Data Debrief: Halloween Special!

In Episode 32 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Chris Daniels, Data Officer for Corporate Banking, Compliance and Anti-Financial Crime at Deutsche Bank; Tom Sadler, Subject Matter Expert at HP; Kinnari Ladha, Chief Data Officer at Travelodge; and Daragh Kelly, Chief Data Officer at The Economist, for a live panel on the future of insights. They discuss why generating insight has become the easy part in an AI-enabled world, and why adoption, ownership and the human side of change are now the real constraints.The panel covers how intuition and evidence coexist, why the insights function is changing rather than disappearing, and why dashboards are multiplying rather than dying.They also discuss:Why Deutsche Bank’s appetite for predictive analytics is driven more by risk appetite than by capability.Why intuition and insight have to coexist in a business with deep operational experience, such as Travelodge.Why The Economist aims to be an impactful organisation rather than simply a data-driven one.Why corporate memory degrades as insight generation becomes distributed, and why an insight archive matters.How democratising insight creation builds ownership, so people trust what they have co-created.What traditional skills, such as forming testable hypotheses, difference-in-differences and propensity score matching, are being rediscovered in the AI era.Why the boring work of governance, training and data cleansing solves the vast majority of AI problems.Why most organisational problems are really communication problems between data, IT and the business.Why adoption can take 12 months even when a predictive model is built quickly.Why business change is the part of a data product that nobody owns, and how data leaders can take it on.How a co-built financial research agent for coverage bankers achieved fast adoption.Why a persona-based agent with callable skills builds trust where probabilistic answers are not good enough.Why dashboards are not dead, and how roughly ten dashboards are now built by others for every one built centrally at The Economist.How agents are being used to build, and then monitor, dashboards on people’s behalf.Why the future insights team focuses on existential, high-stakes questions and acts as a toolmaker for data foundations and semantic layers.Why clear data ownership and governance prevent a blame game when self-serve insights go wrong.Why insights should only be built where there is a known market, and why retiring old products is harder than building new ones.How AI fluency and data fluency can be brought together, and why giving people a safe space to experiment matters.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

Welcome to another episode of Data Debrief, the companion show to Driven By Data: The Podcast. Catherine Dowden-King and Kyle Winterbottom reflect on the first recording from Driven by Data Live, a panel with Barry Panayi (Chief Data Officer, Howden), Sarah Barr Miller (Chief Data Officer, British Airways), Sid Shah (VP of Data and International, Live Nation Entertainment) and Nina Bryant (Data, Analytics and AI Partner, Beringa).They cover:Why it has never been easier to stand out, even with AI-generated content everywhereHow identical CVs, presentations and LinkedIn posts are making candidates blur togetherWhy “lighthouse projects” and other LLM-isms give the game away in interviewsUsing AI for the heavy lifting, then putting your own stamp on the outputWhy physical marketing and in-house copywriters are making a comeback“It’s always you, but on your best day”: what professional authenticity looks likeHow a few minutes in Canva can set your presentation apartWhy preparation and basic etiquette still matter at the final stage of a processHow Driven by Data Live session one balanced foundational data work with funding transformationWhether AI is transformation, or simply another tool for continuing to evolvePlus, in this week’s Thoughts of the Week, Kyle makes the case that no senior data hire should happen without a practical exercise, arguing that these assessments test communication, influence and how candidates handle challenge, rather than the content of the slides. Catherine adds that cutting corners on preparation is a matter of professional courtesy as well as a missed chance to be memorable.Like and subscribe wherever you listen, and if you have a question or topic you would like the team to cover, email [email protected]

In Episode 31 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Barry Panayi, Chief Data Officer at Howden, Sarah Barr Miller, Chief Data Officer at British Airways, Sid Shah, VP Data, International at Live Nation Entertainment, and Nina Bryant, Data, Analytics & AI Partner at Baringa, recorded live at Driven by Data LIVE 2026, where they discuss how data leaders balance foundational work with transformation, and why foundations only get funded when they are tied to a clear business outcome.The panel covers how to prioritise foundations, how to deliver them through the use cases the business actually wants, and the trade-offs between speed, scale, control and innovation.They also discuss:Why foundational data work gets funded when it is delivered “by stealth” through the use cases the business wants.How Howden’s “bullseye” approach shows the commercial prize, prices what it takes to unblock it, and lets the business choose.Why data leaders need to define when foundations are “good enough” and move on to building new capabilities.What it means to give the board a number for how far foundational work is complete, and how acquisitions change the denominator.Why AI is finally giving data leaders a way to put an ROI around data foundations.How the gap between past AI returns and C-suite expectations of a 20% return in two years creates pressure on data teams.Why transformation becomes noise when the data function treats it as a mission in itself.How to separate AI, automation and process reinvention when the business simply asks for “AI”.Why speed buys the right to take time on the next piece of work, and how to be honest about what quick wins cannot yet do.How a low-risk, high-value use case can be fast-tracked while higher-risk decisions are considered carefully.Why communicating wins matters as much as delivering them, and how perception shapes how a data team is judged.How bottom-up AI use cases risk duplication, and how to scale the highest-value ones across the organisation.Why most organisations are not yet managing AI spend effectively, and what happens when the early-adopter discount ends.How embedding commercial insurance experts in the data team gives a business context layer.What the future holds for the Chief Data Officer role, and whether AI needs a separate leader.Why “a 1% error rate equals a 100% issue rate” in the age of AI, and how that has changed board conversations about data quality.What new board questions, such as sustainability and sovereignty, may come next.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

In this week's Data Debrief, Catherine Dowden-King & Kyle Winterbottom have a special Halloween episode! They discuss the tricks of the market, the horror stories that persist and the zombie challenges that won't seem to stay gone!

In Episode 30 of Season 7 of Driven by Data: The Podcast, Catherine Dowden-King is joined by Di Mayze, Chief Data and AI Officer at Marks & Spencer, where they discuss why the CDO role has to prove its value, why clean data foundations still come before AI, and how to build a data culture people want to be part of.As featured in Driven by Data Magazine 2026.As the first person in post at M&S, and previously the first global head of data and AI at WPP, Di shares what she learned at dunnhumby, Boots and WPP, why curiosity matters more than jargon, and how she is balancing innovation with strong data foundations.They also discuss:What dunnhumby and Tesco taught Di about putting data at the heart of every decision.Why she is comfortable saying “I don’t know”, and how that curiosity shapes her teams.Why great data leaders make a subject accessible rather than hiding behind acronyms.How to build confidence in asking “what do you mean?” when vendors say a product is AI powered.How Di built a 3,000-strong data and AI community at WPP through influence rather than power.Why trust, credibility and championing your team’s work matter more than formal authority.Why Di believes there is a direct correlation between joy and productivity.Why the CDO role was in trouble, and why CEOs wanted proof of its value.Why Di wants her team to keep automating its own work and moving into new areas.How to avoid being distracted by shiny new tools while still tracking the market.What makes a data-and-AI mix of foundations and quick wins work when leaders need to show momentum.Why putting AI on bad data or bad processes is a disaster.How M&S is using Copilot licences and employee-built agents to lift individual productivity.How the Sparks loyalty scheme balances personalisation with trust.Why a data culture can’t just mean more reports, and how to motivate action instead.What responsible AI looks like in practice: explainability, and humans before, in and after the loop.Why asking the same question several ways can reveal whether a model is just trying to please you.What a story about an AI deleting people from a gym queue shows about unintended consequences.Why measuring impact is the career advice Di would give her younger self.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/

Welcome to another episode of Data Debrief, the companion show to Driven by Data: The Podcast, hosted by Catherine Dowden-King and Kyle Winterbottom.This week, Catherine reports back from the Marketing Showcase in Bristol, her first event as a pure attendee in a long time. The talk was all data, analytics and AI. She and Kyle explore why marketing and data share the same struggle to show their impact. They also cover how B2B brands can stand out: know exactly who you’re speaking to, be authentic, and don’t jump on every bandwagon.They then turn to a viral LinkedIn post from a Meta software engineer who says he has never felt more uncomfortable in his job. He is shipping AI-generated code he doesn’t fully understand. Is this whistleblowing turning into red-flag waving, and where should the line sit?The conversation moves to Lee Fulmer’s recent Driven by Data episode. He is Head of Data at Canadian Life Reinsurance. They cover:Why clean data alone isn’t enough for AI, and why context is what separates winners from losers.What businesses lose when people leave, and the cultural “lore” that will never make it into a prompt.Why FOMO is not a strategy.Servant leadership, and why “everything else is waste” is a useful way to frame your role.Why businesses that hire for outcomes get outcomes, and what that means for your CV.They also share news of the final Orbition community event of the year in mid-November, and mentorship activity in late November.Listen to Lee’s full episode on Driven by Data: The Podcast.

In Episode 29 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Lee Fulmer, Head of Data at Canada Life Reinsurance, where they discuss why clean, well-governed data still isn’t AI-ready, and why context, a clear business outcome and curation matter more than any blueprint.Canada Life Reinsurance reported around a billion dollars in profit last year from roughly fifty deals, with a workforce of around 400 people in which almost everyone is an actuary. Lee reports to the CEO as the first non-actuary the business has hired, which makes it a high-stakes setting for questions about data, AI and accountability. He also explains why the “AI will replace the data team” belief has faded, how cost and the general ledger are forcing a rethink, and why agentic AI is not the right answer for most businesses yet.They also discuss:Why a data leader in a business with billion-dollar profits on a handful of deals can’t afford to treat data as raw numbers alone.Why the belief that AI makes the data team redundant took hold, and why it has unwound.How the shift to small language models and proprietary training data is putting data back at the centre.Why companies are hiring heads and directors of data rather than chasing the Chief Data Officer title.What matters more than a job title: responsibility, reporting line and empowerment.Why high-quality, well-governed data is not the same as being AI ready.What data governance has missed: the context behind a piece of information, not just its accuracy.How actuaries and reinsurance show the difference between raw data and the context that gives it meaning.Why context, not content, is king, and what the knowledge management craze of the 1990s teaches us.Why there is no universal blueprint for AI readiness, and why every organisation is a unique case.How organisational behaviour and change management matter more than technical skills in data transformation.Why this wave of AI is different: it is accessible to everybody, not just a few million specialists.How a generation that has grown up digital is approaching AI differently from older colleagues.How the general ledger is forcing leaders to question spiralling AI and data platform costs.Why many businesses have more AI pilots than they can measure, and what the RPA era should have taught them.Why the cost of compute is rising, and why success measures must be defined before AI is deployed.Why agentic AI risks repeating the RPA story in organisations with weak IT foundations.How fear of missing out drives AI decisions, and why starting with the outcome gives the right answer.What leaders should do first if their data sits in a data lake: curate it around the outcome, and keep the context.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/

In Episode 28 of Season 7 of Data Debrief, Catherine Dowden-King and Kyle Winterbottom look back on a packed Driven by Data Live and the lessons from its roundtables. They then turn to what a reported attack on a Russian data centre might mean for data leaders, and debrief their conversation with German Faraoni, Global Director of Shared Services at Reckitt, on why data teams need to be in the right room.In this episode:Why Driven by Data Live was oversubscribed, and why roughly 45% of attendees being new to the events is good for the community.How Chatham House round tables help people share candidly, and why authenticity matters more than ever in an AI world.An HR agent use case: an anonymous assistant on top of an HR catalogue, with guardrails that signpost support and flag sensitive questions to the HR team.Why internal, people-led AI use cases are likely to come before customer-facing ones, and how they build confidence.How something as simple as understanding your maternity leave policy can influence employee loyalty, especially if non-competes are challenged in the UK.What the reported disruption to a Russian data centre, and the apparent drop in bot and crawler activity, suggests about how much online engagement is real. Both hosts are careful to flag that the reporting is still developing.Why geopolitical stability and data sovereignty are likely to be on every event agenda next year.How AI is giving data management, governance and quality, as well as cybersecurity, a new lease of life.How CISOs balance battening down the hatches against smothering the business, and what data leaders can learn from them.Why data leaders need to work closely with PR and understand their customers’ values, since decisions about where data lives could cost customers.Why doing business is more complex than ever, and what boards are weighing up on AI.A debrief of the interview with [Guest name], including the “if you’re in this room, you’re in the wrong room” idea.How a SKU-based use case showed the value of sitting with the executive team and working back to a use case that moves the P&L.Why no one will invite you into the room, and how to build the trust and credibility to get there.Why data teams working in isolation risk building things that go nowhere.How to write a CV for two audiences, those screening for keywords and those looking for outcomes, and why hiring managers say AI-written CVs now look the same.Community shout: the final event of the year, the quarterly roundtable, takes place on 17th November. Find the details at orbitiongroup.com.

In Episode 28 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by German Faraoni, Global Director of Shared Services for Data at Reckitt, where they discuss why data and AI investment still struggles to show measurable value, and why the answer lies in articulating it in business outcomes rather than data terms.German also shares how Reckitt cut the time to get a new SKU onto shelves by almost half, and how data leaders can earn their place in the right business conversations.They also discuss:Why data leaders are business professionals first and data specialists second.Why investment in data and AI is still articulated in data terms rather than business terms.How the same trap is repeating with AI, and what “hype credit” means for justifying investment.Why the metrics for data and AI should be the metrics the business already uses to measure success.What a data leader should ask a prospective employer before taking a role.How Reckitt reduced the time to commercialise an SKU from around 200 days to roughly half.Why the SKU release project started with process redesign rather than technology.How Gen AI emerged as the right tool for the job rather than the starting point.Why data practitioners must sit in the business conversations where results are discussed.Why starting small and avoiding “boiling the ocean” matters for scalability.How governance councils by data domain create a sustainable foundation for data quality.Why remediating legacy data issues remains hard, and how to choose which ones to fix.How AI lowers the cost of intelligence and speeds up the decisions that follow insight.What using AI as a “canary in the mine” reveals about weaknesses in your data and access controls.How to approach AI across internal efficiency, reimagining the existing business and creating new business models.Why agentic commerce could reshape how consumers and retailers interact.What AI means for the future of the CDO role, and who may be most exposed to change.Why competitive advantage will come from the right mix of premium and commodity AI, process understanding and orchestration.How large incumbents respond to disruption from smaller, faster AI-enabled businesses.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/

In this episode of the Data Debrief, Catherine Dowden-King and Kyle Winterbottom reflect on the week's news and the latest Driven by Data: The Podcast interview, with Tom Sadler of HP. Their central question is whether organisations are measuring AI activity (headcount, spend, job titles, tooling) instead of the productivity and financial outcomes that matter.They begin with the ASOS breach and the questions it raises about vendor risk. They then work through the latest Evident AI Index and what it rewards, before turning to Tom's candid views on hardware, cost and culture.They also discuss:Why the ASOS ransom note, delivered through the company's own app, marks a new and uncomfortable form of attack.How data leaders should work in lockstep with the CISO, or with the CIO where there is no CISO, as vendor ecosystems grow.Why the full story of a breach like this can take months to emerge, and why speculation helps no one.Why only 12% of reported AI use cases in the Evident AI Index demonstrate operational impact, and just 1% report financial impact.How the index's weightings (45% talent, 30% innovation, 10% transparency) shape what it rewards.Why organisations with the strongest financial returns from AI may not appear on the index at all.What the "too early to judge" argument gets right, and why 1,100 use cases over five years still demands better measurement.Why leaders chasing a higher ranking risk spending more money for the same 1% return.How adding "AI" to a job title, without mandate or budget, repeats the chief data officer pattern.Why the market is starting to reward titles over delivery when it hires.Why Tom Sadler argues hardware should be the last thing you think about in an AI strategy.How local compute can cut experimentation costs compared with spiralling cloud token costs.What running AI locally means for environmental impact.Why the CDO and CIO sometimes have never met, and how that drives sprawling costs and shadow AI.How a confined local "sandbox" lets teams experiment safely, with successful ideas scaled across the business.Why culture, not tooling, decides whether you can spot what has worked, and why a use case that cuts five hours of work to 30 minutes needs a place to be shared.What it takes to build the honesty to review mistakes regularly and correct them.Why a chief financial officer's advice to start running in your old trainers applies to AI spend.