I don’t like it. I don't get it. I don’t trust it.' 

Sounds familiar?

Even with the best Data and AI tools, organisations struggle to see results if their people don’t engage. Change is hard, and without attention to mindset and culture, even the most sophisticated Data and AI initiatives can fall short.

Rosanne Werner, CEO of XcelerateIQ, previously led the multi-award winning Data and AI Mindset and Culture programme at Coca-Cola Europacific Partners, reaching over 2,000 participants worldwide.

In this session, we’ll explore how to ‘Crack the Human Firewall’ with a science-based approach to Data and AI mindset and culture. 

You’ll learn:

• How the brain changes and learns - and how to use this to enable Data and AI acceptance.

• How to rewire habits to integrate Data and AI into daily workflows.

• How to use social dynamics to build a collective mindset that advocates for Data and AI.

Proven by science - Let's help your teams put Data and AI into Action!

In today's fast-moving world, real-time mobility data is transforming decision-making across industries. This session will explore how businesses can leverage real-time mobility insights for faster, more dynamic, and proactive strategies. Through a live demo of real-time mobility data, we’ll showcase real-world applications of how this type of data can help organizations drive precision and agility in their operations.

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.

Site Reliability Engineers spend countless hours diagnosing complex production issues and optimizing system performance. This talk explores how Large Language Models can augment SRE workflows with real-world examples.

We'll also demonstrate live tooling that leverages AI to optimize database configurations - automatically recommending ordering keys, generating materialized views, and suggesting performance improvements based on query patterns and system metrics.

Rather than theoretical possibilities, this talk focuses on practical implementations with measurable results. Attendees will learn actionable strategies for integrating LLMs into their SRE workflows, understand the current boundaries of AI effectiveness in operations, and see concrete examples of tooling already delivering value in production environments.

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.

How do you deliver reliable data across dozens of countries, diverse tech stacks, and constantly evolving use cases? In this session, Vinicio Oliviera, Senior Data Platform Manager at Delivery Hero, shares how his team ensures trust in data at scale. From real-time sales streams to AI-driven vendor insights, he’ll show how Delivery Hero uses Monte Carlo’s monitoring-as-code to unify reliability across regions, balance central standards with local autonomy, and keep data powering decisions around the globe.

Many Data Engineers play very reactive roles. Because of that they struggle with cross-org collaboration, getting a seat at the table, creating alignment, and making their data producers produce high-quality data for them.

But there's another way – Data Engineers can LEAD!

Data Engineers sit at the intersection of data and their business, and if they execute well, they can play a very pivotal role!

In this talk, Orr and Shachar will explain what it looks like when you lead as a Data Engineer, and give practical suggestions for ways to apply that in your job.

AI is rewriting the recruitment rulebook with the potential to make it smarter, fairer and more efficient, but it is not without its challenges. In this 30-minute session, leading Women in Data® experts from AXA, Munich Re, Royal London, Thatcham Research and Virgin Media O2 unpack the real-world impact of AI on hiring practices.  

Our panel will share how they are responding to the rise of AI-generated profiles, the growing cost of candidate screening and the responsibility to maintain transparency and trust at all stages. Expect practical insights and honest reflections on what it takes to proactively utilise AI responsibly, and effectively, in recruitment today.

Powered by: Women in Data®

AI is only as good as the data it runs on. Yet Gartner predicts in 2026, over 60% of AI projects will fail to deliver value - because the underlying data isn’t truly AI-ready. “Good enough” data isn’t enough.

In this exclusive BDL launch session, DataOps.live reveal Momentum, the next generation of its DataOps automation platform designed to operationalize trusted AI at enterprise scale.

Based on experiences from building over 9000 Data Products to date, Momentum introduces breakthrough capabilities including AI-Ready Data Scoring to ensure data is fit for AI use cases, Data Product Lineage for end-to-end visibility, and a Data Engineering Agent that accelerates building reusable data products. Combined with automated CI/CD, continuous observability, and governance enforcement, Momentum closes the AI-readiness gap by embedding collaboration, metadata, and automation across the entire data lifecycle.

Backed by Snowflake Ventures and trusted by leading enterprises, including AstraZeneca, Disney and AT&T, DataOps.live is the proven catalyst for scaling AI-ready data. In this session, you’ll unpack what AI-ready data really means, learn essential practices, discover a faster, easier, and more impactful way to make your AI initiatives succeed.

Be the first to see Momentum in action - the future of AI-ready data.

In data integration and data management, the focus is often on technology—databases, ETL processes, automation, reporting tools. But in the process, the true objective is easily overlooked: generating business value. 

Why does this happen? What organizational and technical barriers contribute to the disconnect? And most importantly: what strategies can we adopt to better align data initiatives with the goals of business stakeholders? 

This session explores the root causes and presents practical approaches to building a data-driven culture—with a clear focus on business impact.

In this session, we will explore the world of small language models, focusing on their unique advantages and practical applications. We will cover the basics of language models, the benefits of using smaller models, and provide hands-on examples to help beginners get started. By the end of the session, attendees will have a solid understanding of how to leverage small language models in their projects. The session will highlight the efficiency, customization, and adaptability of small models, making them ideal for edge devices and real-time applications.

We will introduce attendees to two highly used Small Language Models: Qwen3 and SmolLM3. Specifically, we will cover:

1. Accessing Models: How to navigate HuggingFace to explore and select available models. How to view model documentation and determine its usefulness for specific tasks

2. Deployment: How to get started using

(a) Inference Provider - using HuggingFace inference API or Google CLI

(b) On-Tenant - using Databricks Model Serving

(c) Running the Model Locally - Using Ollama and LMstudio

3. We also examine the tradeoffs of each route

Insights on how to generate real value with AI, treat data as a product, and the state of data products today.

Paradime is the pioneer in building the 'Cursor for Data' - a platform for the AI-enabled analytics engineer to achieve 10x productivity. In this lighting talk, we will showcase how Paradime and DinoAI is changing the way analytics engineering is done.

In the era of AI agents, data products, and exploding complexity, enterprises face an unprecedented challenge: how to give machines and people, the context needed to make decisions that are accurate, explainable, and trustworthy. 

What if data teams could solve this? 

We'll explore a new opportunity: data teams as the architects and stewards of the “Enterprise Brain”, a unified knowledge layer that connects business concepts, data, and processes across the organization. By treating context as a first-class citizen, organizations can bridge structured and unstructured worlds, govern data and AI agents effectively, and unlock faster, more confident decision-making. 

We'll unpack: 

• Why context and knowledge are becoming the missing pieces of AI readiness. 

• How structured and unstructured data are converging, and why this demands a new governance model. 

• Why ontologies, knowledge graphs, and active governance are critical to managing the coming explosion of AI agents. 

• The unique chance data teams have right now to lead and move from building pipelines to building the enterprise brain. 

We won’t talk tools. We’ll talk possibility. By the end, you’ll see why the most successful organizations of the next decade will be the ones who make their data teams the creators and owners of knowledge itself. 

What the rest of the world can learn from gaming’s data-first approach to AI adoption.

Gaming pioneered many of the AI foundations we rely on today, from GPUs to reinforcement learning. It continues to drive innovation, but it has also built a strong resistance to hype. This session explores how gaming teams evaluate, deploy, and reject AI solutions with a discipline other industries can learn from.

Join Maz Sleiman and Harriet Thomson from CRU to explore how their team transformed commodity supply and demand modeling from a siloed Excel environment into a systematic, governed platform using Sigma. The session focuses on building interconnected, robust forecasting models to drive accurate, high-quality reports for CRU’s analyst teams, ultimately benefiting end clients through better data availability and consistency.

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.

As organizations increasingly adopt AI and data-driven strategies, ensuring quality and reliability across the entire data + AI estate has never been more critical. This session will explore 2026 as the year of Data + AI Observability, highlighting key trends driving this transformation. Attendees will gain insights into how observability bridges the gap between data and AI systems across your data, system, code, and models, enabling more trustworthy, scalable, and efficient operations. Join us to learn practical approaches and tools that can future-proof your data and AI initiatives to drive real business impact.

For years, data governance has been about guiding people and their interpretations. We build glossaries, descriptions and documentation to keep analysts and business users aligned. But what happens when your primary “user” isn’t human? As agentic workflows, LLMs, and AI-driven decision systems become mainstream, the way we govern data must evolve. The controls that once relied on human interpretation now need to be machine-readable, unambiguous, and able to support near-real-time reasoning. The stakes are high: a governance model designed for people may look perfectly clear to us but lead an AI straight into hallucinations, bias, or costly automation errors.

This session explores what it really means to make governance “AI-ready.” We’ll look at the shift from human-centric to agent-centric governance, practical strategies for structuring metadata so that agents can reliably understand and act on it, and what new risks emerge when AI is the primary consumer of your data catalog. We'll discuss patterns, emerging practices, and a discuss how to transition to a new governance operating model. Whether you’re a data leader, platform engineer, or AI practitioner, you’ll leave with an appreciation of governance approaches for a world where your first stakeholder might not even be human.

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.

Traditional data governance is often insufficient for the amplified risks of live AI models, from bias to black-box decisions. In this session, we'll discuss a capability framework for full-lifecycle AI governance, designed to manage model behavior, build trust, and ensure your AI performs as intended over time.

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

Modern enterprises can’t manage data they don’t understand – uncovering the code-to-data relationship is the missing link. As data ecosystems grow more complex, traditional approaches to tracking data lineage can’t keep up. This talk explores how AI-driven code analysis can automatically build end-to-end lineage graphs, giving engineers clear visibility into hidden dependencies across large, legacy, and regulated systems. We’ll show how AI enhances data catalogues and introduce Gable - a tool that helps teams map, validate, and monitor data flows at scale. A live demo on a large energy data codebase will highlight how AI transforms lineage tracking from a manual headache into an automated, scalable solution.

In this session, we’ll share our transformation journey from a traditional, centralised data warehouse to a modern data lakehouse architecture, powered by data mesh principles. We’ll explore the challenges we faced with legacy systems, the strategic decisions that led us to adopt a lakehouse model, and how data mesh enabled us to decentralise ownership, improve scalability, and enhance data governance.