A guide to data governance principles and best practices

Creating data governance is crucial to ensure a company's data is relevant, timely, and secure. Learn the essential principles and best practices for managing data through the entire lifecycle.

Data is easily one of the most important resources for organizations. However, organizations gather a lot of data without any plan or process for it. According to the Pulse of the Industry Report 2022, as many as 75% of respondents have no tools or process for managing structured data across line of business systems.​

But implementing a system to collect and process data can make it more accessible for the organization. That's where data governance and its principles come into play.

What is data governance?

The Data Governance Institute (DGI) defines it as "...a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods."

In more simple terms, data governance is the internal policies regarding how data is gathered, stored, handled, and disposed. Throughout the data's entire lifecycle, there is a set of rules to guide it and ensure security and compliance standards are in place.

Data governance includes actions, technology, and processes for an organization's data. The ultimate goal of the data governance framework is to ensure data is high-quality, reliable, and protected.

What is the difference between data governance and information governance?

Before moving on, let's discuss the difference between data governance and information governance.  Both are used to improve data management. But while data governance focuses on the technical aspects of collecting and organizing data, information governance concerns itself with how the data can be used for business goals, customers, and regulators. 

Essentially, data governance is about the processes for the lifecycle of data. Information governance has a broader scope and can include the insights gained from efficient data.

What are the business advantages 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 are a few business advantages of implementing an efficient data governance structure.

• 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 are meeting compliance regulations.
• Make confident decisions: Data governance means your data is accurate, timely, and reliable. These factors are crucial to making business decisions that will drive growth in a fast-paced environment.
• Save costs: Many organizations are storing data that 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 only hold on to essential records.
• Manages risk: Without strong governance, you put your organization at risk of not noticing red flags of an intruder. With cybercrime a consistent issue, you need to keep track of who is accessing sensitive files. 
• Increased 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.

Everyone within your organization will benefit from effective data governance. Marketing can use data to create impactful messages. Finance 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 also be happy to have evidence that your organization is following compliance regulations. 

The essential principles of data governance

The DGI established eight essential principles of data governance:

• Data integrity: Stakeholders who are part of the data governance framework should have integrity and honesty when discussing factors that impact data.
• Data transparency: It should be clear how and when data processes were introduced.
• Data auditability: Data governance should be auditable, especially for compliance and operations auditing requirements.
• Data accountability: Define who is accountable for cross-functional data processes and controls.
• Data stewardship: Define responsibilities for people (data stewards) who are accountable for data.
• Checks-and-balances: When defining accountabilities, you should ensure there is a checks-and-balances system in place with people who collect, manage, or use data.
• Data standardization: Introduce and support the standardization of an organization's data.
• Change management: Implement a system for controlling how data changes. 

Regardless of your organization's goals, data stakeholders should ensure data policies are in alignment with their purpose. These principles may help stakeholders focus on what is important and prioritize accordingly.

Data governance best practices 

When creating a data governance framework and policies, you need to consider the process for how data is used, who gets access to it, and when they are allowed to access it. Here are a few best practices to consider:

• Planning: You need to create a data governance strategy. Consider what your goals are, how they will help your organization, and then determine the metrics you will use to determine a successful data governance process. You also need to define governance roles and who is responsible for what and when.
• Tools and capabilities: You'll need governance tools and capabilities that can help you deliver your framework and policies. These tools should aid in creating processes that are standardized, documented, and repeatable while also supporting compliance.
• Processes: When creating processes, you want to ensure you are clear about what is happening and who is in charge of it. You should document policies, controls, accountability, stewardship, managing changing data, and resolving issues.
• Regulations: External laws have a say in what you do with your data. Ensure you are using solutions that are compliant with data privacy and protection regulations.
• Rules and standards: You will need to design the data governance framework and include data policies, standards, and compliance requirements. Depending on your needs, you may want to evaluate your current data governance and then update data rules or find and address gaps.
• Security: A data breach is a significant problem, especially if it exposes the sensitive data of customers. Data governance can include the classification of data to find sensitive information. Then you can give special protection to classified data with tools like access management and encryption.

What are some examples of data governance? 

The best way to show the effectiveness of proper data governance is to dive into some examples. Here are three that demonstrate the impact that governance can have. 

1. Proctor & Gamble

Proctor & Gamble established a robust data governance framework across its entire global operation to standardize its data stack and ensure it was readily available organization-wide. They achieved this by establishing a centralized system containing metrics, data hierarchies, and more, under their Global Business Services (GBS) division. 

This included embedding analytics experts within business units to facilitate data-driven decisions in real-time. All in all, this data strategy helped P&G improve sales forecasting accuracy, reduce operational costs, and improve collaboration. 

2. Bank of America

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

By centralizing data governance and creating solid foundations for data ownership, and 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. 

2. 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 organization-wide, 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 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 three times data governance issues landed companies in hot water.

• 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 $110m loss in revenue and a 37% drop in shares. 
• Due to data quality and governance concerns, Zoll Medical was forced to call all defibrillators following a discovery that they would display error messages and even fail when used. This led to $5.4 million in fines due to the brand risking loss of life. It also resulted in a massive loss in consumer trust.
• 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 financial information of over 560 million customers, resulting in unauthorized transactions and identity theft. 

What are the lessons learned from these mistakes? 

• Unify all of your data and know exactly where it lies. 
• Then, implement rigorous validation processes and checks to ensure quality before data is used to your advantage. 
• Monitor discrepancies, establish clear responsibility for quality, and conduct regular audits to protect data. 
• Don’t make assumptions about third-party data. The only true way to ensure governance is to keep a tight lid on the sensitive information you possess. 

Exploring data governance tools and technologies

Data governance tools and technologies are essential to ensure data quality, compliance, and efficient management. Here are some of the tools you should consider:

• Data catalogs: These tools help organizations discover, organize, and classify data assets. They unify data under one roof, improving data accessibility and collaboration between teams. 
• Data quality tools: Tools for data quality automate data cleansing and validation, ensuring businesses only receive high-quality data they can use for analytics. They can identify anomalies and remove duplicates, standardizing information. 
• Data lineage tools: Data lineage tools track the flow of information across data systems. In doing so, they help organizations visualize the entire data lifecycle, including how data transforms over time. This transparency supports compliance and audit trails.
• Data governance platforms: All-in-one data governance platforms like RecordPoint provide end-to-end data governance frameworks, incorporating data discovery, minimization, migration, categorization, privacy, and security in one place. 

The primary benefit of these tools is that they can automate tasks like data collection and cleaning, freeing up business time to focus on utilizing the data it possesses. They also remove the opportunity for human error, improving data accuracy.

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 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 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 more 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 the balance between innovation and ensuring compliance with strict standards, such as achieving evolving GDPR compliance. 

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 allows you to connect to all your data sources, allowing you to build a true data inventory and implement one set of policies for all of your data. You have all the tools you need to create your own file plan and retention schedules, set data categorization rules, and activate automated data categorization.