Perry Philipp is Chief Data Officer at Entain, where he leads the organisation’s data and AI agenda with a focus on delivery, adoption and sustainable business impact.
He began his career more than 20 years ago working on mega-scale data centre programmes, an experience that instilled a strong belief in the importance of dependable infrastructure and disciplined execution. He later moved into commercial roles, blending analytical rigour with hands-on delivery to achieve outcomes rather than plans. His career has since spanned management consultancy and senior operational leadership roles across the Middle East, the United States, and Europe, exposing him to a wide range of industries, cultures and operating models.
Perry has held executive positions in FTSE 100 organisations as well as COO roles in venture-backed start-ups, including leading turnarounds and restructures in multi-billion-dollar businesses. These experiences have shaped a leadership style grounded in clarity, pace and trust, particularly in high-stakes environments where technical excellence alone is insufficient.
As a data leader, Perry views his role as fundamentally about people. He believes technology is an enabler, but lasting value comes from capability building, clear accountability and operating models that make responsible data use the default. At Entain, he places strong emphasis on mentoring, upskilling and building communities of practice that make data and AI practical, safe and widely adopted across the organisation.
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?
“For effective data and AI leadership, the most important traits sit at the intersection of clarity, trust, and delivery. First is the ability to define the problem properly: what decision are we improving, what does ‘better’ mean, and how will we measure success? Without that, teams build impressive things that don’t change outcomes.
“Second is building trust at scale. That means clear ownership, strong data governance that enables rather than blocks, and mechanisms that make reliability repeatable: quality checks, lineage, access controls and transparent definitions. Trust is what turns data from ‘interesting’ into ‘actionable’.
“Third is balancing pace with precision. Leaders need to create an environment where teams deliver quickly, learn, and iterate without cutting corners that later erode confidence. Resilience matters too: transformation is a long game and requires steady leadership through setbacks.
“In my organisation, the most influential capabilities have been trust-building and capability-building. When stakeholders trust the numbers and understand them, adoption follows. And when you invest in people through mentoring, communities, and learning pathways you create a network of champions who extend impact far beyond the central function.”
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?
“My non-traditional advice is to develop a healthy selfishness with your time. Data and AI leaders are magnets for ‘just one more thing’: urgent requests, shiny ideas, meetings that feel important, and problems that sit at the edge of everyone’s accountability. If you say yes to everything, you become busy, not impactful.
“Be generous with your expertise and support your colleagues but protect time for the work only you can do: setting direction, building alliances, removing blockers, and shaping the operating model that makes outcomes repeatable. Learn to say ‘not now’, and to insist on clarity before committing effort. It’s not about being difficult—it’s about being intentional.
“The C-suite is a role of focus and trade-offs. The earlier you learn to manage your time with discipline, the more value you’ll create, and the more sustainable your leadership will be.”