Join us for an unmissable evening of insight, discussion, and lively debate at The High Performance Data and AI Debate, hosted by Chris Tabb — a unique Big Data London special running from 6:00–8:00 PM. This fast-paced, interactive event brings together some of the brightest minds in data and AI to tackle the most pressing questions shaping the future of teams, architecture, and products in an AI-first world.

The evening kicks off at 6:00 PM with a welcome and free drinks. Then, across three rapid-fire 20-minute debates, our expert panels will explore:

• AI & Data – Teams (Chair: Eevamaija Virtanen)

Mehdi Ouazza, Paul Rankin, Jesse Anderson, Hugo Lu

• AI & Data – Architecture (Chair: Adi Polak)

Chris Freestone, David Richardson, Nick White, Karl Ivo Sokolov

• AI & Data – Products (Chair: Jai Parmar)

Kelsey Hammock, Jean-Georges (jgp) Perrin, Taylor McGrath, Jon Cooke

Refuel with free pizza at 6:50 PM, then stay for the Town Hall Debate, where all speakers return to the stage for an open-floor Q&A — your chance to challenge their ideas, share perspectives, and shape the conversation.

Expect fresh perspectives, healthy disagreement, and practical takeaways you can bring back to your organisation. Whether you’re leading a data team, designing cutting-edge architectures, or building AI-powered products, this is your space to engage with the people shaping what’s next.

The path to AI enablement runs through governance. High-quality data, model transparency, and ethical oversight aren’t barriers — they are accelerators. In this talk, we’ll connect the dots between Data Governance and AI Governance, show how unified governance, helps embed new requirements to existing processes, while fostering innovation. We will discuss actionable steps to build AI-ready organisations that innovate with proper guardrails.

As AI adoption accelerates across industries, many organisations are realising that building a model is only the beginning. Real-world deployment of AI demands robust infrastructure, clean and connected data, and secure, scalable MLOps pipelines. In this panel, experts from across the AI ecosystem share lessons from the frontlines of operationalising AI at scale.

We’ll dig into the tough questions:

• What are the biggest blockers to AI adoption in large enterprises — and how can we overcome them?

• Why does bad data still derail even the most advanced models, and how can we fix the data quality gap?

• Where does synthetic data fit into real-world AI pipelines — and how do we define “real” data?

• Is Agentic AI the next evolution, or just noise — and how should MLOps prepare?

• What does a modern, secure AI stack look like when using external partners and APIs?

Expect sharp perspectives on data integration, model lifecycle management, and the cyber-physical infrastructure needed to make AI more than just a POC.

In this, the 10th year of Big Data LDN, in its flagship Great Dat Debate keynote panel, conference chair and leading industry analyst Mike Ferguson welcomes executives from leading software vendors to discuss key topics in data management and analytics. Panellists will debate the challenges and success factors in building an agentic enterprise, the importance of unified data and AI governance, the implications of key industry trends in data management, how best to deal with real-world customer challenges, how to build a modern data and analytics (D&A) architecture, and issues on-the-horizon that companies should be planning for today.

Attendees will learn best practices for data and analytics implementation in a modern data and AI -driven enterprise from seasoned executives and an experienced industry analyst in a packed, unscripted, candid discussion.

The rapid evolution of AI, fueled by powerful Large Language Models (LLMs) and autonomous agents, is reshaping how we build, deploy, and manage AI systems. This presentation explores the critical intersection of MLOps and AI architecture, highlighting the paradigm shifts required to integrate LLMs and agents into production. We will address key architectural challenges, including scalability, observability, and security, while examining emerging MLOps practices such as robust data pipelines, model monitoring, and continuous optimization. Attendees will gain practical insights and actionable strategies to navigate the complexities of modern AI deployments, unlocking the full potential of LLMs and agents while ensuring operational excellence.

As AI evolves with powerful Large Language Models (LLMs) and autonomous agents, deploying and managing these systems requires new approaches. This presentation explores the crucial intersection of MLOps and AI architecture, highlighting the shift toward scalable, observable, and secure AI deployments. We’ll examine key architectural considerations for integrating LLMs and agents into production, alongside evolving MLOps practices such as robust data pipelines, model monitoring, and continuous optimization.

As AI agents become embedded in everyday workflows — from healthcare diagnostics to financial services chatbots — the line between human and machine continues to blur. This panel brings together industry leaders to tackle the tough questions:

• How do we trust AI agents in high-risk environments?

• What are the new rules of ownership and accountability when autonomous systems act on data?

• Is AI replacing or enhancing the human workforce — and how do we keep the balance right?

We'll unpack how AI agents are evolving across sectors, debate whether the current LLM paradigm is enough, and explore the new guardrails needed to futureproof agentic AI — without losing control.

Forecasting is no longer just about historical trends and spreadsheets. AI is redefining how organisations anticipate demand, manage risk and make faster, smarter decisions.

In this expert-led panel of Women in Data® senior leaders from esure, Domestic & General and Databricks, moderated by a leading voice from NTT DATA, we will explore how AI-enabled forecasting is transforming planning across industries. They will take a candid look at the current landscape, how to realign goals and priorities and how to forge a business that is dynamic, data-rich and future-ready.

Powered by: Women in Data®

Behind every AI success story is a quiet set of foundations—governance, trust, and purpose-driven decisions—that most teams overlook until it’s too late. This talk reveals what the smartest companies quietly get right—and what everyone else learns the hard way. It also explores the complex, unsolved challenges on the horizon—the ones we haven’t fully mapped, but can’t afford to ignore. 

In AI, speed without the proper foundations isn’t a strategy—it’s a liability.

In a world where AI agents, complex workflows, and accelerating data demands are reshaping every enterprise, the challenge isn’t just managing data, it’s creating trusted context that connects people, processes, and technology. 

Join Rebecca O’Kill, Chief Data & Analytics Officer at Axis Capital, for an Honest No-BS conversation about how her team is transforming governance from a compliance checkbox into a strategic enabler of business value. 

Together, we’ll unpack: 

• Minimal Valuable Governance (MVG): why the old ivory tower “govern everything” mindset fails, and how focusing on just enough governance creates immediate business impact. 

• The ACTIVE framework, a practical approach for governance built on: Alignment, Clarity, Trust, Iterative, Value, Enablement 

• How Axis Capital is embedding governance across the organization by uniting the “front office” (what and why) with the “back office” (how). 

• Why context and knowledge are critical for the next era of agentic AI and multi-agent workflows, and how Axis is preparing for it today. 

By the end, you’ll see how Axis Capital is turning governance into a competitive advantage and why this approach is essential for any organization looking to thrive in a world of AI-driven automation and connected workflows. 

Enterprises are eager to realise the promise of AI—from insights to decisions to automation. The business is ready to innovate, but fragmented data, rigid AI governance, and the cost and complexity of AI innovation just create frustration. 

This session explores a bold idea: the business—not IT—will lead AI innovation. Success depends on enabling teams to ideate freely, experiment rapidly, and unlock new value at market pace. We’ll discuss how a modern data product strategy empowers business teams with access, trust, and agility—so they can test ideas quickly and scale what works.

You’ll learn why experimentation is the new strategic advantage—and how to turn business curiosity into competitive impact.

For years, bol.com relied on an internally built event tracking and measurement system. But as data demands scaled, so did operational risk, technical debt, and governance challenges. 

In this session, learn how bol.com made the strategic shift to RudderStack, replacing their fragile DIY stack with a reliable, scalable, and developer-first event pipeline. You'll hear how they implemented RudderStack across four major platforms, layered in tracking plan enforcement and schema governance, and integrated with their existing batch pipelines.

The results? Faster instrumentation, near-zero tracking violations, higher data reliability, and improved developer experience, all while reducing costs. Whether you're operating at scale or looking to future-proof your architecture, this session offers a blueprint for replacing legacy systems with modern, warehouse-native infrastructure that doesn't compromise on flexibility, governance, or trust.

The data engineer’s role is shifting in the AI era. With LLMs and agents as new consumers, the challenge moves from SQL and schemas to semantics, context engineering, and making databases LLM-friendly. This session explores how data engineers can design semantic layers, document relationships, and expose data through MCPs and AI interfaces. We’ll highlight new skills required, illustrate pipelines that combine offline and online LLM processing, and show how data can serve business users, developers, and AI agents alike.

Migrating your BI platform sounds daunting — especially when you’re staring down hundreds of dashboards, years of legacy content, and a hard deadline. At Game Lounge, we made the leap from Looker to Omni, migrating over 800 dashboards in under three months — without disrupting the business.

In this session, we’ll walk through the practical playbook behind our successful migration: how we scoped the project, prioritised what mattered most, and moved quickly without compromising quality. We’ll share how we phased the migration, reduced dashboard sprawl by over 80%, and leaned on Omni’s AI-assisted features to accelerate setup and streamline cleanup.

We’ll also touch on how we kept quality high post-migration — introducing initiatives like dashboard verification to ensure lasting data trust. And we’ll share what happened next, with over 140 employees now using data to inform decisions every day.

Whether you’re planninga migration or trying to make sense of legacy BI sprawl, this session offers honest lessons, practical frameworks, and time-saving tips to help your team move fast and build smarter.

Last year, Big Data London’s GenAI theatres were packed. Fast forward 12 months, and AI is everywhere. So, this AI lark is easy now… right?  

Lifting the lid on the AI bubble, reality is starting to bite. AI initiatives are stalling, models are drifting, and demonstrating tangible business value is really hard. Why? Because we’ve all sprinted into the AI future without first packing the essentials: high-quality, trusted data; a shared language for decision-making; solid governance; and the skilled people to make it all work.  

In 2025, the organisations that will see the best returns from their AI programs are those that have gone back to the future by pressing rewind to get their data foundations right before scaling the shiny stuff.  

Join Andy Crossley, CTO at Oakland, alongside Alex Pearce, Chief Microsoft Strategist at Softcat, for a no-holds-barred conversation about the realities of AI in practice.  

Lifting the lid on:  

Why so many AI projects fail to deliver real value  

The critical data foundations every business needs to succeed  

Real-world lessons from organisations discovering that AI is far more complex than the hype suggests  

The good news? You’ll leave with practical, actionable steps to start unlocking value from your AI investments.  

We can’t promise all the answers, but this session will reassure you that you are not alone. We aim to inspire new thinking and provide the guidance you need to navigate the most common pitfalls on the path to making AI work for you. 

As the pioneers of the low-code market since 2001, enterprise software delivery solution OutSystems has evolved rapidly alongside the changing landscape of data. With a global presence and a vast community of over 750,000 members, OutSystems continues to leverage innovative tools, including data observability and generative AI, to help their customers succeed.

In this session, Pedro Sá Martins, Head of Data Engineering, will share the evolution of OutSystems’ data landscape, including how OutSystems has partnered with Snowflake, Fivetran and Monte Carlo to address their modern data challenges. He’ll share best practices for implementing scalable data quality programs to drive innovative technologies, as well as what’s on the data horizon for the OutSystems team.

AI agents need seamless access to enterprise data to deliver real value. DataHub's new MCP server creates the universal bridge that connects any AI agent to your entire data infrastructure through a single interface.

This session demonstrates how organizations are breaking down data silos by enabling AI agents to intelligently discover and interact with data across Snowflake, Databricks, BigQuery, and other platforms. See live examples of AI-powered data discovery, real-time incident response, and automated impact analysis.

Learn how forward-thinking data leaders are positioning their organizations at the center of the AI revolution by implementing universal data access strategies that scale across their entire ecosystem.

Practicing analytics well takes more than just tools and tech. It requires data modeling practices that unify and empower all teams within analytics, from engineers to analysts. This is especially true as AI becomes a part of analytics. Without a governed data model that provides consistent data interpretation, AI tools are left to guess. Join panelists Joe Reis, Sarah Levy, Harry Gollop, Rob Hulme, Shachar Meir, and Guy Fighel, as they share battle-tested advice on overcoming conflicting definitions and accurately mapping business intent to data, reports and dashboards at scale. This panel is for data & analytics engineers seeking a clear framework to capture business logic across layers, and for data leaders focused on building a reliable foundation for Gen AI.

A paradigm shift is underway; the primary consumer of data is evolving from human analysts to AI agents. This presents a strategic challenge to every data leader: how do we architect an ecosystem that satisfies relentless, machine-scale demand for governed data without overwhelming our most valuable human experts? A chaotic free-for-all, with AI agents querying sources directly, is a regression that would erase a decade of progress in data warehousing and governance.

To solve this machine-scale problem, we must deploy a machine-scale solution. This session casts a vision for the future, exploring why current models are ill-equipped for the AI era. We will introduce the concept of the virtual data engineer—an AI-powered partner designed to augment and accelerate human capabilities on a collaborative platform. Discover how to evolve your team and architecture to turn this challenge into a strategic advantage, ensuring you lead the way through this transformation.

The entertainment industry is sitting on a huge natural resource: decades of creativity and craftsmanship from talented professionals. Koobrik is an advanced language model designed by, and for, the creative industries. As a Warner Brothers’ accelerator company, the model is already utilised by HBO, A24, DC Comics and many more, to harness ethical artificial intelligence.

Join Koobriks’ CEO and Founder, Orlando Wood, as he shares insights into:

- Building an ethical AI model for the entertainment industry

- The unique challenges of creative data as an asset class

- The AWS and Databricks tech stack powering Koobrik

- Real-world applications, from comic books to screenplays

Maximize the value of your SAP investments by harnessing the power of Data and AI on Azure. In this session, you’ll learn proven strategies to leverage SAP landscapes, unify data across SAP and non-SAP systems, and unlock advanced analytics with Azure’s Agentic AI capabilities. We’ll showcase industry-ready accelerators that accelerate transformation, enable governed data access, and turn insights into action to fuel innovation, agility, and measurable business outcomes.

The most sought-after products don’t just appear on shelves—they arrive at the perfect moment, in perfect condition, thanks to data that works as fast as the business moves.

From premium meats to peak-season produce, Morrisons, one of the UK’s largest retailers, is building a future where shelves are stocked with exactly what customers want, when they want it.

In this session, Peter Laflin, Chief Data Officer at Morrisons, joins Striim to share how real-time data streaming into Google Cloud enables smarter, faster, and more autonomous retail operations. He’ll unpack how Morrisons is moving beyond predictive models to build AI-native, agentic systems that can sense, decide, and act at scale. Topics include:

Live store operations that respond instantly to real-world signals

AI architectures that move from “data-informed” to “data-delegated” decisions

Practical lessons from embedding real-time thinking across teams and tech stacks

This is a session for retail and data leaders who are ready to move beyond dashboards and start building intelligent systems that deliver both customer delight and operational agility.

In the race to unlock value from AI and data, technology isn’t the bottleneck - PEOPLE are. Organisations are pouring millions into data platforms and tooling yet still struggle to deliver measurable impact. Why? Because the real challenge lies in attracting, developing, and retaining the right talent - and empowering them to drive change without burning out.

In this session, Jez Clark, CEO of Eden Smith Group, shares a strategic blueprint for building high-impact data teams that don’t just deliver dashboards, but drive transformation. Drawing on 25 years of experience and partnerships across public and private sectors, Jez will explore how to future-proof your workforce for AI, embed continuous capability building, and bridge the gap between talent strategy and business outcomes.

Discussion points:

• Build data-centric talent ecosystems that scale

• Plan your workforce for an AI-first future

• Tackle change fatigue and boost team resilience

• Align learning and culture to performance

Whether you’re leading a data function, navigating transformation, or struggling to activate the full potential of your teams, this session will offer practical insights and frameworks to help you build talented data teams for business impact - and turn in turn create a lasting competitive advantage

To explore how the University of Oxford leverages a unified approach to high-performance computing infrastructure and scalable data platforms across the Big Data Institute and the Centre for Human Genetics to advance biomedical research across the entire University.

This session will discuss:

• Breakthroughs enabled by HPC and secure data platforms in health research
• Infrastructure needs for biomedical innovation and large-scale data science
• Oxford’s partnership journey with Dell Technologies and NVIDIA and its real-world impact
• How scalable AI infrastructure is accelerating research outcomes

If you read the headlines, you might have sensed that hype and optimism around AI is changing. With talk of an AI bubble bursting, and AI Agents being the latest AI tech to hit the headlines, where does that leave us? 

Agentic systems, vibe coding, and no-code/low-code platforms still hold the promise to reshape how businesses build and deploy technology, but not if done haphazardly. Only with a good long-term plan will they deliver sustainable value at scale

In this fireside chat, Manuka sits down with Lucile Flamand, Chief Strategic Development Officer at Bibby Financial Services, to unpack their AI journey and how they’re driving real business outcomes.  

Attend this session to avoid the hype, sidestep the doomsayers and begin to think long term about how you can see real value from deploying AI Agents. 

Data governance often begins with Data Defense — centralized stewardship focused on compliance and regulatory needs, built on passive metadata, manual documentation, and heavy SME reliance. While effective for audits, this top-down approach offers limited business value. 

Data Governance has moved to a Data Offense model to drive Data Monetization of Critical Data Assets in focusing on analytics and data science outcomes for improved decision-making, customer and associate experiences. This involves the integration of data quality and observability with a shift-left based on tangible impact to business outcomes, improved governance maturity, and accelerated resolution of business-impacting issues.

The next iteration is to move to the next phase of Data Stewardship in advancing to AI-Augmented and Autonomous Stewardship — embedding SME knowledge into automated workflows, managing critical assets autonomously, and delivering actionable context through proactive, shift-left observability, producer–consumer contracts, and SLAs that are built into data product development.