David Castro-Gavino is Executive Director, Axial – Head of Data Deployment at AstraZeneca, bringing close to three decades of experience across technology, data, and analytics. His career spans consulting and executive roles, multiple continents, and industries ranging from finance and retail to life sciences, but his perspective has been shaped by addressing structural issues surfacing across a range of organisations.
David’s relationship with data began at CACI, working on the ACORN classification. The lasting impact wasn’t because of the technology, but because of the clarity that well-structured data creates, revealing customer behaviour, sharpening decisions, and changing how organisations operate. That early lesson carried into hands-on consulting roles, where exposure to data modelling, quality, and systems thinking reinforced the notion that weak foundations are what ultimately limit the value of analytics and AI.
A nine-year period at dunnhumby proved pivotal. Working at true global scale with retailers and FMCGs, David saw how commercial outcomes depend on trust, governance, and ownership – areas that are often seen as the unglamorous mechanics, but insight collapses without them. His view is pragmatic in that if everyone owns the data, no one really does.
After an instructive spell at Booking.com, experiencing high-velocity experimentation in a decentralised environment, David joined HelloFresh as Global Vice President of Data. Reporting to the CEO, he led a transformation that rebuilt the company’s data foundations and operating model to support rapid growth.
Now at AstraZeneca, David has been brought in deliberately from outside the sector to make data tangible inside a large-scale transformation, challenging assumptions, breaking complexity into something usable, and ensuring understanding comes before ambition.
As a data and AI leader, which traits and skills do you think matter most, and which of those have been most influential for you in your current position?
“Resilience is critical because the data leader role can be a lonely one,” said David. Despite the visibility of data and AI, David feels that data and AI leaders are still often not understood in what they are trying to do. He explained that people think they understand data, “but it takes a long time to truly grasp the complexity, the interdependencies, and why things take time.” Without resilience, the work that happens under the surface never really lands, and “the data leader won’t be successful.”
For David, leadership in data isn’t about being the smartest person in the room. “That’s not what data leadership is about. What matters is the ability to diagnose and remove friction,” helping peers see where value can actually be realised, and bringing them along the journey. That’s hard, particularly because, as David said, “a lot of data leaders tend to be very introverted.” It doesn’t always come naturally to sell your capability or articulate why the work matters.
The second critical trait for David is strategic clarity: “We’re technical, we’re geeky, and we complicate things very easily. When that happens, we lose the audience very quickly,” he said. David has learned to invest time in a more human-centred approach: simplifying complexity, framing the work in a way people can engage with, and guiding them through the journey rather than overwhelming them.
For David, it comes down to three things: resilience, friction removal, and clarity. “Those are the skill sets that make a data leader influential.”
Reflecting on your career, what is one non-traditional piece of advice (outside of technical skills) you would give to an aspiring data or AI leader aiming for the C-suite?
“I didn’t want to just repeat myself and say resilience again,” David said, “but the biggest challenge I’ve always had is living in the middle.” Data sits between the business and the deeply technical functions, and “the friction between those worlds is real.” Learning to operate in that space (and being comfortable there) is something David wishes he had understood much earlier.
The non-traditional advice David gave is to lean far more into being a connector than a specialist: “That ability to connect every single function is critical.” In hindsight, David says he would have invested earlier in consultative skills, not data literacy as a concept, but the ability to translate, frame, and persuade.
“Storytelling through data really matters. Become a storyteller.” When David is explaining something now, he will deliberately use analogy. “It helps break things down and simplify the complexity, and it allows people to engage without feeling overwhelmed or excluded.” Too often, data and AI leaders assume the work will speak for itself, and it rarely does.
What David has learned is that influence doesn’t come from precision alone. It comes from making complexity navigable and relatable. “You have to bring data to the language people already speak,” he said. That, combined with resilience, is what allows you to survive and eventually lead in the space where everything intersects.