, you foster a culture that values and understands the importance of good data practices.

A strong data governance strategy will make your business more transparent, more collaborative, and more intentional in how it makes use of its most strategic asset.