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.

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 David Krauza, VP of Enterprise Data Strategy, Products & Governance at Comcast, diving deeper into the "arts and crafts" trap that derails data programmes, the discipline of building stakeholder trust before you need it, and what it really takes to drive the bus rather than ride it.

They cover:

• Why David's mandated business school module ended up shaping his outlook on data leadership, and the recurring pattern of guests whose commercial thinking was forged outside a purely technical background
• The "arts and crafts project" analogy David's boss used to describe technically impressive work that never moves the needle, and why naming the difference between process-enjoyment and outcome-focus matters as much in data as it does in any creative pursuit
• Why so much of the data and AI ecosystem gravitates toward exciting new models, tools, and techniques without tying the work back to specific goals, decisions, and KPIs
• David's framing that you have to help people before you need their help, and why the leaders who consistently land the biggest roles are the ones already putting into their networks and communities long before they need anything back
• Catherine's take on why this same principle defines external brand building, and why leaders who wait until they're job hunting to invest in relationships are always playing catch-up against those who started years earlier
• Kyle's view that building relationships with future stakeholders is not a side project for a data leader, it is the job, every bit as much as overseeing platform delivery, governance, and architecture
• Why David reframed the trust gap many data leaders face as a sequencing problem rather than a communication problem, and what that distinction means for how and when leaders should be reaching out
• The "bus riders and bus drivers" analogy at the heart of the episode title, and why organisations hire a data leader precisely because they don't already know the answer, making it the leader's job to shape direction rather than simply execute instructions
• Why being a strong, detailed communicator changes the entire dynamic of a hiring conversation, and how that same skill plays out with stakeholders once someone is in the role
• Catherine's practical tip for building interview and communication confidence using AI tools like ChatGPT or Claude as a low-stakes practice partner, and why consistent repetition beats waiting for natural talent to show up

Kyle's thought of the week: prompted by a message from a CDO at a crossroads in their career, Kyle reflects on why the CDO role isn't disappearing or resurging industry-wide so much as it's becoming entirely dependent on whether a business's leadership views data as a commercial value-creation function or a technology delivery capability. Where it's the latter, that responsibility increasingly sits with the CIO, and Kyle notes the early signs of broader transformation-style mandates emerging that fold CDO, CIO, and Chief AI Officer responsibilities into a single board-level role.

This episode explores what it actually takes to drive value rather than just deliver outputs, the discipline of investing in relationships long before you need them, and why naming the gap between busywork and real impact is often the first step to closing it.

In Episode 11 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was joined by David Krauza, VP of Enterprise Data Strategy, Products & Governance at Comcast, where they discuss why strategic clarity and proactive stakeholder engagement are the keys to unlocking genuine business value from data and AI, which includes;

• Why the root cause of failed AI and data programmes is almost never the technology and almost always the absence of a clear business outcome.
• How to tell the difference between an organisation that has genuine strategic clarity and one that just has a compelling PowerPoint.
• Why a strategy without explicit trade-offs, knowing what you are not going to do, is no strategy at all.
• How "arts and crafts" projects quietly drain data programmes of focus, credibility, and commercial impact.
• Why retrofitting goals around work already underway creates a circular dependency that pulls organisations further from real value.
• Why the "bus riders and bus drivers" framework reframes what it means to be an effective data leader.
• Why waiting for perfect conditions before driving impact is one of the most common and costly habits of data leaders.
• How proactively building relationships with CFOs, COOs, and business unit heads before you need them is what separates influence from scrambling.
• Why the trust deficit most data leaders face is a sequencing problem, not a communication problem.
• How starting within your own team or with a single friendly stakeholder is the most practical way to begin building the bus driver muscle.
• Why most CDO mandates are structurally designed to deliver outputs rather than value and how that shapes the type of leader organisations end up hiring.
• How to navigate a broken mandate in practice and why challenging it in the interview room is riskier than it sounds.
• Why the incentive structures within data leadership roles have historically rewarded technical delivery over commercial impact.
• Why the data industry's technical origins created an archetype that is now working against the commercial value organisations actually need.
• How company size and culture determine whether data is treated as a strategic asset or an internal IT service and why that changes everything.
• Why organisations that started their data journey for the wrong reasons often find the perception too deeply embedded to shift from within.

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 Sarah Emerson, Group Director of Insight & Business Partnering at Howden, diving deeper into the growing importance of commercial thinking, business partnering, and the role relationships play in driving value from data.

They cover:

• Why Sarah's background in finance and corporate strategy offers a unique perspective on data leadership, and how commercial acumen can become a powerful differentiator for leaders looking to influence organisational outcomes
• The challenge of connecting data strategy to business strategy, why many organisations struggle to articulate strategic priorities clearly, and the practical ways data leaders can uncover them regardless
• Why curiosity about business value shouldn't be reserved for senior leaders, and how analysts at every level can develop a stronger understanding of commercial impact
• The growing importance of business partnering as a dedicated capability, and how organisations can bridge the gap between technical teams and business stakeholders more effectively
• The realities of operating model design, why federated approaches continue to gain traction, and the trade-offs organisations must consider when balancing proximity to the business with cost and complexity
• Sarah's view that self-service analytics has largely failed to deliver on its original promise, and what that means for the future of data enablement and adoption
• Why understanding how business leaders are measured, incentivised, and rewarded can dramatically improve stakeholder engagement and increase adoption of data-led initiatives
• The challenges of discussing performance, incentives, and accountability within organisations, and why trust and relationship-building remain critical leadership skills
• The evolving role of the Chief Data Officer, the increasing consolidation of data responsibilities back into CIO organisations, and what this shift could mean for the future of data leadership
• How AI has accelerated organisational debates around ownership, accountability, and transformation, with many businesses still determining where responsibility ultimately sits
• The emergence of broader transformation and innovation leadership roles that combine data, technology, AI, digital, and business transformation under a single mandate

Kyle's thought of the week: as more organisations place data leadership responsibilities back under the CIO, many of the lessons learned throughout the evolution of the CDO role risk being forgotten. The challenge now is ensuring that value creation, business engagement, and commercial impact remain at the centre of the agenda, regardless of where accountability sits.

This episode explores the realities of commercial leadership in data, the importance of business partnering, and why understanding people, incentives, and organisational dynamics is often just as important as understanding data itself.

In Episode 10 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was joined by Sarah Emerson, Group Director of Insight & Business Partnering at Howden, where they discuss why commercial thinking and business partnering are the keys to uncovering business strategy and making data genuinely matter, which includes;

• Why having a non-technical background can give you a competitive edge as a data leader.
• How to infer business strategy when corporate goals are unclear or poorly defined.
• Why Sarah believes commercial KPIs should always take priority over internal data metrics.
• How finding out what a stakeholder is bonused on is the fastest route to their engagement.
• Why federated operating models consistently deliver more commercial value — and why they're expensive.
• Why self-serve analytics will never truly scale, particularly in relationship-driven industries like insurance.
• How conversational AI is poised to succeed where a decade of self-serve dashboards has failed.
• Why inconsistent data definitions across divisions remain the biggest hidden barrier to AI adoption.
• The importance of executive sponsorship in driving data culture — and what to do when you don't have it.
• How financially incentivising sales teams based on data adoption could become the industry's next big shift.
• Why embedded business partners bridge the gap between analytical output and real commercial impact.
• How to uncover business strategy when no one will give you the time or the meeting.
• Why defaulting to technical language is one of the fastest ways a data leader can lose the room.
• Why the business, not just the data team, needs to take accountability for driving data product adoption.
• How embedding analysts within business teams is what drives genuine commercial impact.

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.