Few executive roles have evolved as rapidly, or remain as difficult to sustain, as the Chief Data Officer (CDO). While organizations continue investing heavily in data and AI, many CDOs struggle to stay in the role long enough to build lasting capability.

A key reason is that the role has never been easy to define. Ask 10 CDOs what they own, and you may get 10 different answers.

While some lead data governance, others oversee analytics, data quality, privacy, records management, and even AI. Similarly, some sit in the first line of defense and own risk, while others operate in the second line and focus on oversight and compliance.

That ambiguity makes it harder for CDOs to stay in the role long enough to create lasting impact.

After more than a decade as a CDO in one organization, this lesson stands out above all others: There is no universal blueprint.

For aspiring and current CDOs, the challenge is not learning a fixed playbook, but it is learning how to adapt their playbook to the organization, the culture, and the moment. Successful organizations define the role based on the problems they are trying to solve.

Start by understanding the territory, not the title

One of the biggest mistakes new CDOs make is assuming the title itself defines the job. An early challenge is understanding how data responsibilities are distributed across the organization.

How do you determine where accountability truly sits, and how do you navigate the distinctions between being responsible, accountable, consulted, or simply informed?

A useful way to think about the role is through a responsibility framework. Some activities will be directly owned by the CDO. Others will be shared. Some will belong entirely to another executive, with the CDO serving as a subject matter expert.

The most successful CDOs spend less time worrying about organizational charts and more time understanding business strategy, desired outcomes, and problems that prevent these two from being realized. They understand which data-related decisions they own, which they influence, and which require partnership.

This clarity becomes the foundation for everything else, including operating models, governance structures, and executive relationships. The role is not static either. As organizations mature, new responsibilities emerge.

Effective CDOs continually adapt their charters, expanding their influence by solving problems and becoming trusted partners.

There is no perfect career path to the CDO role

When discussing the different types of CDOs, people often ask what background creates the best CDO. The reality is that there is no single answer. Some of the most effective CDOs are not necessarily those with the deepest engineering expertise. Instead, they are the leaders who can connect people, processes, and technology. 

While some successful CDOs come with technology backgrounds, others emerge from analytics, operations, finance, risk management, or even business leadership.

Interestingly, the common denominator here is not where they started, but the experiences they accumulated along the way.

The strongest data leaders understand how business processes create data and how those same processes consume it.

They understand the friction points that prevent data from being used effectively. They know enough about technology to appreciate the solution space, but they remain grounded in business outcomes: This distinction is key.

Many people assume data problems are technology problems. In reality, most data problems are business process problems. Technology can help, but technology alone rarely changes behavior. I believe data management is fundamentally a people discipline.

Effective CDOs  bring stakeholders together, create alignment, and translate competing priorities into a common vision.

In fact, backgrounds rooted in social sciences, economics, political science, psychology, and other cross-disciplinary fields often cultivate skills that are surprisingly valuable in the role. 

These disciplines teach leaders how social systems work, how people behave, and how to solve complex problems that do not fit neatly into a single domain.

Technical expertise matters to a certain extent; but soft skills like empathy, influence and problem solving often matter more.

The best CDOs operate between business and technology

There is an ongoing debate about whether the CDO role is becoming more technical or more business-oriented. Well, the answer is both. The role exists at the intersection of people, process, and technology.

CDOs don’t need to be the deepest technical experts in the room, nor do they need to run a business unit. They need enough fluency in both worlds to serve as a translator. That said, the balancing act is not easy.

Business leaders often speak in terms of growth, customers, efficiency, and outcomes. Technology leaders speak in terms of architecture, platforms, scalability, and implementation.

The CDO must be able to navigate both conversations. The real value comes from translating business needs into capabilities and translating technical possibilities into business value. That requires understanding risk, assessing trade-offs, and helping organizations make better decisions.

This same mindset explains why the concept of data products has gained traction. For decades, analysts have been trained to create datasets, perform transformations, and solve problems independently.

Moving toward reusable data products requires organizations to break those habits and embrace shared assets.

That is fundamentally a change management challenge and, like most challenges facing the CDO, it is far more about people than technology.

Generative AI changes the conversation, not the mission

Few developments have generated more discussion in executive circles than generative AI (GenAI). The biggest question is often simple: Who owns it?

Some organizations view AI as a technology capability and place ownership under the CIO or CTO. Others see it as an extension of analytics and data science. Still others believe it belongs within the CDO function.

Even though the debate is understandable, the primary question enterprises should ask is whether GenAI fundamentally changes the mission of the CDO. My view is that it both validates and amplifies the need for it

The core responsibilities remain the same. Organizations still need to know what data exists, where it resides, how it is being used, whether it is trustworthy, and who is accountable for it.

GenAI surfaces these challenges to the collective consciousness of the organization. It does not replace them.

The risks may become larger. The impact of poor data may become more visible. New considerations around relevance and unstructured information emerge, but underlying disciplines remain familiar.

What AI does accomplish is strengthening the business case for mature data management. Organizations eager to deploy AI quickly discover that success depends on data quality, governance, lineage, metadata, and trust. 

Suddenly, capabilities that once seemed optional become strategic necessities.

In many organizations, AI is serving as a catalyst for investments that data leaders have been advocating for years. That does not necessarily mean the CDO should own all AI initiatives.

AI creates value through business processes. Process owners remain accountable for outcomes. Technology leaders enable capabilities. Data leaders reduce friction, improve trust, and help manage risk.

The future will likely involve shared accountability rather than a single owner.

The invisible work that defines great CDOs

One of the most overlooked aspects of the role is how much of the work remains invisible. Great CDOs constantly translate technical achievements into business outcomes. 

They explain how a governance initiative reduced losses. They show how trusted data accelerated decision-making. They demonstrate how partnerships enabled growth, improved customer experiences, or reduced operational friction.

The conversation is never about fixing database columns or improving metadata quality. It is about outcomes:

• What changed?
• Who benefited?
• What risks were reduced?
• What value was created?

The ability to answer those questions separates successful CDOs from those who struggle to gain traction.

Looking forward, I believe the highest-impact CDOs will not be defined by technical mastery alone. They will be defined by their ability to drive measurable value, build relationships, influence change, and connect organizational priorities to data-driven outcomes.

The role will continue to evolve. While AI will accelerate that evolution, newer responsibilities will emerge. But the fundamentals are unlikely to change. The best CDOs will remain translators, connectors, problem solvers, and storytellers. 

Most importantly, they will remember that the job was never really about data. It was always about helping the organization succeed.

Key takeaways

• Define the CDO role around business needs, not job titles: Tailor the CDO mandate to the organization’s specific challenges, maturity level, and strategic objectives rather than adopting a generic operating model.
• Understand accountability before driving change: Clarify who owns, influences, and supports data-related decisions across the enterprise before implementing governance, operating models, or transformation initiatives.
• Build a career through cross-functional experience: Develop expertise across business processes, operations, risk, analytics, and technology rather than relying on a purely technical or data-focused background.
• Prioritize people and process over technology alone: Recognize that most data challenges stem from organizational behaviors, incentives, and business processes rather than technology limitations.
• Act as a translator between business and technology: Connect business outcomes with technical capabilities by helping stakeholders understand trade-offs, risks, opportunities, and value creation.
• Treat data management as a change management discipline: Drive adoption of shared assets, data products, and governance practices by influencing behaviors and aligning stakeholders around common goals.
• Use AI as a catalyst for stronger data foundations: Leverage growing AI adoption to strengthen investments in data quality, governance, metadata, lineage, accountability, and trust.
• Measure success through business outcomes: Demonstrate value by showing how data initiatives reduce risk, improve decision-making, accelerate growth, enhance customer experiences, and create measurable organizational impact.

About this series

This article is part of a CDO Magazine series co-created with seasoned data leader Justin Heller, exploring how to make the Chief Data Officer role durable, effective, and embedded within the enterprise. The series covers:

• Succeeding as a Long-Term Chief Data Officer: Turning a temporary mandate into an institutional function, building trust across the leadership table, and evolving from transformation leader to operating executive.

• Creating Data Management Processes at Enterprise Scale: Moving from ad hoc fixes to standardized, scalable operating models embedded into business workflows.

• What to Base a Data Governance Program On: Shifting from tool-driven governance to principle-based frameworks grounded in risk, accountability, and decision rights.

• How to Make the Organization Actually Use Your Data Program: Overcoming resistance, aligning incentives, and ensuring governance is both effective and unobtrusive.

• Leading Through Technology Waves: Navigating shifts from big data to cloud to AI while maintaining a consistent operating foundation.

• Becoming and Growing as a Long-Term CDO in an AI-Driven World: Building the skills, mindset, and adaptability required to succeed and stay relevant as the role evolves.

About Justin Heller: 

Justin Heller is a seasoned financial services executive and former, longest tenured Chief Data Officer with more than 30 years of experience helping organizations succeed through data strategy, governance, and risk management. He is widely recognized for guiding institutions in strengthening data governance, advancing AI adoption, and enhancing regulatory, privacy, and risk frameworks, including work with G-SIB, D-SIB, and other systemically important financial institutions.

A respected voice in the industry, Heller has spoken at leading conferences and forums such as FIMA USA, CDAO Financial Services, and CDO Magazine, as well as numerous webinars, addressing topics including data governance, artificial intelligence, risk management, privacy, and data minimization. He also holds multiple patents related to data management and innovation in enterprise data practices.

His areas of expertise include financial services, AI and data strategy, data governance, regulatory compliance, risk management, and privacy.