When I became Synchrony Financial’s first Chief Data Officer (CDO) in 2015, it was with a blank sheet of paper. And a mandate that was both liberating and daunting: Tell us what we need to do.

The company had recently separated from General Electric and completed its IPO. We were a newly independent, regulated public company still defining ourselves. There was no formal data office function. No team. No headcount. Just funding for a few contractors.

Now, after more than a decade in the role, I often get asked how I lasted so long when the average CDO tenure hovers around three years. The truth is, it wasn’t about clinging to the role. It was about building something sustainable and understanding what the role was meant to be.

Over time, we didn’t just put policies in place. We built a data management program that regulators respected, business leaders relied on, and that quietly reduced risk, freed up capital, improved operational efficiency, and positioned the company for what came next.

How to build a data management function from scratch

When I walked in, data engineering existed. Warehouses existed. But enterprise-wide data management? Policies? Operating model? None of that was defined.

We had to establish a data management program from scratch, enterprise policy, operating standards, a governance model, and a repeatable operating rhythm. And we had to do it in a company that was heavily regulated and building out risk data aggregation platforms for stress testing and capital planning.

One thing that worked in my favor: I sat in the first line of defense.

That matters more than people realize. In many organizations, the CDO sits in the second line, which can create an adversarial dynamic with the business. I was embedded with operators. That positioned me less as a compliance function and more as a partner.

But positioning alone doesn’t solve the problem.

Many CDOs walk into unrealistic expectations. There’s this belief that “data quality is bad” and the CDO will magically fix it.

In reality, most organizations don’t have systemic data quality failures. What they have are mismatches between data and use cases. People try to use data for purposes it wasn’t designed for. In many instances, the organization never collected the right data to begin with. Sometimes, the business process upstream is flawed.

If you accept the premise that “all data is broken,” you’ve already lost. My job was to shift the conversation from “fix all the data” to “improve the processes that produce and consume data.”

That mindset shift was foundational and, in my experience, one of the most important factors in extending CDO tenure beyond the early years.

Turning data governance into an enterprise operating model

Another reason a CDO tenure is often short is that organizations treat data management like a project. Projects end, while programs endure.

In an early board meeting, I was asked, “Justin, when will you be done?”

Before I could answer, our Chief Risk Officer said I had the unenviable task of running something that never ends. He was right.

Data management isn’t a one-time effort. It has to become a set of ongoing processes and capabilities embedded into the way the company operates every day. We anchored the entire framework in risk assessment. We broke down business processes, identified risks at each step, defined controls, and built capabilities aligned to those controls. 

Prioritization was always based on impact and likelihood, and where stronger data practices could deliver the greatest value to the organization.

When you approach it this way, you’re not selling “data projects.” You’re mitigating risk and enabling business outcomes.

Data becomes the byproduct of disciplined process design.

It also makes technology shifts easier. Whether it was cloud, big data, or AI, we didn’t reinvent the function each time. We designed it modularly. Plug and configure, not rip and replace.

Privacy is a great example. Years before regulations intensified, we had already built risk assessments around data sharing. When the laws evolved, we were positioned to respond. We simply extended what we were cataloging.

That’s what sustainability looks like.

Avoiding over-governance: Enable without blocking

Early on, I made a decision: I wasn’t going to chase perfection.

Perfection kills CDOs.

If you try to impose best practices everywhere, you create friction, bureaucracy, and resistance. Instead, we focused on minimum viable controls.

Always ask these questions:

• What is the least we must do to comply with the law?

• What is the minimum required to mitigate risk to an acceptable level?

• What is just enough to enable a business outcome?

Everything else could be optional, procedural or opt-in. We avoided heavy toll gates. Instead of stopping projects until everything was perfect, we adopted a “trust but verify” model. We monitored through metrics and reporting.

Nobody wanted to appear on the “naughty list,” so behavior improved organically.

Never waste a crisis

I’ve always thought of my role as a fireproofer, not a firefighter.

If you only show up when something explodes, you become the data police. But if you design processes that prevent explosions, you become invisible, which is both the beauty and the curse of the role.

When something broke – and something always breaks – we didn’t get defensive. We leaned in.

I remember a leader who initially resisted engaging with my team. When a long-standing data issue surfaced, we mobilized quickly, listened, quantified the problem, and solved it. That leader became one of our strongest partners.

That’s how you build credibility – you deliver. A fellow CDO once said, “Never waste a crisis.” A crisis makes the abstract real. It gives you a shared story to reference later – Do we want to relive that? Or do we want to invest in prevention?

Why a long CDO tenure depends on culture, not just strategy

Interestingly, my longevity wasn’t just about strategy. It had a lot to do with culture. I adapted to the culture at Synchrony and embraced its values. If you don’t read the culture correctly, you create friction and this often shortens tenures.

Credibility, over time, came down to consistency. If you say you’re going to do something, you have to follow through, deliver on it quickly, and do it without adding unnecessary cost or complexity to the organization. I also learned that soft skills matter far more than technical brilliance in a role like this:

• Empathy matters
• Humor helps
• Authenticity matters even more

And, critically: being able to explain complex data or risk issues in plain language without hiding behind jargon. 

At the same time, you have to be disciplined enough to illuminate risks and explain consequences without creating the kind of bureaucracy or gridlock that slows the company down.

Make the business your hero

Another big part of leadership in this role is being willing to take calculated risks. Sometimes that means stepping in to solve problems without formally asking for permission first, but it also means building consensus along the way and bringing people with you.

If there’s one thing that has helped me stay in the role, it’s this: I never tried to make data management the hero of the story. The business had to be the hero, and data simply had to enable it. 

As I think about what comes next, I don’t really see my time in the role as a victory lap. Instead, I see it as proof that when data is embedded into business processes, anchored in risk awareness, and led with empathy and pragmatism rather than perfection.

As such, the role itself becomes less fragile and far more foundational to how the organization operates.

Building trust across the leadership table

A vital lesson I learned over the years is that it’s impossible to succeed as a  CDO if you try to operate as an individual contributor.

Data sits at the intersection of business processes, technology, and risk, which means success depends heavily on strong partnerships across the leadership team, especially with the Chief Risk Officer, Chief Privacy Officer, and finance executives.

From the beginning, I approached the role with a simple mindset: my job was to help other leaders achieve their goals. I never positioned data management as a mandate that the organization had to comply with.

Instead, I focused on understanding what each leader was trying to accomplish and identifying how data could help remove obstacles or reduce risk in their processes.

That changed the conversation.

We weren’t talking about governance frameworks or policies. We were talking about business outcomes: where processes were breaking down, where data didn’t align with the use case, or where the organization simply didn’t have the data it needed.

At the same time, credibility requires being honest about risks. There were many situations where I had to highlight issues people didn’t necessarily want to hear, whether it involved regulatory obligations, data quality concerns, or unintended consequences of a decision.

The key was to present those risks without creating gridlock, while still enabling the business to move forward responsibly.

Execution is what ultimately makes that work.

Trust builds when leaders see that you can step in, solve problems quickly, and do it without adding unnecessary complexity or cost. Over time, that consistency changes how the role is perceived.

You stop being seen as a control function. You become a partner in how the business operates. And that, more than anything else, is what makes the role sustainable.

Measuring value the unsexy way

CDOs don’t usually generate revenue directly. Even though we enable it, you might have to articulate value differently.

One tangible example: capital reserves for data quality risks.

When I joined, we had to set aside capital buffers for potential losses caused by data issues. My goal was to reduce that buffer by improving data quality and risk controls. Every dollar freed up could be reinvested into the business. That created real value.

Another example was data minimization. By disposing of data we didn’t need, we reduced cyber exposure. Industry estimates place a monetary cost on every compromised record. The cost avoidance numbers were staggering.

Operational efficiency was another way to measure value. If analysts spend 40% of their time wrangling data, we don’t necessarily reduce headcount, we reduce the burden. But we increase productivity and avoid incremental hiring.

Most of the value is invisible. It’s risk reduction, cost avoidance, and efficiency gains. You just have to be able to tell that story.

Key takeaways

• Build a function, not a role: The CDO lasts when data management is embedded into how the business operates.
• Fix processes, not just data: Most issues come from misaligned processes and use cases, not “bad data.”
• Operate as a business partner: Align to business goals and outcomes, not policies and control for their own sake.
• Prioritize pragmatism over perfection: Minimum viable controls reduce friction and keep the business moving.
• Anchor in risk and value: Focus on impact, risk reduction, efficiency, and capital benefits.
• Earn trust through execution: Solve real problems quickly, especially in moments of failure.
• Make the business the hero: Data succeeds when it enables outcomes, not when it seeks visibility.

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.