Driven by Data: The Podcast

Epizódok

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  Data Debrief: Dark Hair, AI Nightmare & The Biggest Role in Data? 01.10.2026 38p

  Catherine and Kyle are deep in event-season chaos this week — the printed Driven by Data magazine has just gone to print, the digital edition is about to launch, and Driven by Data Live is just eight days away. Catherine recaps her (very delayed) trip to Big Data London: reconnecting with the community, a noticeably different vendor landscape stacked with new AI-era players, and "context" as this cycle's buzzword of choice. It sparks a wider conversation with Kyle about why staying visible in the market, through events, LinkedIn or simply contributing to the conversation, matters more than ever in a leadership hiring market where six months out of work is fast becoming the norm.From there, the conversation turns to AI and human oversight, prompted by the now-infamous clip of a Canadian politician reading his AI-generated speech live, chatbot suggestions and all. Catherine and Kyle use it to unpick a bigger pattern: traditional checks and review are quietly disappearing as people lean on LLM output uncritically, whether that's AI-generated pitch decks stripped of intentional choices, or candidates' CVs that increasingly sound like the job description they were lifted from. They also debrief Tuesday's episode with Aimee Smith, UK Government Chief Data Officer, covering her culture-not-technology diagnosis of Whitehall's data-sharing problem, the surprising admission from Microsoft and Amazon that government's scale may be beyond current technology, and just how subjective "success" and value become once you're operating in public service rather than the private sector.They also discuss:Why Catherine's top tip about not moaning about trains backfired within hours of giving it.Why so many familiar faces were missing from Big Data London's vendor stands, and what that says about the market.Why "context" has become this season's buzzword, and why some vendors are stretching to say yes to whatever a prospect asks for.Why stepping out of the market for 18 months, even while genuinely busy doing the day job, can leave you without a way back in when you need one.Why the days of walking straight into a new role within a few weeks of redundancy are over.Why one CDO is advising his mentees to keep six months of financial runway.Why leadership hiring is busier than ever, but so is the competition for every role.What the viral clip of a politician reading his AI-generated speech, chatbot prompts and all, reveals about eroding human oversight.Why neither Catherine nor Kyle blame the politician himself, and where they think the real failure sits.Why AI-generated pitch decks and slides are losing the intentionality that came from every element being a deliberate choice.Why candidates rewriting their CVs to mirror a job description word-for-word is backfiring on them.Why six major banks are flagging the fraud and accountability risks of AI agents transacting on people's behalf.Why B2C brands are already leaning into "handmade" and human-made marketing, and why B2B is likely to follow.Why hiring a copywriter who sounds human could become a genuine differentiator again.Why Aimee Smith's move from 25 years in policing to UK Government Chief Data Officer meant relearning how power and language actually work.Why even Microsoft and Amazon weren't confident the technology exists to handle government's scale and complexity.Why culture and risk appetite, not technology, are the real blockers to data sharing across government.Why "value" and "success" mean something fundamentally different in public service than in the private sector.Why government's federated, siloed structure makes Aimee's mandate harder than the equivalent role in the private sector.A reminder that Aimee Smith will be at Driven by Data Live next week, so bring your questions.
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  S7 | Ep 26 | 300 days as the UK Governments CDO with Aimee Smith, UK Government Chief Data Officer, UK Civil Service 29.09.2026 52p

  In Episode 26 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is rejoined by Aimee Smith, Chief Data Officer at the UK Government, where they discuss her move from 25 years in policing and law enforcement into government's top data role, and why she believes culture, risk appetite and misaligned investment incentives, not legislation or technology, are what's really blocking data sharing across Whitehall.They also touch on the UK's evolving position on data and AI sovereignty, the real purpose behind the National Data Library, and how Aimee is building a framework to track and prove the value of government data.They also discuss:Why Aimee left a 25-year career in policing to become the UK Government's Chief Data Officer.How she is building influence across government departments without having formal authority over them.Why she restructured the Chief Data Officer Council around a strategic data roadmap and sector-based leads.What makes data sharing across government harder than the equivalent challenge in the private sector.Why culture and risk appetite, not legislation or technology, are the biggest blockers to data sharing.How she plans to fix the incentive problem by changing how Treasury assesses technology and data investment bids.Why she wants government's most valuable data treated as critical national infrastructure.How linking investment to data assets rather than departments could unlock sharing.Why legacy technology is as much a data cataloguing problem as a modernisation one.What she believes better data sharing could unlock for citizens navigating public services.Why she thinks the data sovereignty debate currently overlooks the data itself.How she is working with cross-government commercial teams to embed sovereignty principles into procurement.What the National Data Library actually is, and the shifting priorities behind its original manifesto pledge.How the National Data Library differs from a centralised data lake.Why AI adoption depends on getting the underlying data foundations right first.How she is using AI as a way to keep data on the agenda inside government.Why she is cautious about how AI investment gets communicated to the public.What her three-part framework for tracking data value looks like (commercialisation, valuation, usage).How Treasury's new balance sheet requirement for data valuation came about.What she hopes to point to as evidence of progress in twelve months' time.Thanks to our sponsor, Data & AI Literacy Academy.Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity.If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first.At Data & AI Literacy Academy, they don't just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They're bridging the gap between business and data teams, so they can all work towards aligned outcomes.From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme.They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve.Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com.Thanks to our sponsor FOIL AIFOIL are an AI consultancy, and one of the most exciting to watch right now.​Fast-growing, genuinely ambitious, and refreshingly down to earth, with a leadership team who have been doing this for years and are well respected for it.​FOIL push data leaders to claim a voice at the top table, to lead the business rather than trail behind it with a handful of AI productivity tools. They push the point that if every competitor has the same tools, productivity is not an advantage. The real prize with AI is bigger and FOIL have the expertise – both strategic and technical – to push the boundaries of what your organisation can achieve, in a practical way. ​FOIL are a lot bolder and braver that the traditional consultancy. That confidence comes from deep practitioner expertise, and it shows in how they engage with their partnerships.​They are claiming the phrase: the autonomic business. A business that senses what is happening, decides within clear guardrails, adapts as things change, and keeps improving on its own. Intelligence built into how the company runs, and they’ve just been recognised at the British Data Awards for their work with Welsh Water on exactly this concept.​Learn more at https://foilai.co.uk/
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  Data Debrief: Networking, Intentionality & CVs that stand out 24.09.2026 40p

  In this week's Data Debrief, Kyle Winterbottom and Catherine Dowden-King recap the Future of Data, AI & BI Summit with Starburst, share Catherine's top tips for networking at data events, and debrief this week's main episode with Simon Turner, Chief Technology Officer at Foil AI — including the Welsh Water case study that brought his "autonomic business" concept to life.They also discuss:Why the Future of Data, AI & BI Summit's core theme was the gap between giving people AI tools and reimagining how a business actually operates.What "codified business knowledge" — the context layer — means for scaling AI from local productivity hacks to full agentic systems.Catherine's top tips for networking at data events: doing your homework, messaging speakers in advance, and finding natural pauses for small talk.Why leading with curiosity, rather than negativity, makes conversations easier to build on.Why giving yourself permission to step away from an event is as important as showing up to it.How Simon Turner's Welsh Water case study used agentic AI to cut through false alarms during this summer's drought.Why the "autonomic business" concept compares agentic AI to a heartbeat that speeds up without conscious thought.Why having access to AI tools is no longer a competitive advantage, now that everyone has them.The debate between focusing on a handful of core business metrics versus giving people room to experiment and discover new use cases by accident.Why the best AI use cases sometimes emerge locally, before being scaled across a business.Why AI is making CVs look increasingly similar, and how outcomes-focused writing helps candidates stand out.Why intentionality — in networking and in career planning — matters more than ever in a fast-changing market.Why data and AI leaders should back the people already experimenting successfully with AI tools, not just the official roadmap.A preview of the "data dilemmas" segment returning for Driven by Data Live.
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  S7 | Ep 25 | The Autonomic Business: Why AI Is a Leveller, Not an Advantage with Simon Turner, Chief Technology Officer at FOIL AI 22.09.2026 47p

  In Episode 24 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Simon Turner, Chief Technology Officer at FOIL AI, where they discuss the "autonomic business" – organisations run by bounded, goal-driven digital entities that work alongside humans – and why, when every company has access to the same models and tools, competitive advantage has to come from somewhere else: your codified business knowledge, your data, and the creativity of your people.The conversation covers why the most common mistake is automating processes that shouldn't exist in their current form, a real-world water utility case where an autonomic entity cut alarm triage from 40 minutes to seconds during the drought, and why ownership of this agenda ultimately lands with a properly empowered CDO.They also discuss:Why generative AI was the catalyst for rethinking the traditional consulting model.Why "we want to do more AI" is the wrong request, and what businesses actually need.Why, if everyone says they're behind, someone has to be in the lead.Why the technology is easy, and landing it as systemic change is the hard part.What an autonomic business is, and why the term comes from biology.How an autonomic entity differs from RPA and traditional automation.Why autonomic entities pursue goals rather than execute fixed processes.How Gartner's digital-twin thinking seeded the idea years before ChatGPT.Why the autonomic business depends on knowledge management, not technology.How LLMs and knowledge graphs unlocked the 80% of business information that is unstructured.Why access to the same tools is a leveller, not a competitive advantage.Why reducing cognitive load matters more than raw speed.Why operating model and culture decide whether AI transformation succeeds.Why automating a broken process at scale creates no value.Why most business processes live in people's heads, and how to make them computable.How a water utility used an autonomic entity to cope with alarms rising from 800 to 3,800 a day.What the four human–AI partnership models look like, from "entity proposes, human decides" to "human retains authority".What needs to be true before entities can act fully autonomously.Why the CDO should own the AI agenda, and why that role can no longer sit inside IT.Why generative AI is becoming the Excel of the 90s.Why ROI rarely comes from a single AI project.Why headcount is a dangerous yardstick for ROI, and what successful organisations do instead.Thanks to our sponsor, Data & AI Literacy Academy.Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity.If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first.At Data & AI Literacy Academy, they don't just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They're bridging the gap between business and data teams, so they can all work towards aligned outcomes.From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme.They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve.Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com.
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  Data Debrief: We get Political 17.09.2026 40p

  In this week's Data Debrief, Catherine Dowden-King and Kyle Winterbottom are recording a day early ahead of a busy stretch in London – a custom client event, the Future of Data, AI & BI Summit and Big Data London, with Driven by Data Live on 8th October. The headlines this week belong to Donald Trump, who has dismissed AI safeguards and calls for a "kill switch" as a hoax, which sets up the episode's central question: what happens when the people with the power to sign off on AI – presidents or CEOs – sit well above the technical detail and the risk?Catherine and Kyle draw the parallel between geopolitical "space race" thinking and the boardroom instinct to move first and mop it up later, before turning to the vetting questions every data leader should be asking of vendors and LLM providers: what are their values, why are they in the market, and what's in it for them? They then debrief Tuesday's episode with David Castro-Gavino and Boyan Angelov – merchants of complexity, friction versus maturity models, and a CDO role that is hired without an objective – and Kyle's thought of the week on the environmental cost of AI that nobody in the industry seems to be talking about.They also discuss:Why one of the world's most powerful people calling AI safeguards a hoax is a bigger statement than it sounds.Why the AI race is being driven by the fear of China catching up, and how that agenda filters into business.Why "just do it, we'll mop it up later" is the same decision whether it's made in the White House or the boardroom.Why the spectrum between doomsday and handbrake-on leaves everyone struggling to know who to trust.What questions to ask of any vendor or LLM provider before plugging them into your business.What the Careless People revelations about targeting insecure teenagers tell us about tech companies' incentives.Why tech companies handling health and genetic data aren't regulated like health companies.Why "just because you could doesn't mean you should" is the age-old debate, and why nobody boycotts anyway.How a tight-knit CDO community quietly blacklists vendors with poor ethics.Why the vendor community has to own the fact that every pitch deck now sounds the same.Why executives can be forgiven for not understanding the weeds of "AI-powered" everything.How the event agenda has flipped from getting executives to care about data to reining them in.Why data leaders have to learn to sell, and why selling is really just communication.Why influence at ExCo level comes down to trust, credibility and relationships.Why "merchants of complexity" and self-inflicted complexity resonated so strongly with listeners.Why friction isn't uniform, and why "the whole thing's a mess" is rarely true for every department.Why maturity models are theatre, and diagnosing friction through each role's lens is the practical alternative.Why frameworks can contain thinking, and why data people run to structure when they might need creativity.Why the CDO must be the only senior role in business hired without an objective, and what a 90-day plan is really for.Why nobody in the AI adoption conversation is asking whether we need to be using it at all.What 700ml of water per ChatGPT prompt says about the environmental cost of replacing Google.Why nothing in data is sociologically neutral, and why people only care about data centres once the bulldozers arrive.What the new Driven by Data Productions brand means for the community, the podcast, the events and the magazine.How to get 20% off Enabling Data with the code DRIVEN20.
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  S7 | Ep 24 | Are you a Merchant of Self-Inflicted Complexity? David Castro-Gavino, Executive Director, Head of Data Deployment at AstraZeneca and Boyan Angelov, Principal Strategist at Exxeta 15.09.2026 58p

  In Episode 24 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is rejoined by David Castro-Gavino, Executive Director, Head of Data Deployment at AstraZeneca, and joined by his co-author Boyan Angelov, Principal Strategist at Exxeta, where they discuss their new book, Enabling Data, and why the data industry is still stuck in Groundhog Day. The same three arguments – who owns that number, is it right, and why does it take so long – have been repeating for thirty years, and rather than fixing them, the industry keeps renaming the problem.The conversation covers why most complexity in data is self-inflicted, why maturity models are "data theatre" compared to diagnosing friction, and why AI hasn't solved any of this – it has poured fuel on the fire.They also discuss:Why the industry has a short collective memory and keeps rediscovering problems solved twenty years ago.What the three recurring arguments are that every data organisation keeps having.Why renaming the symptom – big data, data mesh, platforms – never fixes the underlying problem.How a simple pizza business becomes a data nightmare the moment it goes digital.Why most complexity in data is self-inflicted, and why that is good news.Why "technology is not the problem, you are" is deliberately provocative.What four questions to ask before going back to the market for a new tool.Why fixing the system, not the tool, is the maxim that matters.Who the "merchants of complexity" are, and why consultants are usually the culprits.Why making things simple is the hardest job in data.What's wrong with using maturity scores as the objective.Why measuring the wrong things promotes the wrong behaviours.How to practically find friction by refusing to accept the first answer.Why friction looks different for an analyst, an engineer and a business leader.Why not every foundational problem needs to be solved, and how to avoid spending forever in the basement.Why data teams that don't understand the business are missing the biggest opportunity.What the cargo cult is, and why copying Spotify's operating model won't make you Spotify.How the enabling model's four pillars – people, governance, technology and enablement – fit together.Why the fragility of senior data leadership is structural rather than personal.Why data doesn't create friction in an organisation, it reveals it – and gets blamed for it.Why a clear mandate matters more than who the CDO reports to.Why the industry needs to stop hiring data leaders on a shopping list of technical skills.How AI has exposed how little progress most companies have actually made on the fundamentals.Thanks to our sponsor, Data & AI Literacy Academy.Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity.If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first.At Data & AI Literacy Academy, they don't just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They're bridging the gap between business and data teams, so they can all work towards aligned outcomes.From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme.They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve.Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com/drivenThanks to our sponsor FOIL AIFOIL are an AI consultancy, and one of the most exciting to watch right now.​Fast-growing, genuinely ambitious, and refreshingly down to earth, with a leadership team who have been doing this for years and are well respected for it.​FOIL push data leaders to claim a voice at the top table, to lead the business rather than trail behind it with a handful of AI productivity tools. They push the point that if every competitor has the same tools, productivity is not an advantage. The real prize with AI is bigger and FOIL have the expertise – both strategic and technical – to push the boundaries of what your organisation can achieve, in a practical way. ​FOIL are a lot bolder and braver that the traditional consultancy. That confidence comes from deep practitioner expertise, and it shows in how they engage with their partnerships.​They are claiming the phrase: the autonomic business. A business that senses what is happening, decides within clear guardrails, adapts as things change, and keeps improving on its own. Intelligence built into how the company runs, and they’ve just been recognised at the British Data Awards for their work with Welsh Water on exactly this concept.​Learn more at https://foilai.co.uk/
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  Data Debrief: Schools Back, AI Attack & the future of paid employment 10.09.2026 48p

  In Episode 23 of Season 7 of Data Debrief, Catherine Dowden-King and Kyle Winterbottom unpack Tuesday's conversation with Greg Freeman, CEO and Founder of Data & AI Literacy Academy, and the gap between organisations that have given everyone a Copilot licence and those that are actually using AI to change how the business operates. As Catherine puts it, a Sunday league player and a Premier League player are both "playing football" – but nobody's confusing the two.They also get into the week's headline that AI has a "greater than 10% chance" of wiping out humanity, why a growing anti-AI mood outside the data bubble matters for leaders trying to drive adoption, and why a landscaper turned content creator might be the best human-in-the-loop example going.They also discuss:Why September is the real new year for data leaders, with budget season and event chaos hitting at once.Why the "AI will kill us all" headlines are irresponsible without the evidence to back them up.How to tell the difference between a credible warning and a researcher looking for a headline on the way out the door.Why 70% of Facebook comments on an AI-generated event poster are people refusing to attend, and what that tells leaders about the mood outside the bubble.What a year five "meet the teacher" evening on WhatsApp groups has in common with the AI conversations happening in boardrooms.Why the toilet-door graffiti of the 1970s and today's comment sections are the same human behaviour at a scale our brains can't cope with.How Catherine explains agentic AI at the dinner table with trains and tracks, and why it still doesn't land.Why "AI" is used to mean automation, machine learning, LLMs and agents interchangeably, and why that confusion matters.What "buttonology" means, and why both hosts are stealing the term.Why training and education are two different interventions, and why most organisations only do the first one.What Greg's three personas – the asker, the conversationalist and the process redesigner – reveal about where most employees really are.Why AI maturity scales measure who can drive the machine rather than who's transformed their thinking.Why organisations want competitive advantage but are investing in local productivity, and why the two aren't the same thing.Why picking four or five core use cases beats a Venn diagram of everything you could possibly do.Why leaders must ask "have you actually understood this?" before accepting AI-assisted work.Why people treat LLMs like Google when Google gave you sources and LLMs give you a decision.Why the absence of sponsored results in LLMs makes people less likely to question what they're served.Why the context layer, not the tool, is where the real value in AI sits.What a founder's blanket ban on "Claude content" reveals about the perception problem holding back adoption.Why whether AI sits with the CIO or the CDO comes down to whether it's seen as a tool or a transformation.Why culture has to allow people to rip up a process and fail before any of the redesign talk becomes real.Why cutting graduate intake could leave businesses with a succession crisis in a few years' time.How the Dodgy Gardener quit his day job by pairing ChatGPT garden designs with advice from tradespeople in the comments.Why attention is the digital currency of the future, and why B2C businesses will create roles to work out how to win it.
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  S7 | Ep 23 | Redesigning Organisations for AI-Driven Competitive Advantage with Greg Freeman, CEO & Founder, Data & AI Literacy Academy 08.09.2026 50p

  In Episode 23 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is rejoined by Greg Freeman, CEO & Founder at Data & AI Literacy Academy, where they discuss why most organisations have mistaken tool training for AI literacy – and why a data-literate workforce, critical thinking and the willingness to rebuild processes from the ground up matter far more than knowing which buttons to press in Copilot.They also explore why data quality and governance are finally having their day in the sun, how leaders must role-model the discipline to challenge AI-generated work, and why the businesses winning with AI focused on four or five core processes rather than spinning up 95 pilots.They also discuss:Why the line between data literacy and AI literacy is blurry, and why Greg wants it to stay that way.Why a data-literate workforce is the enabler of an AI-ready workforce.Why data quality, governance and risk management have gone from "not that sexy" to the most important topics in the business.Why there isn't a Copilot buttonology programme in the world that can teach critical thinking.How AI slop is bleeding from LinkedIn into the work employees put in front of internal and external audiences.Why leaders must ask pointed questions of AI-enabled work to test whether the human in the loop has actually done their job.Why people treat LLMs like Google, and why that makes them less able to challenge the answers.What separates the asker, the conversationalist and the process redesigner, and why 98% of employees are still stuck at the first stage.Why leaders need the mindset to burn processes down and rebuild them with AI at the core, rather than layering it on top.Why enterprise learning conflates training with education, and why that is the root of the tool-centric problem.How hyperscalers and training partners are incentivised to teach the tool rather than transferable principles.Why a workforce using Copilot instead of Google is an expensive thing, not a useful thing.Why generative AI gives executives a hands-on "aha moment" that dashboards never did.Why nine out of ten AI conversations mean generative AI, and what that costs organisations in forecasting, decisioning and recommendation opportunities.Who should own the operating model conversation with the board, and why it depends on having the right kind of data and AI leader.Why cutting headcount and graduate intake is the wrong reason to do AI, and why AI-native graduates are the hires to make.What the US market's shift from 95 pilots to four or five core use cases teaches UK businesses.How democratising AI capability into local teams frees the central team to focus on the big wins.Why customer-facing AI use cases remain a minority, and what Lloyds Bank gets right.How to measure AI literacy by whether people see and solve business problems differently.
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  Data Debrief: Creepy AI Teddy, Running App Renters & The beginning of the Real AI Use cases 03.09.2026 39p

  In this week's Data Debrief, Kyle Winterbottom and Catherine Dowden-King unpack a ChatGPT-powered "smart learning" teddy bear aimed at three-year-olds, and use it as a way into a bigger question: when we let technology deliver the output, what happens to the learning journey that used to produce it? That thread runs from toddlers and university degrees all the way into the enterprise.They also discuss Tuesday's main episode with Chris Pearce, Chief Data Officer at Ageas UK, why AI use cases are finally moving from the sandbox into production, and why the value of that work is still invisible to most customers.They also discuss:Why an AI companion that validates a child's every feeling removes the friction that teaches them how to share, wait and apologise.Why "screen-free" is a weak selling point when the device still talks back, listens and adapts.How closed-circuit toys like a Toniebox or Yoto player carry a fundamentally different risk profile to a Wi-Fi-connected, always-listening teddy.What happens when parental controls protect one side of the conversation but not what the child says.Why universities banned AI not to stop augmentation, but to stop replacement — and why that distinction matters everywhere else too.How one Strava user overlaid running-route data with rent and income data to find up-and-coming New York neighbourhoods before prices caught up.Why personal, intuitive data use cases like that one are a better route into data literacy than heavy-handed formal training.Why psychological safety keeps surfacing as the precondition for genuine experimentation with AI.How the AI hype cycle has bought data leaders more freedom to test and fail than the analytics era ever did.Why podcast guests are suddenly willing to name specific, productionised use cases when a year ago they wouldn't talk on the record.What the shift from internally-focused efficiency gains to customer-facing AI means for how organisations talk about their investment.Why a business can cut processing times from 100 days to five and still have customers asking what changed for them.How the gap between the AI narrative and the actual customer experience is becoming a reputational problem, not just a comms one.Why Kyle still had to request a paper form by post to update his details with a pension provider in 2026.How Octopus Energy empowering agents to send flowers or waive costs resets customer expectations for every other provider.Why data teams need a feedback loop with customers without becoming a ticket office that builds whatever the last complaint asked for.What Chris Pearce's point about hallucinations — that nobody ever measured how often tired, stressed humans got it wrong — says about the standard we hold AI to.Why the structural and operating model problems inside organisations, not the technology, are what keep use cases stuck in the sandbox.How the AI risk conversation has finally given data governance, quality and management their moment of investment.Why CDOs should take that funding while it's on the table, whatever vehicle got it there.
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  S7 | Ep 22 | Getting AI out the Sandbox and into Production with Chris Pearce, Chief Data & AI Officer, Ageas 01.09.2026 51p

  In Episode 22 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Chris Pearce, Chief Data and AI Officer at Ageas, where they discuss why so few organisations manage to get AI out of proof-of-concept and into live production. Chris makes the case that this is a structural and operating model problem rather than a technical one, and that the businesses which crack it are the ones that understand their own commercial engine intimately enough to know exactly which decision they are trying to change.Drawing on a 250-person function spanning data engineering, data science, AI engineering, infrastructure and governance, Chris walks through real deployments into Ageas's contact centres, how the value of those deployments is measured and attributed to the P&L, and why the risk conversation with a board is far more winnable than most data leaders assume.They also discuss:Why rolling out Copilot licences bears no resemblance to putting LLMs into front-end production systems touching customers in real time.What the full cross-functional cast actually looks like, from SRE and infrastructure to UX, middleware developers, AI engineers, business SMEs, risk, legal and compliance.Why AI delivery is fundamentally a structural problem, with the necessary skill sets scattered across different leaders, agendas and backlogs.How building AI capability in isolated pockets of the ecosystem guarantees you never leave POC land.Why the first question on any piece of data science work should be how you intend to measure it, and why nothing starts until that's answered.How Ageas used LLM summarisation at the chatbot-to-agent handover to remove friction for customers already losing patience.Why after-call work was worth attacking, and what shaving minutes off every call does to backlogs, concurrency and demand.How A/B testing capability across 50 agents against another 50, de-biased for tenure and experience, produces evidence a board can't argue with.What it takes to build a genuine culture of experimentation in an environment as dynamic as a contact centre.Why "my job is to help people" is where most value conversations begin, and how to move past it.How to trace the decision chain that follows once the phone goes down, and why that's where the financial link is found.Why brilliant technical analytics is squandered without the work of presenting it visually and narratively.What has to be true for a change in decision-making to be logged, monitored and made someone's accountability.Why any organisation asking for an AI strategy should be asked about its business strategy first.How to uncover a business strategy that isn't written on a wall or neatly captured in a PDF anywhere.Why starting with low-hanging fruit builds the patterns, the track record and the appetite for bigger bets later.What the doom loop of perpetual proof-of-concept does to credibility, investment and the perception of ROI.Why perfect temples of data platforms get built over four or five years and then fail to land.How the risk conversation changes when you demonstrate the operational, technical and information security controls that already exist.Why hallucination rates deserve to be compared with how often humans under pressure get things slightly wrong.What is missing from every AI maturity framework Chris has encountered, and why counting models in production is activity rather than maturity.Why software development skills are becoming essential for data scientists, and how AI engineering mirrors the data science unicorn boom of fifteen years ago.Why the technical barrier to entry has never been lower, and why adaptability is now the trait Chris values most.Why every practitioner needs a degree of commercial nous, and what happens to retention when people can't see the impact of their work.Thanks to our sponsor, Data & AI Literacy Academy.Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity.If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first.At Data & AI Literacy Academy, they don't just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They're bridging the gap between business and data teams, so they can all work towards aligned outcomes.From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme.They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve.Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com/driven
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  Data Debrief: Blame AI! Averages Lie! and Kyle's throat sounds like he's going to... Cough. 27.08.2026 36p

  In this week's Data Debrief, the companion show to Driven by Data: The Podcast, Kyle Winterbottom and Catherine Dowden-King unpack the week's main episode with Michael Ross and range far beyond it into the collapse in graduate hiring, the succession planning nobody is doing, and what's really happening at both ends of the data job market.From a record 45% drop in advertised graduate roles, to the experienced leaders who've been out of work for two years, to Michael's case that every average hides an opportunity, Kyle and Catherine make the argument that AI is taking the blame for decisions plenty of businesses already wanted to make, and that the bill for not developing people will land in about five years' time.They also discuss:Why a 45% drop in advertised graduate jobs is the lowest figure ever recorded, and why AI can't be held responsible for all of it.How record university enrolment colliding with a shrinking entry-level market creates a problem unfolding in real time.Why "entry-level" data roles asking for two years of Python or SQL were never really entry-level.What happens to the pipeline when the admin-heavy tasks juniors cut their teeth on get absorbed by agents.Why the real risk isn't AI replacing juniors, but having nobody ready when the current workforce retires.How data roles are shifting towards QA, product management and facing back into the business.Why succession planning has only ever been pointed at the top of the house, and why that has to change.What skills matrices and career pathways expose the moment you ask "and when this bottom layer moves up, then what?"Why some organisations announced AI-driven headcount cuts when the business was simply performing badly.How "we're cutting because of AI" got turned into a PR positive rather than a negative.Why a retailer, a telco and an airline sat at the same table are nowhere near the same stage of the journey.What the senior end of the market actually looks like, and why it gets discussed far less than the graduate end.Why there are more head of, director and VP roles than at any point in fifteen years, even as true CDO roles decline.How being overqualified has become as much of a barrier as being underqualified.Why an entire cohort of data leaders has been tarred with the same brush through no fault of their own.How the failure to prove value from data and analytics now has a direct, downstream human cost.What Michael Ross's epiphany moment says about technical specialists becoming commercial operators.Why de-averaging matters more than any dashboard, and how averages quietly mislead entire teams.How an 80% average occupancy hid the fact that no hotel was anywhere near 80%.Why 100% occupancy might be a pricing failure rather than a success story.What it takes for a CEO to get close enough to the commercial detail of their own business to win.Why putting your head above the parapet takes bravery, and why the cost of not doing it is the situation the industry is now in.Why Dolly Parton's Imagination Library may be the most important thing she ever built.What's left of the Future of Data, AI & BI event, Driven by Data Live on 8 October at Tobacco Dock, and the new roles on the NED Appointment Finder.
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  S7 | Ep 21 | What being Advisor to a FTSE 100 UK CEO for 10 yrs Taught me about Data Insights with Michael Ross, Data Agitator & NED 25.08.2026 1ó 1p

  In Episode 21 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Michael Ross, Data Agitator & Non-Executive Director at Domestic & General and Policy Expert. They discuss why so many consumer and retail businesses fail to turn data into commercial value, because they run on averages and siloed metrics that hide where money is actually being won and lost.Drawing on a career spanning McKinsey, Figleaves, eCommera and DynamicAction, and years advising CEOs across the Abu Dhabi Investment Authority's portfolio, Michael makes the case for de-averaging: measuring distributions rather than averages, tracking failure modes rather than outcomes, and using driver trees to connect financial results back to the controllable inputs that leaders can actually change.They also discuss:Why Michael's "viewed availability" epiphany at Figleaves exposed a 93% in-stock figure that was really 70%.How siloed teams create a hidden "coordination tax" that quietly erodes commercial performance.Why nobody owning the end-to-end customer journey is still the single biggest issue in most businesses.What separates the CEOs who thrive in a data world: strategic vision paired with "30,000 feet and two inches" of detail.Why "that's where the money is" is the real reason CEOs must get into the commercial engine.How the finance function quietly failed to become the owner of integrated, decision-driving data.Why averages are the enemy of commercial performance, and what to measure instead.How de-averaging an LTV:CAC ratio of 12 revealed that 70% of spend was acquiring customers above their lifetime value.Why bidding on your own brand terms on Google is often just a "navigation tax."What "spill and spoil" are, and how the airline and hotel industries measure their two failure modes.How Premier Inn's headline 80% occupancy hid hotels that were either 60% or 100% full.Why measuring failure metrics drives a far higher-quality conversation than chasing an average upwards.What driver trees are, and how a 1920s DuPont technique connects outcomes to controllable inputs.Why Amazon deliberately spends little time on financial outcomes and focuses on controllable inputs.Why conversion rate is "the CEO's metric" and a recipe for disaster when handed two levels down.How dashboards full of averages and filters create the "illusion of insight" rather than action.Why the best dashboards are the ones that "create a compulsion to act."How de-averaging package utilisation turns one meaningless number into clear churn risks and upsell opportunities.Why data teams so rarely get to this work, and how the "build the foundations" mandate traps them.Why so many data transformations end with "nothing's changed" and an eighteen-month reset.Thanks to our sponsor, Data & AI Literacy Academy.Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity.If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first.At Data & AI Literacy Academy, they don't just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They're bridging the gap between business and data teams, so they can all work towards aligned outcomes.From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme.They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve.Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com/driven
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  Data Debrief: Rustout, Remote Work & Replacing Kyle 20.08.2026 46p

  In this week's Data Debrief, the companion show to Driven by Data: The Podcast — Davin Crowley-Sweet OBE, CDO at National Highways, steps into Kyle Winterbottom's seat and is joined by Catherine Dowden-King to unpack the week's main episode with Nick Zervoudis and range far beyond it into the human side of data leadership.From burnout and its lesser-known cousin "rustout," to the serendipity we've lost to working from home, to why psychological safety matters more than technical mastery, Davin and Catherine make the case that the job of a modern data leader is less about building things and more about building the people and the environment in which those things get built.They also discuss:Why burnout has an opposite — "rustout" — and how to tell which one you're actually facing.How working from home has stripped the chance encounters and serendipity out of professional life.Why so much success comes down to luck, and why being open to it is the real skill.What "the worst they can say is no" taught Catherine about taking a chance.Why so many people tie their identity to a job title, and what happens when the badge disappears.How psychological safety, not technical mastery, is the real job of a data leader.Why tension is healthy and shouldn't be mistaken for conflict.What Davin took from Nick Zervoudis's episode, and the subtle power of the words "value from."Why data is valuable for what you do with it, not for its inherent worth.How to get comfortable working in uncertainty rather than chasing a perfect data-driven story.Why "let's take that offline" is the phrase Davin hates most.How cognitive diversity matters as much as the visible kind.What a neurodiversity tribunal case reveals about being thoughtful, not careful, with language.Why mentorship matters at every stage of a career, not just the junior years.How the move from technical to managerial roles goes wrong when leaders revert to command-and-control.Why curiosity and openness to learning matter more than credentials when hiring junior talent.How Davin's role has evolved into developing 170+ people and lifting people out of poverty through data careers.Why "Jack of all trades, master of none" is only half the phrase.Why a GCSE maths resit needn't define anyone, and the danger of self-limiting beliefs.Why Driven by Data Live keeps drawing people who avoid the rest of the conference circuit.
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  S7 | Ep 20 | Your Data & AI Investment Portfolio: How to Decide What Gets Funded with Nick Zervoudis, Founder at Value from Data & AI 18.08.2026 58p

  In Episode 20 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Nick Zervoudis, Founder at Value from Data & AI, where they discuss why so many data and AI teams struggle to demonstrate measurable business value — and why the real failure almost always happens upstream, long before anyone tries to articulate it.Nick makes the case that "we can't prove our value" is usually a symptom, not the disease: teams solve the wrong problem, skip the value case, or hand off value realisation to no one. Along the way they get into his five-point diagnostic framework, how to build a credible back-of-the-envelope ROI estimate before a line of code is written, how to prioritise a portfolio of opportunities, and where AI productivity savings are real versus imaginary.They also discuss:Why the inability to demonstrate value is usually an upstream failure, not a communication problem.What Nick's five-point framework reveals: wrong problem, wrong solution, poor execution, no measurement, weak communication.Why data teams keep solving the wrong problem by starting from technology instead of the problem itself.How to separate the "problem space" from the "solution space" before reaching for a tool.Why 70–80% of data teams operate as order takers rather than true collaborators.Why being ROI-positive is only the entry ticket, not a reason to do a project.What criteria actually decide prioritisation: return, payback speed, implementation readiness, and strategic relevance.Why nothing a data team builds has inherent value without an owner on the business side to realise it.How to build a credible back-of-the-envelope value case before anything gets built.Why estimating value is far easier to learn than the technical craft most data people already have.How to get stakeholders to correct a rough estimate rather than hand them a blank sheet.Why "how will we measure success?" is the most useful question you can ask when scoping work.What the bystander effect has to do with data teams quietly failing to create value.How framing work around outcomes turns engineers from code-writers into problem-solvers.Why hours saved rarely become money on the balance sheet.What the five-to-six buckets of productivity value are, and why you must never double-count them.Why some AI investment should deliberately have no business case at all.How Monday.com turned a five-week experimentation window into a $100M ARR product.Why blanket self-serve analytics or company-wide AI licences often set you up for failure.What first steps a CDO should take to re-prioritise a roadmap around measurable value.Thanks to our sponsor, Data & AI Literacy Academy.Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity.If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first.At Data & AI Literacy Academy, they don't just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They're bridging the gap between business and data teams, so they can all work towards aligned outcomes.From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme.They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve.Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com/driven
• 
  Data Debrief: Wildfires, New things at Orbition, and teases of things to come! 13.08.2026 33p

  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 Marion Shaw, Senior Director of Data, Analytics and Data Management at Cencora, and her new book on data culture, before digging into why the "data culture" debate keeps circling the same questions, and why the answer almost always comes back to people rather than technology.They cover:The wildfires spreading across South Wales and Europe, and how AI and drones are being deployed in France to spot smoke earlier, distinguish dust from smoke, and cut down the false positives that waste emergency resourceThe bigger climate paradox facing the industry, from record-wet winters followed by hosepipe bans and water mismanagement, to the uncomfortable reality that the data centres powering AI advances are themselves enormous consumers of waterWhy absolutism helps no one, and how the healthiest position on AI, sustainability, and change sits somewhere in the messy middle rather than all-in or all-outHow incentives quietly shape behaviour, illustrated by the fact that three flights across Europe can cost less than a single train from Manchester to London, and why people ultimately do what they're incentivised to doCatherine's latest build for the Orbition community: a NED Opportunity Finder, a live, daily-updating table of listed non-executive director roles showing remuneration, location, and whether the board is public or private, free to access for registered community members, and why she's so keen to see more data leaders move into board positionsWhy Marion's candour hit home, especially the reminder that you simply cannot force people to be interested in or care about data, and why that truth is uncomfortable but essentialHow technology becomes a distraction, the "shiny thing syndrome" that pulls focus away from the outcomes that actually matter, and why that's the real answer to "why now"The endlessly debated question of whether "data culture" even exists, why the industry loves arguing over semantics no one outside it cares about, and why every organisation already has a data culture, somewhere on the spectrum from barely-there to full tiltWhy culture and outcomes feed each other rather than being an either/or, and how influencing behaviours and showing results almost always happen in tandemWhy every business claims to be "data-driven," how the reality usually differs, and why a new CDO's first 90 to 100 days is really about working out where the organisation actually sits versus where leadership thinks it doesThe "slippery shoulders" problem, and why nothing improves or gets maintained unless someone genuinely owns itWhy you can't see your own culture from the inside, and how stepping out to network, attend events, and compare notes with peers is often the only way to know whether you're ahead, behind, or better off than you thoughtA look ahead to the Orbition magazine landing in October, featuring a data leader who hasn't spoken publicly in over two years, alongside mentor and mentee stories and perspectives from beyond the CDO communityThe Director of Police AI role at the College of Policing, and how its rigid entry criteria expose the same old problem seen across data leadership: job descriptions that bear little resemblance to what organisations actually want from the roleKyle's thought of the week: most job descriptions are disconnected from what the business actually needs. Organisations have learned to use the right language, asking for leaders who'll work with the board and use data to drive commercial performance, then listing purely technical requirements underneath. Until that gap closes, the mismatch between what's advertised and what's wanted will keep repeating itself. Fundamentally, every organisation already has a data culture; it simply sits somewhere on a spectrum, and the job is to understand where before trying to move it.Catherine's thought of the week: you rarely recognise your own culture until you step outside it. Whether it's trust versus micromanagement, or how your data leaders are really perceived, the comparison only becomes clear when you go out, meet people, and see how others operate. And the honesty applies inward too, because no organisation describes itself as not caring about data, so the real work is uncovering where it genuinely stands.This episode is a candid, wide-ranging conversation on data culture, ownership, and incentives, and a reminder that the hardest problems in data and AI leadership remain stubbornly human, no matter how much the technology moves on.Housekeeping: The podcast is now broadcasting on LinkedIn Live. To watch along in real time, head to the Driven by Data Productions page on LinkedIn and follow it. We go live with each episode every Tuesday at 1pm BST, and our guest often joins the comments to answer your questions. Keep an eye out for upcoming events towards the end of the year, including Driven by Data Live, where Catherine will be handing out physical copies of the new magazine.
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  S7 | Ep 19 | Technology Is Easy, People Are Hard with Marion Shaw, Senior Director, Data Analytics and Data Management at Cencora 11.08.2026 50p

  In Episode 19 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is rejoined by Marion Shaw, Senior Director, Data Analytics and Data Management at Cencora, where they discuss why building a genuine data culture is a people and process problem rather than a technology one, and what it actually takes to embed trust, curiosity and business value into how an organisation works with data.The conversation centres on Marion's new book, Why Data Culture Matters, and digs into why trust is the single attribute with the biggest ripple effect on a data culture, plus the "AI paradox" — why people distrust their own data yet blindly trust the same data when an AI hands it back to them.They also discuss:Why so many organisations pour money into technology yet still fail to see the returns.What Why Data Culture Matters covers, and who Marion wrote it for.How data culture should reflect and be built around a company's existing business culture.Why there is no universal blueprint for data culture — every organisation's version looks different.How giving people access to data without teaching them to interpret it undermines self-serve initiatives.How trust is built through recognisability, explainability, transparency and repeatability.Why CDOs should never promise something they can't actually deliver.What the "AI paradox" means for organisations rolling out generative AI tools.How younger generations risk losing critical thinking skills by taking AI output at face value.Why healthy scepticism should be a core attribute of every data culture, regardless of industry.How to measure progress in building a data culture beyond simple adoption metrics.Why dashboard adoption is a flawed proxy for a genuinely data-driven culture.Why data teams need to shift from being "order takers" to acting as business partners.What the biggest mistakes are that organisations make when trying to mandate a data culture from the top down.Why flexibility, not rigid planning, is the mindset shift data leaders need most.Why influence and communication skills are essential to embedding a data culture successfully.Thanks to our sponsor, Data & AI Literacy Academy.Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity.If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first.At Data & AI Literacy Academy, they don't just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They're bridging the gap between business and data teams, so they can all work towards aligned outcomes.From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme.They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve.Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com/driven
• 
  Data Debrief: The AI Rollback & Regrets 06.08.2026 39p

  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 Kyle's conversation with Laura Fiacco, Founder of Adaptive Assets, exploring why communication, influence and commerciality remain some of the most overlooked skills in data leadership. Alongside the episode discussion, they dive into LinkedIn's apparent AI U-turn, the growing challenge of AI-generated content, and why the future of data leadership may be less about technology and more about transformation.They cover:Why LinkedIn, Snapchat and Substack are all taking steps to tackle AI-generated content, and what it says about organisations pushing AI adoption before understanding its long-term consequencesWhether we're heading towards a world where overusing AI becomes just as much of a performance concern as not using it at allThe ethical grey areas surrounding AI-generated communication, from political statements to funeral tributes, and why context matters far more than blanket rulesLaura's perspective on communication as a skill that compounds over time, and why nobody starts by presenting to a thousand peopleWhy communication isn't synonymous with public speaking, but instead about translating technical concepts into language that business leaders understand and care aboutThe importance of influencing without authority, building relationships, and developing the commercial mindset needed to turn technical work into business valueHow organisations continue to undervalue communication, relationship building and commerciality because they're harder to measure than technical capabilityPractical ways data professionals can develop these skills themselves, including writing, voice conversations with AI, and using large language models as personalised coaching toolsWhy curiosity, listening and asking better questions are just as important as being able to communicate confidentlyKyle's reflections on why transformation is becoming the next major destination for senior data leaders as technology becomes less of the challenge and organisational change becomes the real differentiatorCatherine's comparison between today's AI leadership roles and the "e-business" and "Chief Internet Officer" titles of the dot-com era, and why AI leadership may ultimately become absorbed into every business function as the technology maturesProgramming note: If you've been following Catherine's recent LinkedIn series on the evolution of executive technology roles, this episode expands on that conversation, exploring why today's AI job titles may eventually follow the same path as digital and internet leadership before them.This episode is a reminder that while AI continues to dominate headlines, long-term success still depends on the fundamentals: communicating clearly, influencing effectively, building relationships, and helping organisations change, not just implement new technology.
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  S7 | Ep 18 | Influence without Authority by Communicating like an Executive with Laura Fiacco, Founder at Adaptive Assets 04.08.2026 51p

  In Episode 18 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Laura Fiacco, Founder of Adaptive Assets, where they discuss why communication, influence and commercial thinking have become the competitive advantage for modern data professionals, and how learning to communicate like an executive is now just as important as technical expertise.They also discuss:Why influence without authority is the defining skill for modern data leaders.The 4 quadrant brain framework for tailoring communication to different thinking styles.Why successful influence starts by understanding other people's goals before presenting your own ideas.Why relationship building happens outside the meeting room, not inside it.How to communicate technical work in a way executives immediately care about.Why every presentation should start with the business outcome, not the analysis.Why data professionals often answer questions that were never asked.What data leaders can learn from sales discovery.The questions every data professional should ask before proposing a solution.Why understanding failed attempts and hidden assumptions leads to better stakeholder conversations.How to influence when different executives have conflicting priorities.Why peer success stories influence behaviour more effectively than data alone.Why communication, influence and leadership should become measurable career progression criteria.Practical habits that help build influence, trust and credibility every day.Why AI will make communication, influence and judgement more valuable than technical expertise.Why every person needs to understand their specific role in delivering the wider business objective.Why every business strategy is built on assumptions that data should validate or challenge.How to align stakeholders when everyone interprets the same strategy differently.Thanks to our sponsor, Data & AI Literacy Academy.Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity.If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first.At Data & AI Literacy Academy, they don't just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They're bridging the gap between business and data teams, so they can all work towards aligned outcomes.From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme.They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve.Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com.
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  Data Debrief: Patios, Politics & the Perils of Meta Glasses 30.07.2026 48p

  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 returns from a two-week break to catch up with Kyle on the conversation with Joanne Riseborough, Group Data Management and Culture Director at Lloyd's Banking Group, alongside a wide-ranging catch-up covering AI ethics, the future of education, and why context is everything when it comes to new technology.They cover:Why Joanne's willingness to repeat the same simple truth throughout her episode, rather than apologise for it, was itself the standout takeaway, and why data management has long been the least glamorous but most under-resourced part of the data value chainHow the AI boom has quietly made data management one of its biggest beneficiaries, as businesses scramble to build the foundations needed to use their new tools effectivelyWhy a job title that puts "management" and "culture" side by side gets to the heart of why the two are inseparable, and can't succeed without one anotherCatching up on two episodes missed while on holiday: Priya's candid take on the CIO-CDO relationship, and the idea that failing to draw clear lines of accountability creates "competition for relevance" between the two roles, not collaborationA conversation with Vin on turning AI into revenue, the value of concrete company examples over abstract theory, and how his "information flywheel" concept has moved from a future prediction to something organisations are now actively chasingA detour into Meta's AI glasses, why the debate over them (cool or creepy?) is really about context and nuance, and why platforms like LinkedIn struggle to hold space for anything in betweenA tie-back to Catherine's own past research using facial recognition technology, and how the same tool can be a public safety asset in one context and deeply troubling in anotherWhether AI ethics has a genuine seat at the boardroom table, or whether "activity as the barometer of perception" makes leaders reluctant to be the one applying the brakesAndy Burnham's call for parity between university and vocational routes, what it might mean for how people enter data careers, and the idea (borrowed from a recent Taylor Culver conversation) that data is a skill rather than a career path in its own rightWhy the debate over LLMs in university coursework mirrors the harm-reduction argument in sex education: teaching safe, responsible use beats an outright banProgramming note and community shout-out: Catherine's back from a two-week camping-and-patio-laying hiatus (send help, or bricklaying tips), and registration for Driven by Data LIVE is officially open, with a growing list of bespoke, invite-only events also on the horizon. Make sure you're on the events list to hear about them first.This episode is a reminder that the unglamorous, unsexy fundamentals, data management, clear accountability, honest conversations about ethics, are usually what make the shinier stuff actually work.
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  S7 | Ep 17 | Data Management: The AI Boom's Biggest Winner with Joanne Riseborough, Group Data Management and Culture Director at Lloyds Banking Group 28.07.2026 41p

  In Episode 17 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Joanne Riseborough, Group Data Management and Culture Director at Lloyds Banking Group, where they discuss why the AI boom has quietly made data management one of the most strategically important disciplines in the boardroom.They explore why AI is shining a spotlight on data quality, governance and culture, how large enterprises balance modernisation with legacy technology, and why organisations that invest in strong data foundations will be best positioned to realise meaningful value from AI.They also discuss:Why AI has made data management more important than ever.Why data quality has moved back onto the boardroom agenda.How organisations should define what "good enough" actually means.What data quality should actually be measured against.How large enterprises balance modernisation whilst continuing to operate critical legacy platforms.The unique data management challenges created by hybrid cloud and on-premise environments.How data culture influences the success or failure of AI adoption.Why data and AI literacy is becoming a strategic capability rather than a technical nice-to-have.How Lloyds Banking Group is building capability through education, practitioner communities and leadership development.Why executive sponsorship remains one of the strongest predictors of successful AI transformation.How organisations should balance fixing today's problems whilst investing in tomorrow's capabilities.Why continuous monitoring and data observability will become fundamental components of modern data management.How reusable data products can accelerate both governance and AI adoption.Why organisations should think about both data for AI and AI for data.Why responsible AI starts with trusted data, lineage and governance rather than model oversight alone.How central strategy and standards can successfully coexist with federated ownership and delivery.Why organisations should assess their data maturity before accelerating AI ambitions.Why the strongest AI strategies are built on strong data foundations rather than stronger AI models.Thanks to our sponsor, Data & AI Literacy Academy.Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity.If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first.At Data & AI Literacy Academy, they don't just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They're bridging the gap between business and data teams, so they can all work towards aligned outcomes.From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme.They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve.Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com.