Busting Common Data Governance Myths

The Importance of Data Governance

Implementing strong data governance has long been a critical – if often elusive – goal for most businesses. A lack of governance means data might not be used safely, effectively, or ethically. Data quality cannot be assured; security, privacy, and regulatory compliance can be jeopardized; data-driven decisions may wind up off-base; team productivity and collaboration can be compromised; trust among employees and partners about the data they work with and the practices surrounding it will be lacking, while customers will be hesitant to share their own data with the organization.

Getting data governance right is becoming even more pressing in the age of AI. According to the 2025 DATAVERSITY Trends in Data Management Survey, data professionals list lack of data governance as among the biggest data management challenges they face. Gartner states that by 2027, 60% of organizations will fail to realize the anticipated value of their AI use cases due to incohesive data governance frameworks. This is no small loss: When AI efforts go well, they deliver an average of $3.70 in return for every $1 invested.

How to get data governance on the right track? Mathias Vercauteren, president and principal of Data & AI Governance (DAIG) Partners, offered answers during his presentation at a recent DGIQ + EDW Conference.

What Are the Biggest Data Governance Myths?

In his presentation, Vercauteren focused on uncovering – and overcoming – data governance myths that keep organizations from implementing it successfully. Below are a few of the highlights.

Myth: You Need a Big Budget

Data governance, he argued, can be stood up on a small budget, benefitting any size company. In fact, all companies – regardless of size – should start small. Focusing on data quality for critical data elements and across true pain points will drive value, and growth can come from that small scale. “Big Bang implementations are not the right way to go,” said Vercauteren.

Myth: You Need Fancy Tools

You don’t need to invest in specialized software right away. Instead, business leaders should leverage business glossary, data dictionary, or data quality tools that are likely already in-house, often from vendors such as Microsoft. Excel and SharePoint, for example, can document, share, and track critical data elements.

Myth: You Need a Large Organization

It is not the case, he assured the audience, that data governance works only in large organizations, explaining that he has worked with corporations with thousands of people spread around the world, as well as with firms with just a few hundred people. Smaller organizations tend to have a lower complexity to start with, which makes for easier implementation with simpler tools. Big businesses have more siloed departments and other complications. That’s one reason why pinpointing the right use case really helps.

“Properly engineering the scope, you can have a short-term saving in specific spending areas from 50 to 20 percent,” he said.

Myth: You Need a Large Team

Implementing data governance in a specific department or process allows for manageable scope and quick, measurable outcomes. Not only that, but you optimize people resources too. Analysts often recommend small governance teams because they are more agile and collaborative, scale through distributed stewardship, and can deliver a faster time to value due to having a more focused approach.

Vercauteren recounted a two-month project he worked on with a bank with just two people, a small budget, and a small scope. “We created a business glossary on SharePoint with a data dictionary. So basically, something to document business terms, something to document data elements,” he said, as well as a short and lean charter stating the data governance roles and responsibilities, vision, principles, and operating model, a simple quality dashboard in Excel, and a plan for communication and training. “That was the seed for future growth.” Some three years later, the bank had a proper data governance business glossary tool – growing documented assets, quality, and measurements.

Myth: Data Quality Will Fix Itself

Vercauteren emphasized the importance of not buying into the idea that data quality fixes itself over time. Data degrades without intervention. “Active management prevents costly errors,” he said. “If you don’t do anything, it degrades and increases the risks, increasing the costs.”

Misinformed decisions, operational inefficiencies, and compliance risks are potential outcomes without regular monitoring and cleaning of data. If issues persist, root cause analysis is necessary, minimizing the chance that errors will return.

While it’s difficult to hit the 100% mark when it comes to improving data quality, it’s essential to try. Gartner estimates that poor data quality costs organizations almost $13 million every year on average. Vercauteren said:

“Companies today do not want to wait six months to come up with a process to fix data quality. We have this AI hype coming along, so we need to fix the data now – or even yesterday.”

Myth: Data Governance Is Too Complex for Non-Data People

Starting at the C-level and working across the organization, it’s important that people have an awareness of what data governance means – that you define processes and how you interact with data – and why they’re doing it. “You need to be able to communicate, why are we changing … why are we doing data governance?” And do it as simply as possible.

For example, when he communicates with business people, he compares data governance to other domains with which they are familiar. For financial pros, that might mean making a comparison with how they define the rules and manage the money, and how audit controls it. For anyone who interacts with that data, it’s important to define their role and ensure that they act according to the responsibilities that they’ve been assigned. It’s all about “training the people, communication, and bridging that knowledge gap,” he said.

Success comes in the form of four categories of key deliverables, he explained:

• Foundational – a business glossary, data governance charter endorsed by management, and a roles and responsibilities matrix
• Communications – awareness and training materials, and stakeholder engagement plan
• Process – data quality dashboard, critical data elements documentation, workflow and process maps to reflect processes, the core of data governance
• Success tracking – data governance metrics scorecard and roadmap

Taking the Next Step

Want to learn more about Vercauteren’s tried-and-tested approach to implementing data governance? Catch his seminar in person at DGIQ + EDW, or register to join his live online course, Data Governance Sprint.