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.

Everyone talks about AI, but few are truly winning with it. In today’s fast-paced analytics landscape, dashboards and chatbot features aren’t enough. Winning in the age of AI takes more: a unified, governed platform that brings data, AI, and people together to drive real decisions at scale.

In this session, discover how Pyramid Analytics helps organizations cut through silos, hype, and complexity to deliver smarter, faster, and more trusted decisions across the enterprise.

AI agents are the next essential enterprise capability, but they bring new complexities in control, safety, and scale. In this interactive session, see how you can design, deploy, and govern agents alongside your existing analytics and models.

The path to AI enablement runs through governance. High-quality data, model transparency, and ethical oversight aren’t barriers — they are accelerators. In this talk, we’ll connect the dots between Data Governance and AI Governance, show how unified governance, helps embed new requirements to existing processes, while fostering innovation. We will discuss actionable steps to build AI-ready organisations that innovate with proper guardrails.

Analytics engineers are at a crossroads. Back in 2018, dbt paved the way for for this new kind of data professional, people who had technical ability and could understand business context. But here's the thing: AI is automating traditional tasks like pipeline building and dashboard creation. So then what happens to analytics engineers? They don't disappear - they evolve.

The same skills that made analytics engineers valuable also make them perfect for a new role I'm calling 'Analytics Intelligence Engineers.' Instead of writing SQL, they're writing the context that makes AI actually useful for business users.

In this talk, I'll show you what this evolution looks like day-to-day. We'll explore building semantic layers, crafting AI context, and measuring AI performance - all through real examples using Lightdash. You'll see how the work shifts from data plumbing to data intelligence, and walk away with practical tips for making AI tools more effective in your organization. Whether you're an analytics engineer wondering about your future or a leader planning your data strategy, this session will help you understand where the field is heading and how to get there.

This session will explore Merlin Entertainments’ data transformation journey in partnership with Telefónica Tech. Starting from a position of minimal governance and limited internal capability, Merlin Entertainments has built a federated data strategy, implemented agile data product teams, and delivered impactful use cases such as the Genie AI. This talk will highlight how Merlin Entertainments developed a partner-agnostic delivery model to embed expertise and drive business value. Attendees will gain practical insights into structuring data teams, overcoming transformation hurdles, and leveraging partnerships to innovate at scale.

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.

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.

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.

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

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. 

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.