Driven by Data: The Podcast

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

This week, Catherine 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 chain
• How 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 effectively
• Why 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 another
• Catching 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 collaboration
• A 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 chasing
• A 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 between
• A 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 another
• Whether 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 brakes
• Andy 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 right
• Why the debate over LLMs in university coursework mirrors the harm-reduction argument in sex education: teaching safe, responsible use beats an outright ban

Programming 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.

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.

In Episode 16 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Priya Enefer, Chief Information Officer at Hakluyt & Company, where they discuss why the tension between CIOs and CDOs isn't a people problem, but an organisational design problem.

They explore how operating models, accountability, product thinking and executive alignment determine whether technology, data and AI become genuine competitive advantages or simply create duplication, politics and confusion.

They also discuss:

• Why the tension between CIOs and CDOs is usually created by organisational design rather than the people themselves.
• Why every CIO role looks different and how organisational context should define the mandate.
• Why organisations should define accountabilities before they hire executives or choose job titles.
• Whether every organisation actually needs a Chief Data Officer.
• Why technology, data and product leadership must operate as one team if transformation is going to succeed.
• The lessons learned moving from Chief Product Officer to CIO and why she still thinks like a product leader.
• How separate technology, product and data strategies create duplication, confusion and competing priorities.
• Why product operating models fundamentally outperform traditional project delivery.
• Who should own AI and why there is no universal answer.
• Why many organisations are measuring AI activity instead of AI value and repeating mistakes made during previous technology waves.
• How AI risks becoming another executive land grab unless organisations are crystal clear on ownership and accountability.
• Why centralised data teams can become ivory towers.
• What the private sector can learn from government about delivering successful transformation.
• Why dashboards and insights in isolation is not leadership.
• Whether technology, product and data leadership roles will ultimately converge or simply become much more interconnected.
• Why organisational design, incentives and culture will ultimately matter far more than whichever AI model an organisation chooses.

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.

In Episode 15 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was joined for the third time by Vin Vashishta, CEO and Founder of V-Squared, where they discuss why the organisations getting the most value from AI are focused less on models and use cases, and more on outcomes, information architecture and business transformation, which includes;

• Why AI strategy has become a revenue growth strategy rather than a technology strategy.
• Why AI is an information product that depends on context, information architecture and data.
• Why information flywheels will become the defining capability that separates AI leaders from everyone else.
• Why organisations are moving from buying AI products to forming outcome-based partnerships with technology and consulting providers.
• Why CEOs and CFOs are now demanding clear links between AI investment, business outcomes and shareholder value.
• Why meaningful AI ROI requires organisations to transform operating models rather than simply automate existing processes.
• Why the fastest-growing organisations are extracting the greatest value from AI by creating entirely new forms of value.
• Why organisations such as JPMorgan Chase and Eli Lilly are turning AI into sustainable competitive advantage.
• How organisations can begin building information flywheels.
• Why technical strategy is becoming a core capability for both executive leaders and technical practitioners as traditional management layers disappear.
• Why ownership of commercial outcomes matters far more than whether AI sits with the CIO, CDO or a Chief AI Officer.
• Why robotics, autonomous systems and edge AI could soon eclipse today's generative AI conversation.
• Why LLMs will become just one small component within far more sophisticated agentic systems.
• Why we'll see LLMs diminish in importance.

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.

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 Peter Crouch, Group Innovation Director at Lloyd's Banking Group, digging into what it really takes to build an innovation function that earns its keep, why "solving the right problem" beats "solving the problem right," and the discipline required to stay pragmatic about AI when the pressure to look busy is everywhere.

They cover:

• Why Peter's insistence that his team isn't a consultancy or a bolt-on, but something fully embedded in the business, matters for how any new function establishes its identity and avoids becoming just another side-of-desk activity
• The distinction Peter drew between solving the right problem and solving a problem right, and why so much technical effort gets poured into questions that were never worth asking in the first place
• Catherine's tie-in to a Rory Sutherland case study on managing customer perception, and why reframing expectations can matter more than actually speeding up a process
• The Porsche brakes analogy: why confidence and trust in the underlying systems, not raw speed or new tech, are what actually give people the courage to move fast
• Peter's candour about AI decisions ageing quickly given the pace of change, and why the psychological safety to kill a six-month project that isn't working is more valuable than seeing it through for the sake of appearances
• The idea of building repeatable, scalable capability for turning ideas into outcomes, rather than chasing the next isolated "big idea"

Kyle's thought of the week: prompted by Catherine, Kyle unpacks a pattern he's seeing across senior searches, talented specialists (using data governance as the example) who've risen to the very top of their track, out-earning some CDOs, only to find themselves boxed in with nowhere left to go. He explains why deep expertise in one domain rarely translates into credibility for a central, cross-value-chain leadership role, and why the people who make that jump early, often before they feel ready, tend to end up better positioned long-term. His advice: get genuinely clear on where you want to end up, be honest about whether your current track can get you there, and be willing to take a sideways or even backward step now if it sets up the bigger move later.

Catherine's thought of the week: inspired by Harry Kane losing his voice mid-interview, Catherine reflects on her own voice-loss moment hosting last year's Driven by Data Live, and makes the case for giving everything to your work when it counts, leaving it all out on the pitch without apology, while still knowing that pace isn't sustainable every single day.

Plus, a programming note and a community shout-out: Catherine is off for a short camping-holiday hiatus, so the show will pause for a couple of weeks, and the mentorship scheme's winter cohort is now open, get in touch to be paired with someone outside your usual industry and hear how genuinely non-linear most people's career paths really are.

This episode explores why real innovation isn't about chasing shiny new ideas, but about building the capability, confidence, and psychological safety to work on the right problems, know when to walk away from the wrong ones, and be intentional about where your own career is actually headed.

In Episode 14 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was joined by Peter Crouch, Group Innovation Director at Lloyds Banking Group, where they discuss why the organisations that thrive in the AI era won't necessarily be those with the best ideas, but those that build the strongest innovation capability, and how large enterprises can adopt startup thinking without compromising governance, risk or customer trust, which includes:

• Why innovation capability matters more than individual ideas.

• How solving the right problem is more important than solving the problem right.

• Why large organisations should adopt startup principles to innovate faster and reduce risk.

• How staged funding and rapid experimentation prevent costly investment in the wrong ideas.

• Why innovation requires a completely different operating model from traditional delivery.

• How regulated organisations can create space for experimentation without compromising governance or customer trust.

• Why embedding innovation into the business creates greater impact than isolated innovation teams.

• How portfolio thinking stops organisations falling in love with ideas too early.

• Why modern engineering platforms are essential for accelerating innovation.

• Why AI should always be driven by business outcomes rather than technology hype.

• How agentic AI is more likely to augment high-value work than replace skilled professionals.

• Why the biggest opportunity for AI in software engineering is removing friction rather than writing code.

• How replacing certainty with a learning-first mindset transforms innovation culture.

• Why treating failure as learning is essential to building innovative organisations.

• How creating intrapreneurs unlocks innovation at enterprise scale.

• Why proving value early is the key to scaling innovation successfully.

• How embedded, personalised financial services could redefine the future of banking.

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.

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 Diana Comsa, Global Director of Customer Data Products at Condé Nast, diving deeper into what it takes to reframe customer data as a growth engine rather than a marketing function, the value of professional friction in shaping better thinking, and the practical blueprint for translating technical output into commercial outcome.

They cover:

• Why Diana's framing of customer data as a growth engine, rather than something that sits under a marketing initiative, struck such a chord, and what that reframing means for how data teams position their value across a business
• Diana's account of learning to ask the right questions, shaped by mentors and managers who consistently challenged her, and why that kind of pushback, however uncomfortable in the moment, is often the biggest driver of professional growth
• The distinction between challenge and conflict: why psychological safety isn't about agreement, but about creating an environment where pushback is understood as people wanting the best outcome, not personal friction
• Catherine's take on choosing a boss over a company, why the person you report to, and the culture of professional friction they create, tends to shape a career more than a brand name ever will
• Why relationship-building remains one of the most underrated skills in the data industry: fundamentally, the job is about changing what people think, do, and believe, and trust is what makes that possible
• Kyle's reflection on remote culture and professional friction, why strong company culture doesn't require co-location, but does require deliberate investment in getting to know people at a personal level

Kyle's thought of the week: put on the spot by Catherine, Kyle lays out his blueprint for commercial articulation, the skill of anchoring data work to what a business actually cares about. He walks through the logic of tracing everything back to organisational goals and KPIs, then down through the decisions that influence them, before returning to his newspaper analogy: lead with the headline (the business outcome), not the small print (the technical how). Kyle unpacks the difference between an output (an improvement in data quality) and an outcome (what that improvement enabled for the business), and why board members and CFOs care almost exclusively about the latter. He also stresses that the narrative changes depending on the audience, a CIO, CFO, and CMO each need a different version of the same story. For anyone wanting to act on this today, his advice: ask your boss why you're doing what you're doing, build relationships with your CFO before you need them, and use tools like Claude to research a company's stated priorities from earnings calls and board updates.

Plus, a few community shout-outs: registration is open for Driven by Data Live in October, the magazine is in production ahead of launch at the event, and the team is still on the hunt for book club nominations — reach out via [email protected].

This episode explores why the technical work is only ever half the job — the ability to build trust, ask better questions, and translate output into outcome is what actually earns data leaders a seat at the table, and keeps them there.

In Episode 13 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was joined by Diana Comsa, Global Director of Customer Data Products at Conde Nast, where they discuss why customer data should be treated as a commercial growth engine rather than simply a marketing asset, and how solving the right customer problems unlocks long-term business value, which includes;

• Why the thread running through an unconventional career from strategy consulting to customer data has always been creating commercial value.
• Why understanding existing customers often creates more sustainable growth than simply acquiring new ones.
• How building a single customer view enables organisations to create deeper customer relationships and unlock new revenue opportunities.
• Why global organisations need consistency in customer identity, consent and architecture whilst empowering local teams to serve customers differently.
• Why defining business outcomes and success metrics before any work begins dramatically improves the chances of delivering value.
• Why customer data platforms should be designed around future business models rather than today's products and revenue streams.
• Why technology platforms and AI models are enablers, not the source of competitive advantage.
• Why AI strategy should always be an extension of business strategy and underpinned by strong data governance and quality.
• How AI is already helping organisations generate customer insight faster, improve reporting and increase engineering productivity.
• Why data monetisation isn't about selling data, but about increasing customer lifetime value through stronger customer relationships.
• Why the most successful customer data initiatives remain relentlessly focused on solving meaningful business problems rather than delivering technical outputs.

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.

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 Justin Borgman, co-founder, CEO, and chairman of Starburst, diving deeper into why AI adoption keeps stalling at scale, the real cost of pointing powerful tools at the wrong problems, and what it means to build differentiated business capability rather than just better infrastructure.

They cover:

• Why Justin's refreshingly candid starting premise — that messy, fragmented data is simply the reality most organisations are working in — cuts against the vendor instinct to promise a clean, unified solution, and why that honesty lands differently coming from a SaaS founder
• The recurring pattern of businesses spending two to three years consolidating data into a single source of truth, only to arrive at the same "so what?" question — and why Starburst's founding premise of using data where it lives challenges the orthodoxy of centralisation as a prerequisite for value
• The context problem that no platform solves on its own: how the same number pulled from the same source can mean two completely different things depending on interpretation, and why that ambiguity at enterprise scale can quietly corrode trust in data across an entire organisation
• Justin's observation on where AI is delivering the clearest, most demonstrable value right now — coding — and what that signals for how skill sets in software development, data science, and adjacent technical roles are likely to evolve faster than most organisations are prepared for
• The entry-level talent question neither businesses nor education systems have yet answered: as AI absorbs the work that once built foundational experience, where does the next generation of senior leaders come from, and who quality-assures the outputs of people who have never done the work themselves
• Catherine's take on AI as fire: extraordinarily useful when understood and controlled, capable of running out of control very quickly when deployed at enterprise scale through FOMO rather than focus — and why a CFO's instinct to shut it all down is an entirely predictable response to cost spirals
• Kyle's reflection on the speed problem at the heart of this AI cycle: unlike previous technological revolutions, where the pace of change gave industries time to adapt and reskill, this one is moving fast enough that many organisations and individuals haven't yet worked out what adaptation even looks like
• A moment from Catherine's farming background and the latest series of Clarkson's Farm that brings the AI transition into sharp relief — precision agricultural technology that looks futuristic to most farms but is closer than people think, and what the emotional weight of replacing a working horse with a tractor tells us about how humans really respond to transformation

Kyle's thought of the week: prompted by a pattern he's been tracking across executive search processes throughout 2026, Kyle reflects on a frustrating gap between capability and communication at the senior leadership level. The people not getting the roles aren't failing on technical grounds — they're losing out on energy and enthusiasm, inability to be concise, talking around questions rather than answering them, and failure to give specific examples. Kyle's concern is that these aren't just interview problems: they're signals of how someone will perform in front of a board or a CEO, and the skills that fix them can be self-taught and improved quickly. Catherine adds a practical tip for building confidence in high-pressure communication situations using AI tools like ChatGPT or Claude as a low-stakes rehearsal partner — and shares a striking example from a full studio broadcast that shows how dramatically even experienced communicators can disappear under pressure.

This episode explores why the data and AI industry's biggest bottleneck isn't the models — it's the foundations, the focus, and the people trusted to lead the work and make the case for it.

In Episode 12 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was joined by Justin Borgman, Co-Founder and CEO of Starburst, where they discuss why the biggest barrier to AI success is no longer about models.

The conversation explores why traditional approaches to data architecture are struggling in the AI era, how enterprises can overcome fragmented data estates, the importance of context and semantics, why many organisations remain stuck in pilot mode, rising AI costs, build versus buy decisions, agentic AI, and what the next three to five years of enterprise AI adoption are likely to look like, which includes;

• Why the vision of centralising all enterprise data into a single platform has never truly reflected reality.
• Why the AI industry's obsession with model selection is increasingly distracting organisations from the real challenges.
• How advances in foundation models are rapidly commoditising model performance and shifting attention elsewhere.
• What the true bottlenecks to AI adoption actually are.
• Where the clearest examples of AI delivering measurable value are today.
• Why many organisations remain trapped in POCs despite significant investment and executive attention.
• How the lack of context and semantic understanding continues to limit the effectiveness of AI in enterprise environments.
• Why trust, meaning and business context matter as much as access to data itself.
• Why AI success depends on; data foundations, analytics performance, enterprise context and trusted agentic interfaces.
• Why rising AI costs are becoming one of the biggest concerns for enterprise leaders and CFOs.
• Why data products are emerging as a practical solution for creating AI-ready context across the enterprise.
• Why separating context from physical data location creates more flexible and scalable architectures.
• Why executives are increasingly expecting answers rather than reports and dashboards.
• Why organisations should be building differentiated business capabilities rather than core platform infrastructure.
• How businesses that feel behind are often closer to the market than they realise.
• What the next three to five years could look like as AI becomes embedded into every major business function.

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.