Matt Crawley is Chief Data Officer at Lebara, leading the company’s data and AI agenda with a focus on building strong foundations and delivering tangible business value.
He began his career as a hands-on data practitioner and has spent more than a decade working across data and analytics roles, primarily in the telecoms sector. As his career progressed into leadership, Matt remained closely connected to the technical detail while deliberately embedding himself within the wider business. By partnering with marketing, commercial, finance, and operations teams, he developed a deep understanding of how organisations function in practice, shaping his belief that data teams only succeed when they are tightly aligned to real business constraints and priorities.
At Lebara, Matt established the Data and AI function from the ground up, building an award-winning team spanning analytics, data science, data engineering, and data governance. His work has focused on creating robust data foundations through clear strategy, effective tooling, and the development of strong in-house expertise. He treats data as a critical business asset—one that must be actively prioritised, protected and leveraged responsibly.
A defining moment in Matt’s career was moving from large FTSE organisations into a much smaller company. The shift exposed him to different operating models, risk profiles and decision-making dynamics, sharpening his judgement about what scales well and what does not in data leadership. That experience continues to inform his approach today, balancing ambition with pragmatism.
Matt advocates for technical leadership within data and AI, valuing hands-on understanding alongside people leadership, and remains focused on enabling efficient, value-driven use of data across the business.
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?
“The most important trait for me remains a strong commercial understanding. Any data strategy must directly enable the business strategy, which requires deep knowledge of organisational goals and constraints.
“Second, self-awareness and trust in expertise. Now more so than ever, no leader can be an expert in every area of a rapidly evolving field. Recognising blind spots and empowering specialists to shape direction is increasingly essential.
“Third, adaptability. Long-term, rigid data programs are being replaced by shorter cycles of delivery, reflection, and course correction. Leaders must be comfortable pivoting quickly and learning from what doesn’t work.”
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?
“While technical leadership is now a viable route to senior roles, success at the C-suite level depends on influence and collaboration with executive peers. Broadening your business understanding is one of the most effective ways to increase your impact.
“Data often operates in an idealised environment; understanding real-world constraints – systems, budgets, commercial models, and operating realities, allows you to design solutions that truly scale. This exposure can come through secondments, mentorship, or simply volunteering for projects outside your comfort zone. The most effective leaders are business leaders first, and data leaders second.”