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.

This talk will introduce NVIDIA Inference Microservices (NIMs), a set of easy-to-use microservices designed to accelerate the deployment of generative AI models across various platforms, including clouds, data centers, and workstations. We will explore how NIMs simplify and speed up the deployment of AI applications to provide AI solutions for various industries.

Data remains one of the most valuable assets a company has to guide its decision making. How that data is processed, used and presented is changing rapidly and with it the role and skills of data engineers. 

In this fireside chat, Manuka will explore the future of data engineering and the ongoing challenges of overcoming legacy constrains and governance with the latest breakthroughs in AI.

Expect a grounded discussion on:

• What “AI-ready” really means for data engineers

• Engineering through legacy constraints in a highly regulated environment

• Designing ingestion, orchestration, and observability that scale

• Embedding governance and quality without slowing delivery

• What’s next for data engineering in the age of generative AI

Whether you’re building pipelines, managing platforms, or designing modern data infrastructure, this is a rare behind-the-scenes look at how data engineering is evolving to meet the AI moment.

AI can enable you to achieve a lot for your business in terms of increased revenue, more efficient operations, and reduced risk. However, most organisations are not getting the traction or the value. 

We’ll look at how you get traction, moving from concept to value and everything in between. Referring to Generative AI and Agentic AI. You’ll also understand that starting with a project is a mistake and will stop you scaling and growing your capabilities. 

You’ll get an understanding of a framework for identifying and aligning AI activities to your business strategy. Using a proven approach to enable you to identify and prioritise projects with the best impact and greatest chance of success, which in turn will generate most value for you

You’ll also gain an understanding of how you need to organise yourself and manage your data to ensure the success of AI.

Deepak has building AI systems since 2014 starting with a Logistic Regression based model to now building Gen AI based systems in 2025. His talk will feature both the technical, business and human aspects of the AI systems he has built and contrast and compare them over the years. The intention is to peek into what could be possible in the future, keeping the past in mind. 

The term 'agentic AI' is all the rage these days, but there's still not much clarity around what it means. We'll walk through the basic building blocks of these agentic AI systems - predictive AI, generative AI, and workflow automation - and discuss why it's harder (and more important) than ever to ensure a trusted, enterprise-grade, and secure data backbone to get the reliable and trusted solutions our end-users are looking for. We'll also touch on market trends where we see the technology and capabilities evolving in the coming months.

Agentic AI—systems that autonomously set goals, make decisions, and execute multi-step business processes—is transforming the enterprise, unlocking new levels of productivity. But with greater autonomy comes greater risk, as agentic AI amplifies the challenges of traditional and generative AI by increasing agency.

In this session, attendees will learn how to govern agentic AI with trust and transparency, enabling innovation without compromising safety. The speaker will discuss how targeted controls—enabled by the right tools and frameworks at the right time—can keep pace with fast-moving technology. Real-world case studies will illustrate how leading organizations are successfully managing agentic AI to transform workflows, boost productivity, and scale responsibly.

75% of GenAI projects fail to scale—not because the models lack sophistication, but because they’re built on fragmented data. If your systems don’t know who they're talking about, how can your AI deliver reliable insights?

This talk unveils how real-time Entity Resolution (ER) is becoming the silent engine behind trusted, AI-ready data architecture. We will discuss how organizations across financial services, public safety, and digital platforms are embedding ER into modern data stacks—delivering identity clarity, regulatory confidence, and faster outcomes without the drag of legacy MDM.

You’ll learn:

• Why ER is foundational for AI trust, governance, and analytics
• Patterns for embedding ER into streaming and event-driven architectures
• How ecosystem partners and data platforms are amplifying ER value
• How to build trust at the entity level—without slowing down innovation

Whether you’re modernizing architecture, launching AI programs, or tightening compliance, this session will equip you to embed trust from the ground up.

Sound AI outcomes start with trusted, high-quality data and delivering it efficiently is now a core part of every data and AI strategy. In this session, we’ll discuss how AI-supportive capabilities such as autonomous data catalogs, unstructured metadata ingestion and automated data trust scoring are transforming how organizations deliver AI-ready data products at scale with less hands-on staff involvement.

You’ll see how GenAI and agentic AI can accelerate reliable data delivery at every stage, from identifying and fixing data issues to building semantic business layers that give your AI models the context-rich inputs needed for success. We’ll also explore how agentic AI enables self-updating catalogs, proactive data quality monitoring, and automated remediation to free your teams to focus on innovation instead of maintenance.

If you’re shaping your organization’s data and AI strategy as a CDO, CDAIO, CIO, or data leader, this is your blueprint to operationalizing trusted, governed, and AI-ready data for every initiative, faster and smarter.

SAP Business Data Cloud is a fully managed solution that unifies and governs all SAP data while seamlessly integrating with third-party sources. With SAP Business Data Cloud, organisations can accelerate decision-making by empowering business users to make more impactful choices. It also provides a trusted foundation for AI, ensuring that data across applications and operations is reliable, responsible, and relevant—enabling organisations to harness the full potential of generative AI.

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

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.

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.

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.