A guide to data governance principles and best practices

Data is the lifeblood of all modern businesses. It’s the most valuable asset to an organization, underpinning all vital business operations, strategies, and intelligence. However, data that’s mismanaged can become a company’s biggest liability and lead to severe reprimands, potentially significant penalties, and a damaged reputation.

That’s where data governance comes in.

In this article, we’ll cover exactly what data governance is and what the main benefits and principles behind it are. We’ll also explore how you can build an effective data governance strategy using what we believe to be the best practices.

What is data governance?

Data governance is the process of improving and maintaining a business’s data integrity and compliance by establishing and adhering to internal data policies and protocols. 

In simpler terms, it’s essentially related to how business data is gathered, stored, handled, and disposed of. A set of rules that adhere to security standards guides each step of the entire data lifecycle, protecting both the business and its customers.

The ultimate goal of the data governance framework is to ensure the business has high-quality, reliable data and that it maintains compliance with regulations. 

Data governance vs information governance: What’s the difference?

Before we move on, it’s important to understand how data governance and information governance differ. The two terms are often used interchangeably, but there are some key defining characteristics for each.

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Both are used to improve data management. However, while data governance focuses on the technical aspects of collecting, organizing, and validating data, information governance is concerned with utilizing data to meet business goals and benefit customers, as well as establishing policies and procedures to maintain data security and compliance. 

Essentially, the purpose of data governance is to ensure an organization uses data effectively and appropriately, secures it throughout its lifecycle, and adheres to all data regulations. Information governance plays an overarching role by laying out the game plan for how an organization will handle data, including establishing procedures for team members to follow when interacting with it.

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The essential principles of data governance

The Data Governance Institute (DGI) has established eight essential principles for data governance. They are: 

• Data integrity: Stakeholders who are part of the data governance framework should have integrity and honesty when discussing factors that impact data.
• Data transparency: Organizations should be clear about how, when, and why they introduced each of their data processes.
• Data auditability: Data governance will include maintaining documentation to demonstrate compliance and to meet operational requirements. 
• Data accountability: There should be precise information regarding who is accountable for cross-functional data processes and controls.
• Data stewardship: The responsibilities of people (data stewards) who are accountable for data should be clearly defined.
• Checks and balances: When defining accountability, organizations should ensure a system of checks and balances is in place with people who collect, manage, or use data.
• Data standardization: Businesses need to develop a standardized format for their data and maintain it. 
• Change management: Every organization should have a system for tracking and controlling how data changes over time. 

Regardless of your organization’s goals, data stakeholders should make sure that data governance policies are in alignment with their purposes. These principles may help stakeholders focus on what is important and prioritize accordingly.

How to build an effective data governance strategy

Data governance is a high-profile area of business operations, so it isn’t something to take lightly. An effective data governance strategy should satisfy the needs of the business and guarantee complete compliance with relevant standards and regulations.

If you’re looking to build your own strategy, we recommend following these steps:

1. Define an objective
2. Establish a framework
3. Assign roles and responsibilities
4. Develop data policies and standards
5. Implement data quality management
6. Ensure data security and compliance

Let’s take a look at each of these in a little more detail.

1. Define an objective

Before you start putting things in place, you need to have a clear understanding of why you’re using time and money to devise this strategy.

Most businesses will keep their objectives broad and all-encompassing, covering aspects such as regulatory compliance, quality of data, and analytics. But if you don’t know the reasons for creating the strategy, you risk developing an incomplete and somewhat vague strategy.

2. Establish a framework

Once you’ve clearly defined your objective, it’s time to move on to laying out the framework of your strategy. It will act as the central skeleton for the strategy, and you’ll add individual components of the strategy to it. Some common features of a data governance framework include a clear ownership and accountability hierarchy, codified policies and procedures, and standardized process workflows.

All-in-one data governance platforms such as RecordPoint can make this process easier for you by providing end-to-end data governance frameworks that incorporate data discovery, minimization, mitigation, categorization, privacy, and security, all in one place.

3. Assign roles and responsibilities

Data and data governance are complicated areas, so it’s essential to identify who is responsible for what in your strategy. The best approach is to segment roles and responsibilities into specific areas rather than having a single person or two responsible and accountable for everything.

Some ofthe typical roles you’ll expect to see as part of a data governance plan include data owners (as in data on sales, company finances, etc.), a data steward (responsible for data quality and compliance), and a data governance council (a cross-department set of stakeholders responsible for overseeing data management and use).

4. Develop data policies and standards

For each operational task that involves data in some way, there must be a data policy that clearly identifies and defines everything related to that data. For example, a finance team will need a data policy that covers areas such as how finance data is collected, stored, used, and disposed of.

It’s important to create policies for operations such as issue resolution, change management, and lifecycle management. You should also develop standards for what constitutes acceptable data in terms of source, quality, age, and accuracy.

5. Implement data quality management

Not all data is “good” data. Some might be outdated, missing vital parts, or simply inaccurate. So any data your business may use for various strategic purposes needs to go through rigorous evaluations to guarantee its validity.

This includes evaluating what the key data assets are and creating data validation criteria. You’ll also want to make sure you schedule regular data audits to ensure data standards remain consistently high.

6. Ensure data security and compliance

Even after you’ve put all other aspects of a framework in place, if your business data is at risk, it won’t do you any good. So once you have a clear understanding of all your data assets and locations, you’ll need to implement robust data security and privacy protocols.

Tools that enable high-level access control using the principle of least privilege access ensure that anyone who doesn’t need the data doesn't have access to it. You should also use encryption where possible, as well as data loss prevention (DLP) services. 

And you must always make sure that all data practices and processes are compliant with all relevant regulations and standards.

Data governance best practices

When creating a data governance framework and policies, you need to consider the most efficient ways of enabling data usage and access. We don’t want you to waste time on developing a substandard strategy.

So here are some of the best practices we believe will help you achieve a high-quality data governance framework:

• Use the best tools: There are hundreds of tools and applications out there claiming to be the best. Do your research to identify which data governance programs are consistently recommended, and make sure they do exactly what you need them to do.
• Foster a data-driven culture: Data governance will only be effective if every party recognizes its importance, so make sure all employees treat data responsibly and use it to inform business decisions.
• Start small and iterate: If your business has no data governance in place, it might be worth initially implementing it for a department rather than the whole business. Don’t roll it out across other departments until you see progress. 
• Prioritize staff training: To ensure that your data governance strategy is successful, you’ll need to make sure all employees understand why data is important and how best to manage it.
• Keep stakeholders informed: All relevant stakeholders should be kept up to date in the event of data policy updates or regulation changes.

The benefits of strong data governance

A strong data governance framework benefits nearly every aspect of an organization. Data governance is used to meet a multitude of business objectives. By harnessing data, an organization is in a much better position to make accurate data-driven decisions and improve business performance. Here’s what you can do with an efficient data governance structure in place:

• Improve compliance: Depending on your industry and local regulations, your organization may be subject to legal requirements for keeping sensitive data secure. Data governance can ensure you’re meeting compliance regulations.
• Make confident decisions: Data governance means your data is accurate, timely, and reliable, all of which are critical for making business decisions that will drive growth in a fast-paced environment.
• Lower costs: Many organizations are storing data they don't need. Storage costs can quickly add up, and excess data slows down the system. Data governance will have a policy for removing redundant, obsolete, and trivial (ROT) data, meaning you’ll only hold on to essential records.
• Manage risk: Without strong governance, your organization risks missing the red flags of an intruder. Cybercrime is a persistent threat, so you need to keep track of who is accessing sensitive files. 
• Increase efficiency: Setting up data policies makes your organization more efficient since everyone will use data that meets high-quality standards. Greater accessibility doesn't mean you lose reliability.
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Everyone in your organization will benefit from effective data governance, since all departments rely on data to function. The marketing team can use data to create impactful messages. The finance team will benefit from having accurate reporting. Your product team will gain insights into customer behavior and desires. Senior management will have the reliable data they need to make informed decisions. And your legal department will be glad to have evidence that your organization is following compliance regulations. 

What happens when an organization fails to govern its data?

Poor data governance leads to breaches, compliance violations, inefficiencies, and the erosion of consumer trust. Here are examples of where data governance landed companies in hot water.

Unity Technologies

In 2022, Unity Technologies experienced a significant data quality issue when the company’s ad targeting tool, Audience Pinpointer, ingested corrupted data. This led to inaccurate ad placements, leading to a $110 million loss in revenue.

Ticketmaster

In 2024, Ticketmaster discovered unauthorized activity on an isolated cloud database managed by a third-party provider. Due to a lack of visibility and governance over their data, the company compromised the personal and financial information of 560 million customers, resulting in unauthorized transactions and identity theft.

Examples of effective data governance

While there have been instances of poor practices, there are many more examples of companies that demonstrate excellent data governance. Here are three that demonstrate the impact that governance can have on a business.

Bank of America

Bank of America had a serious problem. The rapid growth of fintech companies, the impact of failing profitability, and increasingly tight regulations meant a change was required for the organization to keep pace.

By centralizing data governance and creating solid foundations for data ownership in addition to improving data quality management, the bank was able to transform its data standards and achieve total data visibility. This allowed the bank to consistently achieve compliance, improve operational efficiency, and deliver personalized experiences to customers.

The Mayo Clinic

The Mayo Clinic used data governance to enhance patient care and deliver better client experiences. Their governance strategy involved consolidating vast amounts of patient data under their own Clinical Data Analytics platform.

This data platform unified all patient data across the organization, providing a single source of truth for predictive modeling and personalized medicine. By employing advanced algorithms to analyze patient histories and real-time health data, physicians received insights that they could use to detect diseases early and administer treatments precisely and proactively.

What does the future of data governance look like?

The future of data governance looks exciting. Here are two key trends that will shape the coming decade.

1. AI-powered data governance

‍The integration of artificial intelligence (AI) and machine learning (ML) is already changing the core principles of data governance.

In the future, however, we can expect to see total automation of critical data governance processes through AI. Processes related to data observability, for instance, can be completely automated, increasing productivity and improving data accuracy.

2. Decentralized data governance‍

With the advent of blockchain technology, it’s likely we’ll see a shift toward distributed data governance models, where decision-making is democratized across a network of participants rather than being centralized in a single authority. This will offer more flexibility for data owners, enhance transparency and accountability, mitigate risks, simplify data sharing, and streamline compliance with data protection laws and regulations.

What are some of the challenges?

However, these advancements aren’t without their challenges. Businesses will need to manage more data than ever from diverse sources, including Internet of Things (IOT) devices, social media platforms, and new cloud services. Maintaining data governance and achieving increasingly strict compliance standards will remain a constant concern.

Businesses will also need to work harder to maintain data sovereignty as the world becomes increasingly interconnected and more countries develop their own stringent legal frameworks. The challenge will always be striking a balance between innovating and ensuring compliance with strict standards, such as achieving evolving GDPR standards.

How RecordPoint can help

Creating data governance is crucial to ensure a company’s data is relevant, timely, and secure. Implementing your data governance framework and policies means you need a solution for the full data lifecycle, including data inventory, categorization, privacy, and minimization.

RecordPoint streamlines data governance with a centralized data inventory. Connectors empowers you to connect all your data sources, allowing you to build a true data inventory and implement one set of policies for all of your data. You’ll have all the tools you need to create your own file plan and retention schedules, set data categorization rules, and activate automated data categorization.

FAQs

Are data governance tools expensive?

As with most things, you’ll likely pay a premium for high-quality tools from reputable companies. The price is largely dependent on a number of factors aside from the company’s reputation and market presence, including the scope of the tools needed, the features required, the number of data sources it has, and the type of deployment required.

Open-source tools will often be more budget-friendly.

How is AI being used in data governance tools?

AI already has a large presence across many business operations and technologies, and AI governance tools are no different. You’ll find AI functionality present across areas such as automatic data classification, poor data quality detection, and data usage recommendations.

What key features should a data governance tool include?

Effective data governance tools should have the functionality to carry out a number of tasks, including data cataloging, metadata management, workflow management, policy and compliance management, and data access control protocols.