Ryan Moore, Director of Data, The Guardian

Ryan Moore is Director of Data at The Guardian, where he leads the group data function. With more than 20 years’ experience in data leadership across industry and consulting, Ryan focuses on building data capabilities that deliver trusted, high-quality data products that deliver value while remaining firmly rooted in The Guardian’s values of trust, integrity, fairness and service to readers. 

Since joining The Guardian in November 2024, Ryan’s remit has expanded to include enterprise AI. He partners with the colleagues across innovation, policy, training and the broader group technology function in guiding the organisation’s ethical adoption of Enterprise AI tools and capabilities in line with The Guardian’s principles and approach to AI. 

This value without compromising on values approach – brought to life by the Data to Value (D2V) framework is already delivering results across their reader revenue, advertising and editorial teams through greater visibility, trusted data products and faster time-to-insight.  Prior to The Guardian, Ryan’s technical data leadership was further established during his time as the Group Head of data analytics platforms and engineering at Pets at Home. 

There, he realigned the data strategy to focus on strategic outcomes through a data mesh-inspired architecture and operating model. This technical foundation across customer, pet, and product domains was instrumental in underpinning the Petcare platform and driving revenue growth within their Vets business.  

Before moving fully in-house, Ryan spent several years in senior leadership roles within data and AI consultancy. During this period, he built and scaled multidisciplinary data practices and led large-scale transformation programmes across utilities, financial services and the public sector, including nationally significant data platforms. This work continues to inform his pragmatic approach to governance, platform design and value realisation. 

Earlier in his career, Ryan worked across Southeast Asia, Japan and UK delivering complex data transformation projects and programmes for organisations including Public Bank, GE Japan, Barclays, and Anglian Water. Operating largely in highly regulated environments shaped his practical, value-led approach to data delivery. 

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? 

Ryan frames effective data and AI leadership as fundamentally relational, rooted in trust, context and comfort with uncertainty. Trust, he argues, is foundational, particularly because data teams are often asked to “back up” decisions in some instances that have already been made. When the data points in a different direction, leaders need the credibility to say, “these are the facts”, even when that is uncomfortable. 

For Ryan, trust is built through consistent delivery and when things don’t go to plan, being able to put your hands up and say “we got it wrong, let’s learn from that and go again”. That honesty enables genuine conversations rather than performative analytics. 

Alongside trust sits deep domain knowledge. While tools and techniques can be learned, understanding the organisation requires time and proximity: “spending time truly listening, asking the brave questions, challenging the responses”. That depth allows data leaders to interpret signals properly and push back when necessary, rather than acting purely as a service function. 

The third capability Ryan emphasises is comfort with uncertainty. Even with clear strategy and goals, “every quarter, how you can achieve the goals can change very quickly”. Data leaders therefore need to adapt without losing credibility or momentum. 

For Ryan, trustworthiness, domain fluency and resilience in the face of uncertainty are at par with technical skillsets. Tools evolve, strategies shift and plans change, but leaders who can speak honestly about what the data does and does not say remain effective. 

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? 

Ryan’s advice cuts against two common instincts: chasing visibility for its own sake and forming quick judgements about people. 

On the first, he notes how often data leaders are pulled into requests for “a report or a dashboard”. Visibility, he argues, is frequently mistaken for progress. The discipline is to keep pushing conversations back to value and outcome: what decisions will this change, what behaviour will shift, and why insight matters. That can be uncomfortable, but it prevents data teams becoming factories for activity rather than impact. 

His second piece of advice is to “take a long-term view on people”. In high-pressure environments, relationships can become transactional. Ryan cautions against letting isolated interactions define how you see someone. Over time, he has found that holding judgement lightly leads to better collaboration and stronger teams. 

Together, these ideas reflect a broader mindset: resist optimising for immediacy, whether in metrics or relationships. Anchor data work to value and give people time to show who they are.