Driven by Data: The Podcast

Welcome to another episode of the Data Debrief, the companion show to Driven by Data: The Podcast, where hosts Catherine Dowden-King and Kyle Winterbottom unpack Tuesday’s episode, share what’s been on their minds, and explore the realities of leadership, culture, and capability across the data and AI landscape.

This week, Catherine and Kyle reflect on the conversation with Dru Patel from the FA, diving deeper into the human side of data leadership, from storytelling and self-awareness to the commercial realities of what it actually takes to succeed at the executive level.

They cover:

• Why Dru Patel’s approach to storytelling and communication stood out as one of the most compelling conversations the podcast has hosted to date
• How technical capability alone has become “table stakes” in data leadership, and why the differentiator is now influence, communication, and the ability to shape perception
• Why “soft skills” might be the most damaging phrase in the industry, and how cultural buy-in and human-centred leadership are often the real drivers of ROI
• The uncomfortable reality that working hard and being technically brilliant doesn’t automatically lead to progression, and why self-awareness is becoming a critical leadership trait
• How data leaders can shift conversations away from platforms, dashboards, and governance, and toward decisions, business outcomes, and commercial impact
• Why organisations still struggle with the perception of data teams as back-office technical functions, and how that perception shapes hiring, mandates, and ultimately failure
• The difference between data literacy and data culture, and why culture is what happens when nobody is watching
• How lived experience, industry context, and organisational history shape expectations around data quality, trust, and value creation
• Why many CDO mandates continue to fail, not because the individuals lack capability, but because organisations hire for technical delivery while expecting commercial transformation
• The growing disconnect between what data leaders are hired to do and what boards actually expect them to achieve
• They also dig into the future of data leadership and organisational accountability:
• Why businesses are now entering their third, fourth, and even fifth iteration of the CDO role, and what those repeated resets reveal about the maturity of the market
• How hiring behaviour has unintentionally incentivised technical specialisation over commercial leadership for more than a decade
• Why asking questions around decisions, KPIs, revenue targets, and business performance during interviews can quickly reveal an organisation’s true perception of data leadership
• Kyle’s thought of the week: why the debate around failed CDO mandates is becoming too polarised between “it’s the organisation’s fault” and “it’s the individual’s fault,” and why the reality sits somewhere in the middle
• Catherine’s thought of the week: what happened after asking LinkedIn for web developer recommendations, and what the overwhelming response revealed about vendor outreach, personalisation, and the growing problem of AI-generated sales noise

They also discuss:

• Why AI has enabled many organisations to operate “badly at scale, but faster”
• How senior leaders increasingly avoid broad vendor engagement unless there is an immediate need
• The importance of building trusted communities where candid conversations can happen openly and safely
• Why Orbition Group’s private membership community continues to grow as leaders look for more meaningful peer-to-peer discussion away from the public spotlight

This episode is a candid exploration of the skills gap that rarely gets discussed in data and AI leadership, not the technical gap, but the commercial, cultural, and human capability gap that increasingly determines who succeeds, who gets overlooked, and why so many organisations still struggle to realise value from data.

In Episode 6, of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was joined by Dru Patel, Data Lead at The Football Association, where they discuss how embracing failure fuels a human-centred approach to data leadership that unlocks adoption, trust, and real organisational change, which includes;

• How an unconventional background spanning Kenya, the Cabinet Office, and a life coaching qualification shaped a distinctly human approach to data leadership.
• Why hard work alone has a ceiling, and how the first ten years of Dru's career proved that technical output without soft skills will only take you so far.
• Why starting with the "why" is the most powerful tool a data leader has for driving engagement, adoption, and business buy-in.
• How asking "why" five times gets you to the real root of what a stakeholder actually needs, and why most data teams stop at the first answer.
• How the gap between data teams and the business is normal, and why failing to challenge it with the right questions is the real problem.
• Why data teams need to push back on the brief rather than just building what's requested.
• How a dashboard that stops being used isn't always a failure, and why it often signals that the business is ready to ask bigger questions of the data.
• Why data literacy and data culture are not the same thing, and what it actually takes to move from one to the other.
• How the six blind men and the elephant illustrates what happens when everyone is right in their own context and nobody is looking at the whole picture.
• Why treating data like the organisation's own money, rather than a technical function, is the mindset shift that drives real literacy.
• Why data leaders take failure far harder than anyone else in the room, and what a 1980 psychology study reveals about the stories we tell themselves.
• How building a PPE supplier system in six days during COVID taught Dru that perfection is the enemy of progress.
• Why owning failure openly builds more trust than silence, and how to reframe the conversation from blame to improvement.
• How imposter syndrome shows up in data leadership, why it never fully goes away, and what mentors and trusted voices can do to help reframe it.
• Why nerves and excitement are the same feeling, and how the most effective leaders choose which one to act on.
• Why listening, really listening, before jumping to solutions is the soft skill most data professionals underestimate.
• How a human-centred lens, not a technical one, is what ultimately bridges the gap between data teams and the decisions that matter.

Welcome to another episode of the Data Debrief, the companion show to Driven by Data: The Podcast, where hosts Catherine Dowden-King and Kyle Winterbottom unpack Tuesday's episode, share what's been on their minds, and explore what's really happening across the data and AI landscape.

This week, Catherine and Kyle reflect on their conversation with Richard Masters, VP of Data & Analytics at Virgin Atlantic, diving deeper into the themes that matter most right now, from decision-led data strategies to the realities of building for reusability in an AI-driven world.

They cover:

• Why Virgin Atlantic's surprisingly lean fleet of 44 planes is a masterclass in doing more with less, and what data teams can learn from it
• Richard's astrophysics background and how the principle of signal over noise shapes his entire approach to data
• Why the North Star of any data function should be decision support, and how working backwards from decisions changes everything
• The shift from "collect all the data" to "what decisions are we trying to impact" — and why that transition is still hard for most organisations
• The move from single-use data projects to reusable, scalable products, and why building for one use case is the old way of thinking
• How AI is democratising business capability, the rise of the "builders vs coders" mindset, and what that means for how data teams are structured
• Why fewer platforms, used well, will beat a sprawling vendor stack, and what that means for the vendor community going forward

They also dig into the future of talent and skills in data:

• Why critical thinking, curiosity, and imagination are becoming more valuable than technical qualifications
• How the widening talent pool challenges universities and educators to stop being anti-AI and start teaching people how to use it responsibly
• Why neurodiversity and unconventional backgrounds will be a competitive advantage in an AI-augmented world
• Catherine's thought of the week: why data professionals have a duty to educate those around them as AI misinformation spreads, from the boardroom to the toddler group
• Kyle's thought of the week: why the CDO role may be heading toward a fractional, advisory model, and what that split between strategy and execution means for the future of data leadership hiring

This episode is a candid look at where data is heading, where the real value is created, and why the leaders who thrive will be the ones who connect commercial strategy to the decisions that actually move the needle.

In Episode 5, of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was joined by Richard Masters, Vice President of Data & AI at Virgin Atlantic where they discuss how a "signal over noise” mindset helps cut through complexity, and enables better decision-making and real business impact, which includes;

• How an astrophysics background shaped a “signal over noise” mindset for data and decision-making.
• How reducing data noise and surfacing the right signal drives meaningful business action.
• Why decision support should be the North Star for every data team.
• How aligning data work to business strategy requires constant iteration, challenge, and course correction.
• Why iterative improvement wins the day.
• How data context and metadata are critical to trust, usability, and adoption.
• Why data lineage, governance and trust are foundational to scaling AI successfully.
• How prioritisation should be driven by impact vs feasibility, not technical curiosity.
• Why a product mindset enables reuse, scalability, and faster value realisation.
• How platforms act as the governed foundation for reusable data, AI and decision-making capabilities.
• Why simplifying to fewer platforms improves trust and speed of delivery.
• How observability and adoption tracking link data products to real decision-making and P&L impact.
• Why AI success depends on evaluation frameworks (“is it right?”) and human-in-the-loop validation.
• How AI is shifting roles from coders to builders using and diversifying D&A talent beyond traditional STEM backgrounds.

Welcome to another episode of Data Debrief, the companion show to Driven by Data: The Podcast, where hosts Catherine Dowden-King and Kyle Winterbottom unpack Tuesday’s episode, share what’s been on their minds, and explore what’s really happening across the data and AI landscape.

This week, Catherine and Kyle reflect on the conversation with Peter Everill, diving deeper into the themes that matter most right now, from decision-led data strategies to the realities of operating in an AI-hyped market.

They cover:

• Why the market is becoming saturated with “AI-everything” messaging, and how vendors are losing differentiation
• The growing gap between activity and impact, and why many data teams are busy but not moving the needle
• Peter Everle’s decision-making framework, and why focusing on decisions that impact P&L is the only thing that really matters
• The cultural challenge of empowering teams to push back, and why most organisations still operate like a ticketing service desk
• The tension data leaders face between challenging the business vs protecting their role, especially under board-level pressure to “do AI”
• Why this AI cycle feels different from previous hype cycles, and how pressure is now coming from every function, not just IT
• What separates leaders who deliver real commercial value from those who don’t

They also dive into the current hiring market:

• Why data leadership hiring has become overwhelmingly saturated, with thousands of candidates for a single role
• How CVs are becoming indistinguishable in the age of LLMs
• Why job applications are no longer an effective strategy on their own
• The shift from traditional hiring to network-led, trust-based recruitment
• And what candidates must do differently to stand out in a market that’s noisier than ever

This episode is a candid look at the realities of data leadership today, where every path carries risk, AI pressure is unavoidable, and success comes down to judgement, influence, and the ability to focus on what truly drives value.

In Episode 4, of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was joined by Peter Everill, Head of Data Product at IAG, where they discuss why most organisations still invest heavily in data, analytics and AI capabilities without ever connecting that work to the handful of decisions that truly move business performance. They break down why the future of data transformation is really decision transformation, sharing a practical five-layer framework that links performance visibility, root cause, budget trade-offs, automated decisioning and enterprise optimisation directly to P&L impact, which includes;

• Why the real purpose of data and AI is not building outputs, but improving the decisions that materially change business performance.
• How Peter’s career shifted from historical reporting into transforming decisions and workflows that directly influence operating models.
• Why the biggest capability gap in most organisations is linking technical capability to strategy, commercial priorities and P&L outcomes.
• How starting with business decisions instead of tools helps avoid the common trap of capability-first transformation.
• Why stakeholder requests that cannot be tied to action or performance change should rarely make the roadmap.
• How bottom-up demand creates sprawl, fragmented priorities and lots of activity that never ladders up to enterprise value.
• What the five decision layers are that connect data transformation directly to P&L impact.
• Why performance visibility is the first step to stop leadership teams debating numbers instead of making decisions.
• How root cause analysis becomes the turning point where prioritisation, ownership and commercial focus become clear.
• Why smarter budget trade-offs matter more than simply asking for more investment.
• How AI creates the most value when it improves decision quality before automating workflows at scale.
• Why automating workflows without improving the decision just helps organisations get to the wrong answer faster.
• How enterprise optimisation exposes where one team’s success is unintentionally creating cost or lost sales elsewhere.
• Why local optimisation inside siloed teams often damages enterprise performance without leaders realising it.
• How making performance problems visible requires executive sponsorship because transparency can create organisational tension.
• Why root cause is usually the point where leaders realise they still lack clarity on what really drives performance.
• How both business leaders and data leaders should start by focusing on one decision that matters most.

Welcome to another episode of Data Debrief, the companion show to Driven By Data: The Podcast, where hosts Catherine Dowden-King and Kyle Winterbottom sit down to unpack Tuesday’s conversation, share what’s been on their minds, and explore what’s really happening across the data and AI landscape.

Fresh off Kyle’s return from holiday, the pair dive into Tuesday’s episode with Daragh Kelly, Chief Data Officer at The Economist, unpacking the ideas that stood out most, and a few that challenge the dominant narratives in the market right now.

They cover:

• Why the concept of “Trad AI” (traditional machine learning and data science) is a useful lens, and how the market is blurring the lines between legacy AI and the new wave of generative and agentic capabilities
• The ongoing hype cycle in AI, why it’s nothing new, and how organisations risk getting distracted by buzzwords rather than focusing on real outcomes
• The growing gap between building AI solutions and making them scalable, reusable, and commercially viable
• The importance of defining what “AI” actually means inside your organisation, and why vague language is creating confusion at the board level
• The tension between speed and direction, and why moving fast means nothing if you’re not solving problems that actually matter
• Whether operating models really need to change for AI, and why Dara’s perspective challenges the prevailing narrative
• The shift from analysts as insight generators to “toolmakers”, and what that means for the future of data and analytics roles
• The rise of self-serve capability across organisations, and the risks of uncontrolled experimentation without governance
• The ongoing power struggle between CDOs, CIOs, and CTOs over AI ownership, and why the answer is far from settled
• The role of optics, titles, and external brand in shaping career progression for data leaders in an AI-first market

Plus, in this week’s Thoughts of the Week, Kyle challenges the long-standing narrative around “having a seat at the table,” arguing that it’s often used as an excuse for not delivering value, and that true impact comes from driving outcomes, regardless of reporting lines. Catherine reflects on the role of diversity, equity, and inclusion in the data community, why the conversation is still far from where it should be, and the responsibility leaders have to actively shape a more inclusive industry.

Like and subscribe wherever you listen, and if you’ve got a question or topic you’d like the team to cover, email [email protected]

In Episode 3, of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was joined by Daragh Kelly, Chief Data Officer at The Economist, where they discuss why most AI initiatives are still failing, why there's not much measurable progress and how insight functions have become toolmakers and decision intelligence partners, which includes;

• Why most AI initiatives fail because they as a solution looking for problems.
• The importance of aligning AI use cases to strategic goals, KPIs and measurable outcomes.
• Why speed rather than velocity leads to very little measurable progress.
• Why compelling POCs create false confidence before the real production challenges begin.
• The deployment gap: why robust, scalable and commercially viable AI is still hard.
• Why disconnected tools and poor workflow integration stall AI value realisation.
• The simple test for prioritisation: is this problem big enough to matter?
• Why the best AI use cases act as building blocks for future capability.
• How AI and UX together are driving true self-service insight generation.
• Why insight teams are evolving from answer providers to toolmakers.
• The growing importance of data governance, quality and observability in an AI-first world.
• How distributed insight creation can weaken corporate memory and knowledge curation.
• The skills shift toward UX, enablement, storytelling and decision intelligence.
• Practical build vs buy criteria in fast-moving and rapidly commoditising AI markets.
• Why operating models matters less than discipline, purpose and capability building.

Welcome to another episode of Data Debrief, the companion show to Driven By Data: The Podcast, where hosts Catherine Dowden-King and Kyle Winterbottom sit down to unpack Tuesday’s conversation, share what’s been on their minds, and explore what’s really happening across the data and AI landscape.

With Kyle away this week, Catherine is joined by Kinnari Ladha, CDO, Travelodge, to reflect on Tuesday’s episode with Barry Panayi, diving deeper into the themes that sparked the most discussion, and a few that deserve even more attention.

They cover:

• Why the path from CDO to NED is becoming increasingly attractive, but remains unclear, relationship-driven, and difficult to break into
• What boards actually expect from data leaders, and why being “the data expert” is only a small part of the value
• The growing lack of standardisation in the CDO role, and how differing mandates, ownership, and expectations are shaping success or failure
• The emerging split between data foundations and value delivery, and whether the CDO role is becoming too big for one person
• The ongoing tension between CDO, CIO, and CTO over AI ownership, and why no single function can truly own it
• The challenge of balancing short-term ROI with long-term foundational investment, and why many organisations are still getting this wrong
• Why strong collaboration between data and technology leaders is critical, and how misalignment can derail even the best strategies
• The widening maturity gap across organisations, from those deploying AI at scale to those still grappling with basic data challenges
• Why data and AI literacy remains one of the most overlooked barriers to success, despite heavy investment in tools and platforms
• The growing pressure on organisations to drive adoption, and the reality that without behaviour change, even the best technology fails

Plus, in this week’s Thoughts of the Week, Catherine shares her perspective on the growing divide in organisational maturity and how the AI boom is widening the gap between leaders and laggards. Kinnari reflects on the often-overlooked challenge of data and AI literacy, and why organisations must prioritise workforce capability if they want to unlock real value from their investments.

Like and subscribe wherever you listen, and if you’ve got a question or topic you’d like the team to cover, email [email protected]

In Episode 2, of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was re-joined (over 2.5 years on) by Barry Panayi, Group Chief Data Officer at Howden, where they discuss, how the CDAO role continues to evolve and whether accountability is being diluted, which includes;

• Why the CDAO role is splitting between commercial outcomes and BAU leadership
• Why the biggest trade-off today is speed vs sustainable value
• A pragmatic view on AI ownership: enterprise enablement vs bespoke build
• The risk of over-indexing on ROI and neglecting foundational data capability
• The danger of the CDAO role being watered down
• Why many organisations still hire CDAOs for the wrong mandate
• How culture and incentives shape whether data leaders can succeed
• How AI is making value measurement easier than traditional data work
• Why proving quick wins can sometimes slow long-term progress
• Why CDAO–CTO chemistry now matters more than job titles
• The leadership lesson: make the least bad decision with conviction
• Why Barry wrote The AI of the Beholder as a leadership decision simulator
• The reality that there is rarely one “right” leadership choice
• Why future board opportunities for CDAOs require broader leadership breadth
• What boards actually value from ex-CDAO leaders in NED roles