CDOIQ 2025 Recap: Data Strategy, AI Governance, and the Future of CDOs

Every July for the past 19 years, a tribe of data leaders and information enthusiasts gather together in Cambridge, MA, for the Chief Data Officer and Information Quality Symposium (CDOIQ). Touted as “one of the key events for sharing and exchanging cutting-edge ideas, content, and discussions,” CDOIQ’s purpose is to “advance the knowledge and accelerate the adoption of the role of chief data officer (CDO) in all industries and geographical countries.” 

Over the course of the week, data leaders from across industries shared ideas, best practices, and stories from the trenches, while data technology vendors highlighted new and innovative technologies and the positive impact they have on their clients. 

Not surprisingly, AI, including generative AI (GenAI) and AI agents, featured heavily in many of the conversations, sparking healthy debate over the best ways to adopt AI and how to use it to drive meaningful change throughout the business. These discussions led speakers and attendees to explore other critical topics as well, including data quality, AI and data governance, and the roles and responsibilities of modern CDOs. Many sessions also explored how today's CDOs can drive value through AI-powered data management and modern master data management (MDM) strategies.

While this is by no means an exhaustive list, below are nine insights I took away from this year’s event.

9 Insights from CDOIQ 2025

1. Without analytics and AI, the CDO role is a dead end

For years, CDOs have had an identity crisis. Inconsistent reporting lines and evolving expectations have made it difficult for CDOs to define—and demonstrate—their strategic impact. And as the role continues to mature, many CDOs find themselves working to clarify their mandate.

Savvy CDOs know that their primary focus needs to be on data strategy. But as analytics and AI become firmly cemented in modern business, one thing is clear: CDOs need to be responsible for analytics and AI, too. Given the interconnectedness of data, analytics, and AI, it makes sense for CDOs to add these elements to their purview. That way, they can develop the right strategies to capitalize on the value of each of these components and leverage them to drive even greater value of data at the strategic level. 

2. Stop creating dashboards (your time is better spent elsewhere)

CDOs take on a lot of responsibility within the organization. Too much, in some people’s opinions. As the role has evolved, many CDOs, in addition to developing data strategy, have found themselves responsible for the administrative side of data, including data quality, data governance, data integration, and everyone’s favorite: dashboard development. But in order to elevate the role of the CDO—and change the perception of the role within the ogranization—CDOs must focus their time on strategy, not tactics. 

While analytics remains an important component within a CDO’s portfolio, delegating the creation of dashboards to the people who use them is a good first step toward changing the narrative around what CDOs are responsible for—and what they are not. It’s a culture change, but one that must occur in order for CDOs to drive the value of data at a strategic level and deliver measurable business value. Some also argue that CDOs should leave data quality, data governance, and the integration of data across disparate systems to the CIO. But that’s a debate for another day. 

3. Start AI initiatives where success is a guarantee

Many organizations make the mistake of trying to boil the ocean when it comes to introducing AI. Or they invest in multiple AI initiatives because they have FOMO. Both of these approaches are a mistake. 

Instead, companies should launch their AI transformations in areas where success is guaranteed. Low-risk use cases are strong candidates, as are use cases with high data volume and data availability, measurable impact, and stakeholder buy-in. Once you’ve proven value, demonstrated ROI, and secured champions, then you can move on to more complex areas of the business. 

4. Don’t be AI-enhanced—be AI-native

Enhancing your business with AI involves adding AI to existing business processes and taking advantage of AI tools to augment the way your business operates. And it’s a good first step toward transforming your business. But if you want to be a leader in your industry, you can’t stop here. 

Instead, your business must become AI-native, and that includes using tools that are AI-native, not ones that have limited, bolt-on AI features. By adopting AI as a core capability and designing AI-first processes, you can transform your business, drive greater value from your data, and remain steps ahead of the competition. 

5. AI governance and data governance are not the same

Governance was a hot topic in many sessions this year. But instead of focusing on data governance, the conversation shifted to AI governance and the important distinctions between them. 

Data governance focuses on the quality, security, and accessibility of an organization’s data. And it ensures the data is fit for use based on the use cases prioritized by the business. AI governance, on the other hand, focuses on confidence and use of your AI systems. It includes the development of policies, practices, and frameworks that ensure safe, responsible, and ethical use of the AI systems across your organization. Together, data and AI governance provide the guardrails needed to empower your organization to capitalize on the value of your data and the benefits AI provides. 

6. You are the manager of the AI agents

AI agents are the way of the future. Not only do they provide new levels of automation for routine tasks, but they have the potential to transform customer experiences and uncover valuable insights—and they can do so at scale. 

But employing AI agents requires another role to emerge: manager of the AI agents. But what, exactly, does that mean? Managers of AI agents are the ones responsible for hiring—and firing—AI agents. They must oversee their work and provide quality checks, just as they would do for a human team, and provide feedback when things go off the rails. Managing AI agents is less emotional than managing humans, but it does require a level of technical acumen and expertise to ensure the agents are operating as expected and that the data fueling them is fit for purpose. 

7. Leading with governance (or MDM or data quality) doesn’t work

Talking to business leaders about topics such as data quality and data governance is a surefire way to halt the conversation in its tracks. That’s because you’re not telling the business what’s in it for them. 

But what if you flipped the script and led with how you can help them support their mission? Go on a fact-finding mission and ask business leaders “who uses the data and for what purpose?” It’s also important to ask where the data comes from. Inevitably, they’ll share not just answers to your questions, but also the issues they are facing when it comes to their data and MDM strategy. They’ll reveal how incomplete or untrustworthy data is, leading to inconsistent insights. And they’ll ask why data standards and accessibility vary system to system. Listen to what they have to say, assess the data, and then show them how you can address their pain points so they can achieve their mission faster. 

8. When it comes to strong data leadership, curiosity doesn’t kill the cat

Throughout the conference, many discussions turned to the qualities CDOs need to effectively transform their organizations. While technical chops are important, they are no longer the be-all and end-all they were in the past. 

While having the ability to use different data- and AI-related tools and technologies is important, so, too, are skills such as curiosity and skepticism. In addition, CDOs must be strong communicators and storytellers. And they need to model the behavior they promote. It’s these skills that help them win the hearts and minds of others across the business. Which brings us to our final insight.

‍9. Change mindsets and change culture

CDOs are fundamentally change agents. Through their work with data, analytics, and AI, CDOs are transforming modern business and elevating the value of data throughout the organization. 

But they can’t do it alone. That’s why it’s critical for CDOs to change the mindsets of people throughout the organization so they, too, become ambassadors for change. These ambassadors speak for the CDO when they are not in the room. They advocate for the work the CDO is doing and evangelize the CDO’s results to others across the business. 

Done well, the CDO plus their ambassadors can effect true culture change, where everyone across the organization understands the importance of data, analytics, and AI and how to use them to drive the business forward. 

The Next Chapter for CDOs and Data Strategy

As I reflect on the sessions, it’s clear that we are on the cusp of monumental change. Data and AI are fundamentally changing the face of modern business. And with many executives viewing AI as “the most transformational technology in a generation,” there is a long road ahead as we navigate the impact of these advancements together. Events such as CDOIQ provide a valuable forum for fostering the discussions that matter, and I look forward to attending again next year.