In a world where data defines destiny, creativity is being reimagined through the lens of artificial intelligence. This session brings together Payal Jain, Chair of Women in Data®, Benjamin Field, Executive Producer at Deep Fusion Films and Roisin McCarthy, Founder Women in Data® for a candid conversation about their pioneering partnership at the intersection of data, storytelling, and AI.

Together, they will explore how Deep Fusion Films is pushing the boundaries of creative AI, from avatar-led cinematic experiences to AI-assisted documentaries and why the arts must embrace this evolution. The discussion will unpack the ethical considerations that come with innovation, the importance of human-centred design, and the role of inclusive collaboration in shaping responsible creative futures.

Expect insights into how AI is not just a technical revolution but a human one, and why the next generation of artists, technologists, and data leaders must co-create with purpose. This session is a celebration of imagination, impact, and the power of partnership.

Powered by: Women in Data®

The modern enterprise is increasingly defined by the need for open, governed, and intelligent data access. This session explores how Apache Iceberg, Dremio, and the Model Context Protocol (MCP) come together to enable the Agentic Lakehouse. A data platform that is interoperable, high-performing, and AI-ready.

We’ll begin with Apache Iceberg, which provides the foundation for data interoperability across teams and organisations, ensuring shared datasets can be reliably accessed and evolved. From there, we’ll highlight how Dremio extends Iceberg with turnkey governance, management, and performance acceleration, unifying your lakehouse with databases and warehouses under one platform. Finally, we’ll introduce MCP and showcase how innovations like the Dremio MCP server enable natural-language analytics on your data. 

With the power of Dremio’s built-in semantic layer, AI agents and humans alike can ask complex business questions in plain language and receive accurate, governed answers.

Join us to learn how to unlock the next generation of data interaction with the Agentic Lakehouse.

The growth of connected data has made graph databases essential, yet organisations often face a dilemma: choosing between an operational graph for real-time queries or an analytical engine for large-scale processing. This division leads to data silos and complex ETL pipelines, hindering the seamless integration of real-time insights with deep analytics and the ability to ground AI models in factual, enterprise-specific knowledge. Google Cloud aims to solve this with a unified "Graph Fabric," introducing Spanner Graph, which extends Spanner with native support for the ISO standard Graph Query Language (GQL). This session will cover how Google Cloud has developed a Unified Graph Solution with BigQuery and Spanner graphs to serve a full spectrum of graph needs from operational to analytical.

As brands grow and scale, the increasing number of customer data sources creates fragmented profiles, which obscures high-value customers and prevents effective personalisation. Join New Look’s Chief Transformation Officer, Dan Chasle, as well as Matthew Biboud-Lubeck, Amperity’s General Manager EMEA, as they discuss the challenges of building accurate customer profiles when data is scattered across multiple systems.

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.

In today’s world, the organizations that succeed are the ones that can pivot quickly, confidently, and without disruption. The power of choice is everything. Today organizations must be able to choosing which data to use, and systems to use, and how to adapt when priorities shift is now critical for survival.

The West Yorkshire Combined Authority (WYCA) faced a high-stakes challenge: prepare for multi-million-pound bus franchise negotiations with clear, defensible evidence of network performance. Using FME, they transformed over 2.5 million real-time GPS bus pings into a scalable, grid-based value-scoring model that revealed exactly which routes and operators delivered the most value.

The result? Faster, in-house analysis without costly consultancy fees, stronger negotiating power backed by objective data, and a repeatable blueprint for data-driven ROI that is applicable across the UK public sector and beyond.

Join Safe Software and WYCA to discover how all-data, any-AI integration can turn raw, real-time data into actionable intelligence empowering organizations to make smarter decisions, faster.

Unlock the true potential of your data with the Qlik Open Lakehouse, a revolutionary approach to Iceberg integration designed for the enterprise. Many organizations face the pain points of managing multiple, costly data platforms and struggling with low-latency ingestion. While Apache Iceberg offers robust features like ACID transactions and schema evolution, achieving optimal performance isn't automatic; it requires sophisticated maintenance. Introducing the Qlik Open Lakehouse, a fully managed and optimized solution built on Apache Iceberg, powered by Qlik's Adaptive Iceberg Optimizer. Discover how you can do data differently and achieve 10x faster queries, a 33-42% reduction in file API overhead, and ultimately, a 50% reduction in costs through streamlined operations and compute savings.

As organisations adopt artificial intelligence and autonomous agents, they encounter new technical challenges when integrating and scaling these solutions across the enterprise. This session provides an engineer’s view on how to effectively scale agents in complex business environments.

The presentation will cover the key architectural decisions, integration techniques, and best practices needed to ensure that agent-based systems can perform reliably at scale. You’ll learn how to overcome common obstacles such as managing data, ensuring compatibility with existing systems, and monitoring performance. The session will also explore new tools and frameworks that support the deployment of agents on a large scale, as well as practical advice for engineering teams implementing these solutions.

Whether you’re planning your first agent deployment or looking to improve your current systems, this session will give you valuable technical insights and a roadmap for scaling intelligent agents in your organisation.

Data products enable the transformation of raw data into actionable insights, enabling faster decision-making, democratised access to data, and innovation. Organisations that leverage them gain competitive advantages like improved efficiency, cost savings, and enhanced customer experiences.

I will address the critical role of data products in modern organisations. Combining compelling storytelling, actionable framework, and real-world examples to explore the challenges of data utilisation, the transformative potential of data products, and strategies to drive innovation.

With most data collected being unused, the opportunity cost and the pressing need for action will leave you with a clear understanding of the role data products play as strategic assets, the steps to get started, and how to avoid common pitfalls.

Key Takeaways

The Challenge: Organisations are generating massive amounts of data but face hurdles like siloed systems, delayed insights, and limited access for non-technical users. The consequences of inaction include inefficiencies, missed opportunities, and suboptimal customer experiences.

The Case for Data Products

Pitfalls and Solutions

Start Small, Focus on Users, and Scale

Data Products Are Your Future Competitive Strategy.

How long will it be until an AI first company dominates your industry? Could that disrupter be your business? 

In this keynote, Gareth Martin (CEO of Manuka AI) and Maria Bines (CEO and co-founder of SynapseDX) will challenge comfortable assumptions about how AI should be used. Maria will share how she turned AI agents into a working scrum team — complete with job descriptions, an org chart, and the messy lessons of managing digital workers who don’t behave like humans. Gareth will explore why today’s obsession with “use cases” is a band-aid, and why process-driven adoption is the only way to avoid being disrupted — or irrelevant. 

This session isn’t about safe platitudes. It’s about what’s really happening in the wild, what technical professionals need to prepare for, and why the future of AI could look like seamless orchestration… or a chaotic swarm.

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.

Data leaders today face a familiar challenge: complex pipelines, duplicated systems, and spiraling infrastructure costs. Standardizing around Kafka for real-time and Iceberg for large-scale analytics has gone some way towards addressing this but still requires separate stacks, leaving teams to stitch them together at high expense and risk.

This talk will explore how Kafka and Iceberg together form a new foundation for data infrastructure. One that unifies streaming and analytics into a single, cost-efficient layer. By standardizing on these open technologies, organizations can reduce data duplication, simplify governance, and unlock both instant insights and long-term value from the same platform.

You will come away with a clear understanding of why this convergence is reshaping the industry, how it lowers operational risk, and advantages it offers for building durable, future-proof data capabilities.

This talk explores the disconnect between MLOps fundamental principles and their practical application in designing, operating and maintaining machine learning pipelines. We’ll break down these principles, examine their influence on pipeline architecture, and conclude with a straightforward, vendor-agnostic mind-map, offering a roadmap to build resilient MLOps systems for any project or technology stack. Despite the surge in tools and platforms, many teams still struggle with the same underlying issues: brittle data dependencies, poor observability, unclear ownership, and pipelines that silently break once deployed. Architecture alone isn't the answer; systems thinking is.

Topics covered include:

- Modular design: feature, training, inference

- Built-in observability, versioning, reuse

- Orchestration across batch, real-time, LLMs

- Platform-agnostic patterns that scale

In this session, Paul Wilkinson, Principal Solutions Architect at Redpanda, will demonstrate Redpanda's native Iceberg capability: a game-changing addition that bridges the gap between real-time streaming and analytical workloads, eliminating the complexity of traditional data lake architectures while maintaining the performance and simplicity that Redpanda is known for.

Paul will explore how this new capability enables organizations to seamlessly transition streaming data into analytical formats without complex ETL pipelines or additional infrastructure overhead in a follow-along demo - allowing you to build your own streaming lakehouse and show it to your team!

Many but not every new AI tool has the potential to increase productivity. From a domain expert view, it is often a challenge to find out which tool supports, which one distracts and eventually how to take an objective decision where and how to apply these.

Is your analytics workflow stuck in fragmented chaos? AlphaSights, the global leader in expert knowledge on demand, used to juggle queries, scripts, spreadsheets, and dashboards across different tools just to get one analysis out the door. Manual updates slowed their teams, stakeholders waited too long for insights, and opportunities slipped through the cracks.With Hex, AlphaSights built a fully integrated Research Hub that unifies data queries, API calls, ML-powered enrichment, and reporting — all in one place. They eliminated manual work, automated updates, and empowered business teams to act faster on opportunities.The result: faster reaction times, broader coverage, and measurable commercial impact. Join this session to see how AlphaSights turned fragmented workflows into a seamless, automated pipeline — and learn how your team can build faster, smarter insights too.

Data platform migrations and modernisations take 18 months. 80% fail or are late. Teams waste 35% of their time rediscovering tribal knowledge. 'Vibe coding' causes 2000% compute spikes. Generic AI tools generate code without understanding your business logic, creating production disasters.

Fortune 500 companies are escaping this $280B crisis. One retail giant cut their SAP-to-Fabric migration from 18 to 6 months. Another achieved 85% error reduction in procurement analytics.

This session reveals how global enterprises capture organisational knowledge permanently, automate the entire data product lifecycle, and deliver production-ready analytics 3x faster. Learn the AI-first approach that transforms months of manual work into weeks of automated delivery.

Ready to move beyond passive data cataloging and unlock true AI-driven value? Join us for an in-depth session on data.world, now fully integrated with ServiceNow’s Workflow Data Fabric. We’ll show how you can unify, govern, and activate your enterprise data—across cloud, hybrid, and on-prem environments—to fuel agentic AI and intelligent automation. See a live demo of data.world’s knowledge graph in action: discover how to connect and contextualize data from any source, automate governance and compliance, and deliver trusted, explainable insights at scale. We’ll walk through real-world use cases, from rapid data discovery to automated policy enforcement and lineage tracking, and show how organizations are accelerating time-to-value and reducing risk. Whether you’re a data leader, architect, or practitioner, you’ll leave with practical strategies and a clear vision for making your data estate truly AI-ready. 

When customers enjoy Gousto's recipe kits, they see the delicious result but not the careful steps it takes to get there. Data works the same way. In this session, Yanick will share how Gousto built a business case for analytics engineering, making an often-invisible discipline central to the company's strategy. He'll unpack how the team moved from ad-hoc outputs to a structured, mesh-ready approach, reducing complexity, proving ROI, and giving leadership confidence in data as a competitive advantage.

Agentic AI is developing fast. Agents are not just a tool; they are learning, adapting, and making decisions alongside us. Integrating Agents into teams is not just a technical challenge, it’s a cultural one. Teams need the space to experiment, the confidence to trust AI where it helps, and the right conditions to learn together. When adoption is thoughtful and inclusive, agentic AI can become a powerful extension of how teams think, decide, and deliver.

The future of healthcare depends not only on breakthroughs in science, but also on how we harness the power of data, technology, and AI. To realise this future, we must challenge long-held assumptions about how data products are delivered. What once took months of complex engineering now happens in days—or even hours—by re-imagining the way we work. At AstraZeneca, we shifted from a traditional IT-centric model to one where business teams take ownership, rapid prototyping drives innovation, and automation ensures quality, compliance, and trust.

 This change is more than a process improvement; it is a cultural transformation. By aligning every step to business value, embracing bold goals, and learning from failure, we have built a system that empowers people to innovate at speed and at scale. Data products are no longer the end goal but the enablers of something greater: a knowledge fabric ready for AI, where enterprise context unlocks smarter decisions and accelerates the delivery of life-changing medicines.

Our journey proves that when ambition meets courage, and technology meets purpose, we can transform the way data serves science—and, ultimately, transform the lives of patients around the world.

As populations age and healthcare systems strain under growing demand, AI is emerging as a vital force for innovation. From predictive health models to AI-powered caregiver assistants and conversational companions, data-driven tools are increasingly supporting elder care. But the rise of the “AI nurse” presents a profound challenge: How do we innovate responsibly while preserving human dignity and empathy?

In this session, Dr. Serena Huang explores the practical and ethical dimensions of applying AI in elder care. This talk bridges the gap between technical development and compassionate delivery, highlighting the critical role data professionals play in building trustworthy, equitable systems for vulnerable populations.

In this session, you will learn:

- How AI and predictive models can address workforce shortages and rising care needs in aging populations.

- How to design systems where AI handles data-heavy tasks while freeing up human caregivers for high-touch, empathetic care.

- Key principles for developing ethical, inclusive, and transparent AI systems that protect privacy and reduce bias.

We are the last generation to lead human-only organizations. The rise of agentic AI—autonomous systems capable of making decisions, learning independently, and collaborating with other agents—demands a profound shift in how we manage, govern, and grow our workforce. As enterprises accelerate AI adoption, we are entering uncharted territory where humans will no longer be the sole decision-makers, creators, or collaborators. This session explores the critical new skills and organizational capabilities needed to safely deploy, oversee, and scale hybrid human/agentic AI systems. We will examine how emerging regulations are reshaping expectations for transparency, explainability, and ethical alignment. But governance alone is not enough. Human teams must develop new roles—AI risk stewards, model behavior auditors, and cognitive ethicists—to ensure these agents operate without bias, hallucination, or unintended escalation. To meet this challenge, we must also define new career paths that grow the skills needed to lead hybrid teams and rethink early career roles that can serve as feeders into AI governance and oversight disciplines. In a future where machines continuously learn and evolve, leadership must be redefined for the age of intelligent agents.

Stuck in endless AI conversations? Learn real-life tips to break through. Get materials to improve human-in-the-loop AI and automate pipelines that lead to decisions—not just dashboards.