Everyone’s talking about GenAI. But at Big Data London, you want more than hype. 

In this session, Simon Devine (Founder of Hopton Analytics) shares how the East of England Co-op embedded GenBI – Pyramid’s generative AI tool – into their business intelligence platform to improve how decisions are made across the organisation. 

This wasn’t a flashy experiment. It was a carefully planned rollout of AI-generated explanations, natural language querying, and explainable analytics – designed to support busy operational teams, reduce report backlogs, and drive smarter decisions at scale. 

Simon will take you behind the scenes of the project: how it was planned, what hurdles had to be overcome, and the governance structures that helped it succeed. You'll hear honest reflections on what worked, what didn’t, and what they’d do differently.

 Whether you’re a data leader looking for real-world use cases, a BI owner exploring GenAI adoption, or a transformation lead trying to unlock value from your reporting stack – this session will give you practical insight, not just theory.

 Come for the lived experience. Leave with ideas you can actually use.

This talk will explore how NVIDIA Blueprints are accelerating AI development and deployment across various industries, with a focus on building intelligent video analytics agents. Powered by generative AI, vision-language models (VLMs), large language models (LLMs), and NVIDIA NIM Microservices, these agents can be directed through natural language to perform tasks such as video summarization, visual question answering, and real-time alerts. This talk will show how VSS accelerates insight from video, helping industries transform footage into accurate, actionable intelligence.

AI Agents aren’t just changing how we build software - they’re redefining how software is bought, adopted and scaled. From customer support to manufacturing to compliance, AI-driven systems are unlocking new productivity and automation. But turning that potential into business impact takes more than smarter models and data. It requires rethinking go-to-market strategy, packaging and distribution.

In this session, Ravi Ramachandran, Co-Founder of AI agent project Eidolon AI and Growth Advisor to several startups through The GTM Firm, offers a dual perspective from inside the engine room building intelligent systems and the front lines of bringing them to market. Drawing on patterns across industries, he’ll share how AI tools are actually being used, what’s driving awareness and adoption and the new GTM playbooks emerging in an Agent and GenAI-powered world.

You’ll leave the session with practical, real-world examples of how to package, position and scale AI Agent solutions and a clear view of what’s hype versus what’s delivering results today.

Are you struggling to gain leadership support, craving stakeholder engagement, and begging for proper funding? Even though you may create Agentic AI wonders with your data, it won’t matter unless you explain the value in practical business terms. Join The Data Whisperer’s rollicking and riotous review of current buzzwords and some practical tips including:

• Differentiating between a data management narrative and other data storytelling and data literacy efforts

• Developing strategies to secure sponsorship and funding

• The 3Vs of Data Storytelling for Data Management

See how GenAI + graph algorithms enable AI agents to reason, learn, and uncover insights—beyond static data or simple retrieva

This session presents the knowledge graph as a dynamic reasoning engine, not just a static data repository. Learn how to deploy autonomous AI agents that intelligently navigate the relationships within your connected data to discover profound insights. Leveraging GenAI and graph algorithms, this agentic approach moves beyond simple retrieval to create a verifiable foundation for AI systems that can reason and learn.

As enterprises scale their deployment of Generative AI (Gen AI), a central constraint has come into focus: the primary limitation is no longer model capability, but data infrastructure. Existing platforms, optimized for human interpretation and batch-oriented analytics, are misaligned with the operational realities of autonomous agents that consume, reason over, and act upon data continuously at machine scale. 

In this talk, Zhamak Dehghani — originator of the Data Mesh and a leading advocate for decentralized data architectures — presents a framework for data infrastructure designed explicitly for the AI-native era. She identifies the foundational capabilities required by Gen AI applications: embedded semantics, runtime computational policy enforcement, agent-centric, context-driven discovery.

The session contrasts the architectural demands of AI with the limitations of today’s fragmented, pipeline-driven systems—systems that rely heavily on human intervention and customized orchestration. Dehghani introduces autonomous data products as the next evolution: self-contained, self-governing services that continuously sense and respond to their environment. She offers an architectural deep dive and showcases their power with real-world use cases.  

Attendees will learn the architecture of “Data 3.0”, and how to both use GenAI to transform to this new architecture, and how this new architecture serves GenAI agents at scale.

Join Sami Hero and Tammie Coles, as they share how Ellie is reinventing data modeling with AI-native tools that empower both technical and non-technical users. With CData Embedded Cloud, Ellie brings live metadata and data models from systems like Snowflake, Databricks, and Oracle Financials into a unified modeling workspace. Their platform translates legacy structures into human-readable insights, letting users interact with a copilot-style assistant to discover, refine, and maintain data models faster—with less reliance on analysts.

You’ll see how Ellie uses generative AI to recommend new entities, reconcile differences between models and live systems, and continuously document evolving data environments. Learn how corporations are using Ellie and CData together to scale high-quality data modeling across teams. reducing rework, accelerating delivery of analytics-ready models, and making enterprise architecture accessible to the business.

Starburst delivers faster time to insights whilst breaking down data silos, serving data to & tightly integrating with GenAI & Agents.

The world of data is undergoing a seismic shift. From increasing scale & concurrency, to increasing technical complexity, increasing compliance scrutiny, and all this in the face of supporting the data-ravenous AI revolution.

So how do you deliver the right data to the right place at the right time whilst still maintaining control & accountability in increasingly regulated environments?

In this session, we’ll explore how the Starburst data platform delivers faster time to insights whilst breaking down data silos, serving data to & tightly integrating with GenAI & Agents at velocity, and achieving all this within the tight constraints of a well-governed architecture that meets regulatory compliance demands.

The Generative AI revolution is here, but so is the operational headache. For years, teams have matured their MLOps practices for traditional models, but the rapid adoption of LLMs has introduced a parallel, often chaotic, world of LLMOps. This results in fragmented toolchains, duplicated effort, and a state of "Ops Overload" that slows down innovation.

This session directly confronts this challenge. We will demonstrate how a unified platform like Google Cloud's Vertex AI can tame this complexity by providing a single control plane for the entire AI lifecycle.

Grounding Large Language Models in your specific data is crucial, but notoriously challenging. Retrieval-Augmented Generation (RAG) is the common pattern, yet practical implementations are often brittle, suffering from poor retrieval, ineffective chunking, and context limitations, leading to inaccurate or irrelevant answers. The emergence of massive context windows (1M+ tokens) seems to offer a simpler path – just put all your data in the prompt! But does it truly solve the "needle in a haystack" problem, or introduce new challenges like prohibitive costs and information getting lost in the middle? This talk dives deep into the engineering realities. We'll dissect common RAG failure modes, explore techniques for building robust RAG systems (advanced retrieval, re-ranking, query transformations), and critically evaluate the practical viability, costs, and limitations of leveraging long context windows for complex data tasks in Python. Leave understanding the real trade-offs to make informed architectural decisions for building reliable, data-grounded GenAI applications.

Leaders operate across three dimensions: people, business, and technology. A generational shockwave like GenAI has large-scale and fast impact (be it true or perceived impact) on these three dimensions.

We leaders then face a sprint of interesting challenges like:

• How to determine what value of this technology is currently underestimated vs overestimated, and how does this change in the future?
• How do we contribute to the larger leadership team across different skillsets (sales, product, etc) in the company, being the subject matter experts on this topic?
• How do we steer through the learning curve, for both the individual contributors in the team, and the wider company?

And few more similar challenges!

Join us for a nice panel discussion on this topic.

Buckle up for a bold ride into the future of performance intelligence. In this session, Keyloop - one of the world’s top digital innovators in automotive retail shares how it’s putting data in the driver’s seat to revolutionise decision-making.

Powered by ThoughtSpot and AWS first-party technologies, get an inside look at VEGA, their next-gen AI-powered performance intelligence platform. No dashboards. No bottlenecks. Just real-time, actionable insights that surface hidden issues, suggest smarter actions, and boost performance, profit, and customer experience.

If you're ready to see what happens when AI meets speed, scale, and simplicity, this is your green light.

Want to get your GenAI idea noticed? Databricks engineers share their hands-on experiences building interactive demos that actually made business leaders sit up and take notice.

We’ll walk through the journey from a single idea to a working prototype in under a month. Hear how we did it, what worked, what didn’t, including the unexpected hurdles that tripped us up, by taking a practical look at how to:

• Translate technical impact into business value
• Make your voice heard in large dev teams
• Avoid common pitfalls, from permissions to procurement

If you’re a data scientist, engineer, or AI leader who wants to move fast and make your work impossible to ignore, join us to explore how you could create the Minimum Viable Product that makes you the Most Valuable Player.

A leading rheumatologist teamed up with an AI specialist to help with a systematic review of lupus research, a condition of which 90% of patients are women. Together, they revealed the deep-rooted biases in research methodologies - and the machine learning models that underpin them.

Professor Coziana Ciurtin (UCL) and Mirela Gyurova (Kubrick) share the story behind their GenAI tool which can revolutionise underfunded and underexplored areas of medical research, including identifying ML-enforced biases.

When gender and ethnic disparities in healthcare persist, what responsibilities do data professionals have in shaping ethical, impactful AI? And how can partnerships between industry and academia unlock new standards for evidence, equity, and trust in ML?

For anyone building, using, or regulating AI, this session will challenge assumptions and make the case for responsible, cross-sector innovation.

Powered by Women in Data®

How Generative AI dynamically transforms business problems into executable graphs of data products, by creating AI-powered digital twins. Describing business needs in plain language and generating and simulate entire end to end business processes with UX as an interconnected eco-system as graph data products, demonstrated through real-world use-cases.

Governing generative AI systems presents unique challenges, particularly for teams dealing with diverse GenAI subdomains and rapidly changing technological landscapes. In this talk, Maarten de Ruiter, Data Scientist at Xomnia, shares practical insights drawn from real-world GenAI use-cases. He will highlight essential governance patterns, address common pitfalls, and provide actionable strategies for teams utilizing both open-source tools and commercial solutions. Attendees will gain concrete recommendations that work in practice, informed by successes (and failures!) across multiple industries

According to MIT, 95% of organisations are seeing no return from their GenAI investments. Why? Because value doesn’t come from models alone. It comes from trust, governance, and people. Learn how organisations are breaking through the hype using Microsoft Fabric to unify data, Purview to govern it, and Copilot to empower every user. With a real-world customer story and a clear blueprint for action, this session will help you join the 5% who are turning AI ambition into impact.