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Driven by Data: The Podcast: Daily Summaries Delivered
by Orbition Group
Orbition Group is delighted to bring you this podcast series, which is designed for Data Enthusiasts, to hear from some of the most high-profile Data, Analytics and AI thought leaders from around the globe.Each episode will detail the guests journey to the top while bringing unique insights, drawn from first-hand experience on the industry’s most trending topics. This podcast was created as a way for our industry's most respected leadership figures from across the world to give back to the Data & Analytics community, by sharing; knowledge, experiences and ideas, to inspire, innovate and provide real-life use cases on the industries most pressing topics/challenges.
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- Today47 min
S7 | Ep 25 | The Autonomic Business: Why AI Is a Leveller, Not an Advantage with Simon Turner, Chief Technology Officer at FOIL AI
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.
- 5 days ago40 min
Data Debrief: We get Political
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.
- 1 weeks ago58 min
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
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 bigge
- 1 weeks ago48 min
Data Debrief: Schools Back, AI Attack & the future of paid employment
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.
- 2 weeks ago50 min
S7 | Ep 23 | Redesigning Organisations for AI-Driven Competitive Advantage with Greg Freeman, CEO & Founder, Data & AI Literacy Academy
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.
- 2 weeks ago39 min
Data Debrief: Creepy AI Teddy, Running App Renters & The beginning of the Real AI Use cases
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.
- 3 weeks ago51 min
S7 | Ep 22 | Getting AI out the Sandbox and into Production with Chris Pearce, Chief Data & AI Officer, Ageas
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
- 3 weeks ago36 min
Data Debrief: Blame AI! Averages Lie! and Kyle's throat sounds like he's going to... Cough.
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.
About Driven by Data: The Podcast
Orbition Group is delighted to bring you this podcast series, which is designed for Data Enthusiasts, to hear from some of the most high-profile Data, Analytics and AI thought leaders from around the globe.Each episode will detail the guests journey to the top while bringing unique insights, drawn from first-hand experience on the industry’s most trending topics. This podcast was created as a way for our industry's most respected leadership figures from across the world to give back to the Data & Analytics community, by sharing; knowledge, experiences and ideas, to inspire, innovate and provide real-life use cases on the industries most pressing topics/challenges.
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