In this episode of the Databricks Diaries, Daniel is joined by James Blagg, a seasoned Chief Data Officer with over 25 years of experience leading data strategy, architecture, and modernisation programmes across financial services.

James shares hard-won lessons from a career that spans the 2008 financial crisis, the big data hype cycle, the move from Teradata appliances to cloud-native platforms, and now the AI wave. It's a candid conversation about what actually drives value in data — and the gap between vendor promises and operational reality.

What's covered:

• James's career journey from Oracle PL/SQL developer to CDO
• Leading BI teams at Lloyds through the financial crisis and the HBOS integration
• Nationwide's data simplification programme and the "almost cloud" Teradata investment
• How platform evaluation has changed — and why cost, skills, and relationships now matter more than features
• Why "it's on the roadmap" is the most dangerous phrase in vendor selection
• The MVP hypothesis approach to picking analytics use cases
• Why so many data warehouse projects are perceived as failures (and why metadata investment is finally fixing it)
• Where AI really sits on the maturity curve — and why financial services is 10–15 years away from full back-office adoption
• A practical playbook for governance, cost monitoring, and security guardrails before scaling AI on Databricks
• The truth about cloud consumption pricing and reserved pricing deals

In this episode of The Databricks Diaries, Andy Davis is joined by Claire Thompson, Chief Data Officer at Quilter, to explore what AI readiness really looks like inside a modern financial services organisation.

Claire shares her perspective on the pace of change in data and AI, why strong foundations still matter, and why organisations cannot afford to wait for everything to be perfect before starting. The conversation covers how to build momentum through practical use cases, engage senior stakeholders, create safe environments for experimentation, and make governance a helpful enabler rather than a blocker.

Andy and Claire also discuss the importance of continuous learning within data teams, how to prioritise AI opportunities, and why technical teams need to get better at telling the story of the value they create.

This episode is for data, technology and business leaders who want a grounded view of AI adoption beyond the hype, with practical lessons on moving from early experimentation to scalable, valuable outcomes.

In this episode of the Databricks Diaries, host Andy is joined by Gordon Logan, Director of Data Services at PRS for Music, for a deep dive into the unique challenges of preparing a century-old organisation for the AI era. Managing data for over 180,000 members—including composers, songwriters, and publishers—Gordon shares how his team navigates astronomical data volumes driven by the modern shift to streaming and digital consumption. He discusses the critical transition from isolated experiments to a structured, momentum-driven AI strategy backed by executive leadership.

Gordon explores the essential components of building an AI-ready culture, prioritising data literacy and "AI efficacy" to ensure the workforce uses powerful tools like Copilot ethically and responsibly. The conversation highlights a disciplined, business-led approach to use cases, moving away from "technology vanity exercises" and focusing instead on measurable outcomes like accurate royalty distribution and improved member services. Listeners will learn how PRS for Music employs a cross-functional AI team to stress-test hypotheses, ensuring that every technological advancement facilitates tangible business value while protecting the sensitive data of the UK’s creative community.

In this episode of The Databricks Diaries, host Andy sits down with Steve Green, a senior data leader who has navigated the high-stakes intersection of data and AI from both the regulatory halls of the FCA and the front lines of private banking. Steve strips away the persistent AI hype to offer a masterclass on building a truly "AI-ready" business within the rigorous constraints of the financial sector.

Steve explains why the "garbage in, garbage out" reality remains the biggest hurdle to success and how a horizontal architectural view must prioritise business outcomes over technology for its own sake. The conversation reveals how to align legal, compliance, and IT through ethical control frameworks that maintain trust in generative AI outputs while ensuring foundational data hygiene remains a non-negotiable priority. By moving beyond the simple automation of annoying tasks, Steve challenges leaders to re-engineer end-to-end business processes to capture true value.

In this episode of The Databricks Diaries, I sit down with Tom Heath, a data and AI leader who has spent the last 20+ years working across data, semantic technologies, machine learning, and organisational transformation.

Tom shares a really thoughtful perspective on how the conversation has shifted — from having to convince people AI mattered, to helping organisations work out what to actually do with it.

We talk about what it takes to lead AI inside large, complex businesses, why so many projects still fail, and why technical capability on its own is never enough.

This is a grounded conversation on strategy, leadership, operating models, and the real work required to turn AI into something meaningful.

1. Why the public arrival of ChatGPT changed executive conversations overnight
2. The link between data structure, semantics, and the future of intelligent agents
3. Why AI success depends on more than just technical execution
4. The difference between efficiency, quality, and innovation as AI value drivers
5. Why some AI projects succeed technically but still fail commercially
6. The importance of problem definition before touching the technology
7. What leaders should focus on when building AI capability inside large organisations

In this episode of The Databricks Diaries, I sit down with Chris Hounslow, Head of Data Science & AI at Lebara, to talk about what it actually takes to turn AI from hype into real business impact.

Chris shares the journey behind Lebara’s transformation — from building solid data foundations to rolling out production-grade AI tools across a lean team… and winning industry recognition along the way.

We get into the realities of operating with limited resource, choosing the right use cases, and why most AI projects fail after the proof of concept stage.

If you’re trying to scale AI in a real business (not just experiment with it), this one’s worth your time.

1. Why the shift from “data science” to “AI” is often just rebranding, not reinvention
2. How to run a high-performing, lean AI team (5 people, 80% success rate)
3. Why being problem-led (not tech-led) is the key to choosing the right use cases
4. The importance of end-to-end ownership (not just building models)
5. Why most AI projects fail after the POC stage
6. How to build trust and adoption with non-technical stakeholders
7. The role of data foundations as the real competitive advantage
8. Why testing and validation are the biggest blockers to scaling AI

In this episode of the Databricks Diaries, Andy sits down with Mike Leverington, Director of Data Analytics at Skyscanner, to discuss the practical realities of AI readiness in a rapidly evolving tech landscape. Mike shares why successful AI integration begins with a cultural mindset rather than just technical infrastructure, urging leaders to "lean in" and embrace experimentation even amidst market volatility and uncertainty.

The conversation explores the threefold impact of Generative AI: driving internal productivity, revolutionising the customer experience, and disrupting entire industries. Mike provides actionable advice for data leaders on securing board buy-in, leveraging "small lab" innovation teams, and why the current hype cycle presents a unique opportunity to finally master self-serve analytics. From role-modelling AI use in daily admin tasks to "unlearning" how to Google, this episode is a masterclass in driving the last mile of the data value chain

AI isn’t failing because of the models. It’s failing because organisations can’t scale and adopt it.

Everyone’s talking about AI readiness. Very few are talking about what actually happens after you build something.

In this episode, I sit down with Magnus — a seasoned Data & AI leader who’s spent 20+ years helping global organisations (UEFA, Unilever, Worldpay, National Energy System Operator) move from experimentation to real impact.

We go beyond the usual “data, tools, roadmap” conversation and dig into what’s really holding organisations back.

This is a more honest look at where AI programmes succeed… and where they quietly stall.

If you’re investing in AI but not seeing meaningful ROI, this episode will likely hit home.