For years, companies have poured millions into data platforms, AI talent, and new technologies — yet many executives still find themselves asking the same question: Why aren’t we seeing real business value from all this data?

That’s the problem Julia Bardmesser set out to address in her new book, “From Data to Dollars: Turning Data Strategy into Business Value.” With more than 25 years of experience, Bardmesser has led major transformations at Voya Financial, Deutsche Bank, Citi, FINRA, and Freddie Mac. Today, she serves as CEO of Data4Real, teaches as an Adjunct Professor at NYU Stern, and advises multiple startups as a board member.

Throughout her career, she noticed a recurring pattern: companies struggle to turn data investments into measurable outcomes — not because of weak technology, but because of a disconnect between data teams and business goals.

In this Q&A, Bardmesser explains why so many initiatives fall short, what leaders must do differently, and how data strategy becomes powerful only when it is embedded within business strategy.

Edited Excerpts

Q: You outline why so many data and AI initiatives fail. What’s the single most common pitfall leaders should avoid?

The most common pitfall leaders should avoid is treating data and AI initiatives as purely technical projects. Time and again, I’ve seen organizations invest in cutting-edge tools, hire brilliant data teams, and stand up the latest platforms — only to find that business value remains elusive. Why? Because the hard part isn’t the technology. It’s the people.

The real challenge lies in culture, communication, and alignment. When leaders don’t focus on building a shared understanding between data teams and business teams, when they skip the work of change management and fail to clearly link data to real business goals, the efforts tend to stall. Without that connection, data initiatives can feel like science experiments rather than value drivers.

Data-driven transformation is fundamentally business transformation. It requires executive sponsorship, a shift in how decisions are made, and a deep commitment to making data usable and actionable. If you start by aligning priorities, investing in data literacy, and fostering a culture of trust and collaboration, you’ll have a much better shot at success.

Q: One of your core messages is that growth and profitability come when data and AI are tied directly to business outcomes. Can you share a practical way executives can make that link?

Absolutely. One practical way executives can make the link between data, AI, and business outcomes is by starting with the business goals, not the data. Start by asking each business leader, “What are your goals this year?” Then work backward to identify the data and capabilities needed to get there. For example, if a goal is to increase customer retention, tie that directly to metrics like churn prediction or customer lifetime value, and align data efforts to improve those outcomes.

Q: You highlight case studies across industries. Which example stands out to you as the best proof that data and AI can deliver real competitive advantage?

The biotech case study stands out because it shows what’s possible when data strategy is treated as a core part of business strategy. This was a growth-stage startup, operating in a high-stakes, fast-moving environment. 

They started with outcomes: What do we need to deliver as a business? Faster R&D cycles, stronger regulatory reporting, better visibility into pipeline performance. From there, they mapped the data capabilities needed to support those goals, and only then did they think about tools. That order — outcomes first, capabilities second, tooling third — kept the focus on business value and helped them avoid common traps like overengineering or chasing shiny new tech.

Even though this example comes from biotech, the approach applies across industries. When companies use data strategy as a lever for differentiation, that’s when they start to pull ahead. 

Q: Many organizations still struggle to bridge the business-technology divide. What’s the first step in getting executives and data leaders truly aligned?

The first step is having an honest, business-first conversation, without jumping straight into platforms, tools, or technical jargon. Executives and data leaders need to sit down and have a conversation about: What are we trying to achieve, and how will we know if we’ve succeeded? That means framing the conversation around business goals, not dashboards or models.

Too often, alignment fails because the two sides are speaking entirely different languages. Data teams talk in terms of systems and pipelines; business leaders think in terms of growth, cost, and risk. The bridge between them is outcomes. When both sides can see how data capabilities support real, measurable goals — like increasing customer retention or accelerating product development — that’s when alignment starts to happen.

It sounds basic, but this shift in how the conversation starts makes all the difference. Begin with shared priorities, stay focused on outcomes, and make the technical strategy follow—not lead.

Q: Generative AI is reshaping strategies everywhere. In your view, how should leaders treat it — more as an enabler or as a core part of business strategy?

AI in general and GenAI in particular are data capabilities. The mistake I see many organizations make is jumping straight to “we need a GenAI strategy,” without asking what business problem they’re trying to solve. That’s backwards.

Start with the business goals. If GenAI can help you achieve those goals faster, smarter, or more efficiently — great. Then it becomes part of the broader data capability strategy that supports your business strategy. But it’s still a capability, not the strategy itself.

The companies getting it right are the ones treating GenAI as one of several tools in the toolbox. It can absolutely drive differentiation and new ways of working, but only when there is a clear understanding of what the business is trying to accomplish.

Q: You stress the importance of a “data-first culture.” What does that look like day-to-day in a successful organization?

In a successful data-first culture, data is something you start with, as opposed to something you take care of at the end. It’s built into the foundation of how the business operates, not layered on top as a reporting function. That means when teams are setting priorities, launching new products, or designing processes, they’re already thinking about what data will be needed, how it will be used, and how it will flow through the organization. Usability and trust are built as requirements from the start.

You also see clarity around ownership. People know where to go for the data they need, and they trust it because quality is being managed, definitions are consistent, and context is available. Data isn’t trapped in silos or only accessible to specialists, it becomes part of the shared language across teams.

Leaders play a big role too.They ask questions like “What does the data tell us?” and expect thoughtful, evidence-based answers. That kind of leadership sets the tone and reinforces the idea that paying attention to data is essential.

Q: For a newly appointed CDO or CIO, what’s the most important move they can make in their first 90 days to set the stage for impact?

The most important move a new CDO or CIO can make in their first 90 days is to dig into the business priorities — and then start building relationships around those priorities. Not just with the CEO or their direct peers, but across the organization. Sit down with business leaders, listen more than you talk, and ask one key question: “What are you being held accountable for this year?”

This early alignment is critical. It shows you’re not there to push a tech agenda or build an empire, you’re there to help the business win. Once you understand what matters most to each area, you can begin to map where data and technology can create leverage. That’s your foundation.

At the same time, start assessing where things really stand: architecture, skills, governance, technology and culture. But don’t jump straight into fixing everything. The goal in the first 90 days is to build credibility and learn. Build trust, find a few quick wins tied to business value, and start painting the bigger picture of what’s possible. That’s how you set the stage for long-term impact.

Q: Writing a book is a big undertaking. What was the most surprising — or personal — lesson you learned during the process of putting From Data to Dollars together?

It turned out to be much harder than I expected. I was already teaching the content of this book at the NYU Stern, speaking about it at conferences, and using it as the foundation for workshops with clients, so I thought I had a strong head start. I had well over 100 pages of slides, detailed frameworks, real-world case studies, and a clear storyline I’d refined over the years. But translating that into a book was a completely different challenge.

PowerPoint lets you live in shorthand — visuals, bullet points, a few words on a slide that you explain in conversation. But writing a book requires you to slow down and spell things out in a way that stands on its own. Every concept had to be unpacked, clarified, and made relevant for someone who might be reading it without ever having heard me speak. That forced me to really dig into why certain things mattered, how to communicate them without the aid of a whiteboard or Q&A, and how to calibrate my tone and writing style.

There were definitely moments where I thought, “This was supposed to be the easy part,” but looking back, that process of rethinking and reshaping the material made it stronger. It pushed me to get clearer about the message and to make sure the book would be useful to someone navigating these challenges in the real world, be it as a data professional or as a business leader.