Modern Data Governance: Trends for 2025

Key Takeaways:

• Prioritize metadata maturity as the foundation for scalable, impactful data governance.
• Recognize that artificial intelligence is a data governance accelerator – and a process that must be governed to monitor ethical considerations and risk.
• Integrate data governance and data quality practices to create a seamless user experience and build trust in your data.
• When planning your data governance approach, start small, iterate purposefully, and foster data literacy to drive meaningful business outcomes.

As we reflect on the data management outcomes our customers achieved in 2024 and the goals they have for their 2025 business initiatives, one thing is clear: modern data governance remains at the heart of successful data strategies.

The past year brought significant changes, from the growing importance of metadata maturity to the increasing convergence of data governance and data quality practices. For your organization to thrive, you need to be ready to embrace these advancements and pivot your approach where needed.

Nicola Askham, the Data Governance Coach, recently joined Precisely’s David Woods, Senior Vice President of Global Services, and Ken Beutler, Vice President of Product Management, to share their thoughts on the most important trends in data governance for the coming year and beyond.

Watch their full conversation for all of the insights and read on for a recap of some of the top takeaways.

Key Trends in Modern Data Governance

Reflecting on the past year, the panelists highlighted several important shifts in the data governance and data quality landscapes.

1. Metadata maturity is key to a strong foundation
   Metadata management maturity is growing in importance, especially with the rise of new data management architectures like data mesh (which takes a decentralized approach) and data fabric (which takes a unified approach). These architectures have both emerged to accelerate the delivery of trusted data to users so that it’s actionable and accessible for informed decision-making.

Woods emphasized that many organizations start out with the question of which architecture is best for them – but where they really need to focus first is on the critical role of metadata maturity: “What I tell folks is, foundationally, let’s focus on rich metadata and then building out foundational and then democratized capabilities around data governance to the organization.” The panel agreed that metadata maturity is essential for scalability and driving business outcomes.

2. AI’s role in data governance is growing – and so is AI governance
   AI (artificial intelligence) has emerged as a powerful enabler in modern data governance – automating repetitive tasks like data cataloging. But as Woods noted, AI isn’t a replacement for people – it’s an augmentation tool. “True data governance really comes down to context,” he explained, emphasizing that while AI enhances operational efficiencies, people remain central to delivering business value.

Beutler further elaborated: “The large language models are only as good as the data that they’re trained on. And there is still a significant portion of knowledge and information that are retained within the people that are managing the data and part of the data governance and quality organizations,” he says. “As we find ways to unlock that, AI will be an even larger accelerator.”

The strong link between data and AI outcomes is also resulting in a shift towards more AI governance. Askham notes that as businesses account for AI’s ethical considerations and risks, like unintentional bias, she believes that “we’re going to have a lot more data governance teams taking on the AI governance role.” This shift will necessitate new skills and collaboration with AI experts.

3. Motivations for data governance expand beyond regulations
   Historically, many organizations pursued data governance primarily to meet regulatory requirements. However, Askham observed a refreshing shift – noting that more organizations tackling data governance in the past year haven’t been in regulated sectors.

“They’re doing data governance because they understand the business value it’s going to bring. And that’s really refreshing,” she says. This shift underscores the growing recognition of data governance as a strategic enabler.

4. Data governance and data quality converge
   The line between data governance and data quality is blurring. Woods notes that to this point, the differences between governance and quality – and when to tackle each – have often been confusing for organizations, and we’ll likely see an increase in integrated approaches. “The coalescence of data governance and data quality is getting tighter and tighter over time … and I think it’s a good thing,” he says.

Adding to that point, Askham states that “Data governance and data quality should be done by the same team,” emphasizing that business users typically prioritize high-quality data, but are often less likely to work with the governance team, because they see governance as being less impactful. The reality, she says, is that “They should be seamless from the business user’s point of view. So, the closer we can get them together, the better.” 

Tips for Data Governance Success in 2025

Our experts also shared their insights into the practical strategies you can deploy in your data governance journey – in 2025 and beyond.

1. Align people, processes, and technology

Successful data governance requires a holistic approach. Beutler says that when considering your strategy, “Don’t do data governance to do data governance. There needs to be a reason behind it – know what you want or what are you actually buying the solution for.” You need to have a deep understanding of what benefits the capabilities will provide for your business. And when measuring the adoption of a solution, Beutler adds, “it’s not just necessarily how many people are using the software itself, but it’s really trying to understand what all the people, the processes, and technology are doing together in order to deliver on some type of business outcome.”

Woods echoed this sentiment, emphasizing the importance of a roadmap for both data governance and quality initiatives: “A roadmap should include technology, people, and processes.” Tools are important, but they need to complement your strategy.

2. Start small, iterate, and grow
   Both Beutler and Askham highlighted that more organizations are realizing the value in starting small with data governance initiatives. Regardless of the size of your organization or where you may currently be in your data governance journey, taking a more pragmatic approach to data governance that focuses on the quick wins can be hugely beneficial.

Beutler explains, “We’re seeing more team-based governance programs starting in support of analytics or even smaller AI projects. Or we’re seeing a single business unit being able to find and measure success with governance and then planning ways to extend that further across the organization.”

Askham adds, “Trying to do data governance as a ‘big bang’ approach doesn’t work … start small, learn, adapt, change, and roll it out.” An iterative approach that’s purposeful and intentional allows your organization to learn, adapt, and expand your programs effectively.

3. Build a data culture
   Data literacy plays a crucial role in modern data governance programs. “As we federate and democratize data governance capabilities, irrespective of kind of what architectural approach organizations are thinking about … data literacy is going to play a big, big role,” Woods says.

He goes on to explain that data literacy helps ensure that your users don’t just know the data terms themselves, but understand how that data can be used in the context of their business processes – that way, they can drive meaningful outcomes and avoid inadvertent biases. He emphasized that organizations need to make it easy for employees to “do the right thing” by providing tools like business glossaries and clear data definitions.

Woods also highlighted the importance of tailoring governance efforts to different needs – predictive analytics, for example, may require lighter governance, but compliance or financial reporting needs tighter controls.

Take the Next Steps in Your Data Governance Journey

Which takeaways from our panel resonate most with your own data governance journey? How can you further improve your strategy moving forward?

When you take steps like prioritizing metadata maturity, fostering a strong data culture, and taking an integrated approach to data governance and quality, you can foster greater collaboration between your teams and unlock real value from your data governance initiatives.

And as you plan, remember to start small and embrace the quick wins, iterate purposefully, and align people, processes, and technology for the most impactful outcomes. These steps will help you address the complexities of modern data management and position your organization for transformation.

To learn more about modern data governance read our eBook Four Steps to Improved Data Governance – a Business First Approach.