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.

What does it take to deploy a data platform serving 22 European territories? We built it in just six months, and two years on, I'm sharing the insights. This is a journey filled with challenges and discoveries. I'll be sharing the inside story of how SkyShowtime achieved this ambitious goal, from the initial vision to the realities of large-scale implementation. Expect candid insights, valuable lessons, and a glimpse into how we're leveraging data to fuel SkyShowtime's continued growth and innovation.

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. 

Analytical Data Product success is traditionally measured with classic reliability metrics. If we were ambitious, we might track user engagement by dashboard views or self-serve activity; they are blunt, woolly indicators at best. The real goal was always to enable better decisions, but we often struggle to measure whether our data products actually help. Conversational BI changes this equation. Now we can see the exact questions users are asking, what follow-ups they need, and where the data model delights or frustrates them. This creates a richer feedback loop than ever before, but it also puts our data model front and centre, exposed directly to business users in a way that makes design quality impossible to hide.

This session will recap the foundations of good data product design, then dive into what conversational BI means for analytics teams. How do we design models that give the best foundation? How can we capture and interpret this new stream of usage feedback? What does success look like? We'll answer all of these questions and more.

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.

Open Banking data offers transaction-level granularity at scale. This is ideal for building models that power credit underwriting, customer segmentation, and market trend analysis. This session covers practical techniques for transforming raw transaction data into structured features using entity resolution, feature engineering, and aggregation pipelines.

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.

Change – social and environmental, rural and urban – disproportionately impacts women and children. The UN reports that 80% of people displaced by climate change are women and girls. Research shows that domestic violence and crime, particularly against women, increases with male job loss. Understanding prevailing patterns by combining location data with social and environmental data can help identify vulnerable populations and mitigate these risks.

Join this session with Snowflake and Ordnance Survey to:

• Explore how integrating different types of geospatial data, such as maps, with open source data can expose the impact of these changes

• Discover how overlaying this information enriches visualization and analysis of vulnerabilities to identify better solutions

• Learn how data from the UK’s Ordnance Survey available through the Snowflake Marketplace addresses global challenges and helps mitigate risks

• Hear about Snowflake’s initiative to End Data Disparity

How to move data from thousands of SQL databases to data lake with no impact on OLTP? We'll explore the challenges we faced while migrated legacy batch data flows to event-based architecture. A key challenge for our data engineers was the multi-tenant architecture of our backend, meaning that we had to handle the same SQL schema on over 15k databases. We'll present the journey employing Debezium, Azure Event Hub, Delta Live tables and the extra tooling we had to put in place.

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. 

Sales and Operational Planning (S&OP) is a crucial function for operations and logistic industries. This talk explores how you take a technical and data led company through the process of building and upgrading your tech stack as you scale, so your forecasting and planning teams can upskill and mature, and the teams are delivering the most value with the data and tools available to you.

In today’s data-led environment, fostering a robust data culture is no longer a nice-to-have, it’s a strategic necessity. This session will explore how forward-thinking organisations are embedding data into the very fabric of their culture, not only within technical teams but across marketing, operations, and senior leadership.

We’ll delve into how companies are aligning data strategy with customer experience, and how inclusive, high-performing teams are being built with diversity and empowerment at their core.

Drawing on examples from leading UK and global brands such as Unilever, Sainsbury’s, and Haleon, we’ll examine how scaling data literacy and trust across functions can unlock innovation and drive competitive advantage. Join us to discover what a strong data culture looks like in 2025, how to foster cross-functional collaboration, and what lies ahead for data-driven transformation.

Powered by: Women in Data®

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.