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.

Learn how to transform your data warehouse for AI/LLM readiness while making advanced analytics accessible to all team members, regardless of technical expertise. 

We'll share practical approaches to adapting data infrastructure and building user-friendly AI tools that lower the barrier to entry for sophisticated analysis. 

Key takeaways include implementation best practices, challenges encountered, and strategies for balancing technical requirements with user accessibility. Ideal for data teams looking to democratize AI-powered analytics in their organization.

AI is revolutionising industries, and regulation is rising to meet the moment. From empowering smarter decisions to enhancing customer experience, AI is an exciting tool driving transformation across media, finance, and data privacy.

In this energising 30-minute lightning talk session, senior leaders from Ofcom, Financial Conduct Authority (FCA), and Information Commissioner’s Office (ICO) will share how they’re embracing AI’s potential while guiding its responsible growth.  

Expect content to include real-world examples, emerging policy trends, and candid perspectives from those leading the charge toward an AI-powered future. This session will explore how regulators are shaping inclusive, ethical frameworks to unlock innovation while protecting public trust.

Powered by: Women in Data®

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 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.

As AI reshapes every aspect of data management, organizations worldwide are witnessing a fundamental transformation in how data governance operates. This panel discussion, hosted by DataHub, brings together two forward-thinking customers to explore the revolutionary journey from traditional governance models to AI-autonomous systems. Our expert panelists will share real-world experiences navigating the four critical stages of this evolution: AI-assisted governance, where machine learning augments human decision-making; AI-driven governance, where algorithms actively guide policy enforcement; AI-run governance, where systems independently execute complex workflows; and ultimately, AI-autonomous governance, where intelligent systems self-manage and continuously optimize data stewardship processes. Through candid discussions of implementation challenges, measurable outcomes, and strategic insights, attendees will gain practical understanding of how leading organizations are preparing for this transformative shift. The session will address key questions around trust, accountability, and the changing role of data professionals in an increasingly automated governance landscape, providing actionable guidance for organizations at any stage of their AI governance journey.

This high-energy session will showcase 15 real AI use cases in just 30 minutes—all powered by Alteryx ONE. Discover how Alteryx ONE acts as the AI Data Clearinghouse, turning fragmented, messy data into trusted, governed inputs that make AI practical, scalable, and impactful.

We’ll explore applications across Controlling, Tax, Procurement, Marketing, Legal, and Support. See how analysts and data scientists can move from idea to execution faster with rapid prototyping of workflows and use cases. And with inbuilt AI capabilities, making your data speak has never been easier—transform insights into compelling emails, presentations, and messages in seconds.

Expect fast, practical takeaways—no fluff—ready to apply directly in your workflows.

We are entering an Age of Artificial Intelligence with unprecedented opportunities. Companies are integrating AI-driven solutions to enhance efficiency, drive innovation, and maintain a competitive edge. However, prevailing myths about AI create uncertainty in strategic decision-making and adoption. We will discuss four foundational myths in our AI centric world: 1) regulation is an innovation killer; 2) scaling current models will lead to Artificial General Intelligence (AGI); 3) general models create maximum value; and 4) the value of data is unlimited. We will show these myths are delaying AI progress and provide research in overcoming their challenges.

Legacy data tools weren’t built for the AI era. Agentic Data Management replaces static rules and siloed platforms with intelligent agents that monitor, reason, and act—automating quality, governance, and lineage at scale. Discover how data leaders are shifting from manual firefighting to autonomous control, powering faster, trusted, and scalable data for AI and analytics.

- See a live demo of an agentic system in action

- Learn how probabilistic and deterministic approaches work in concert

- Explore how to build intelligent data products using the MCP protocol

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.

Data teams know the pain of moving from proof-of-concepts to production. We’ve all seen brittle scripts, one-off notebooks, and manual fixes turn into hidden risks. With large language models, the same story is playing out, unless we borrow the lessons of modern data engineering.

This talk introduces a declarative approach to LLM engineering using DSPy and Dagster. DSPy treats prompts, retrieval strategies, and evaluation metrics as first-class, composable building blocks. Instead of tweaking text by hand, you declare the behavior you want, and DSPy optimizes and tunes the pipeline for you. Dagster is built on a similar premise; with Dagster Components, you can build modular and declarative pipelines.

This approach means:

- Trust & auditability: Every LLM output can be traced back through a reproducible graph.

- Safety in production: Automated evaluation loops catch drift and regressions before they matter.

- Scalable experimentation: The same declarative spec can power quick tests or robust, HIPAA/GxP-grade pipelines.

By treating LLM workflows like data pipelines: declarative, observable, and orchestrate, we can avoid the prompt spaghetti trap and build AI systems that meet the same reliability bar as the rest of the stack.

AI is only as strong as the data beneath it. Yet most enterprises still rely on fragmented tools and reactive processes that undermine trust. The result: innovation that looks impressive in demos but collapses under real-world pressure. In this keynote, Salma Bakouk, CEO of Sifflet, argues that metadata, not models, is the true foundation for the AI era. By building a metadata control plane enriched with agentic observability, enterprises can move from reactive patchwork to proactive intelligence. In this keynote, she offers a provocative vision of where the market is heading, what traditional approaches are getting wrong, and why the winners of the AI economy will be those who treat trust not as insurance, but as infrastructure.

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.

We are entering the Era of Experience, where AI agents will transform customer journeys by learning directly from interactions. But most customer-facing agents today are “senseless,” lacking the real-time context needed to deliver relevant, empathetic, and valuable experiences. This session will explore how real-time streaming architectures and proprietary customer data can power the next generation of intelligent, perceptive agents.

Join Snowplow’s Jon Su as he unpacks:

• Why brands risk commoditization if they rely on third-party agents
• How real-time context enables smarter, more personalized customer interactions
• The key ingredients for building agents that perceive, adapt, and self-optimize
• How Snowplow Signals provides the real-time customer intelligence foundation for agentic applications

Discover how to shift from static personalization to adaptive, agent-driven experiences that improve customer satisfaction, loyalty, and business outcomes.

How do you prepare a global industrial business for AI? At Secil, the answer was data governance. In this session, Ricardo Carvalho shares how the team replaced siloed systems with a unified data platform using Domo, delivering enterprise-level analytics, smarter operations, and a foundation for scalable AI that drives real outcomes in just 18 months.

Millions has been poured into data and AI, but the returns often fall short of the promise. In this fireside chat, Jason Foster and Helen Louwrens will get under the skin of the ROI gap - why business cases can feel like guesswork, who should really own the number, and the messy politics of attributing value. We’ll also tackle the tough question: how to meaningfully measure ROI on data investments despite the challenges, and explore whether the gap can ever truly be closed, or if we need to rethink what “value” really means.

AI innovation is accelerating, and your opportunity to lead is here. With the upcoming arrival of Dell Pro Max with GB10, powered by NVIDIA Grace Blackwell architecture, you no longer have to wait for the future of AI development; you can experience it first, right at your desk.

Join this session to discover how purpose-built, enterprise-class AI compute, compact enough for your workspace, yet powerful enough to drive breakthrough results, can transform your organisation’s AI productivity

We need to tie our data agents to the infrastructure where our data runs. The issue is that the industry creating more of the same that we have all come to expect: loosely defined tooling with overlapping functionality. Is there any other way for AI systems to mediate work? Let us show you how, live.

Data is one of the most valuable assets in any organisation, but accessing and analysing it has been limited to technical experts. Business users often rely on predefined dashboards and data teams to extract insights, creating bottlenecks and slowing decision-making.

This is changing with the rise of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). These technologies are redefining how organisations interact with data, allowing users to ask complex questions in natural language and receive accurate, real-time insights without needing deep technical expertise.

In this session, I’ll explore how LLMs and RAG are driving true data democratisation by making analytics accessible to everyone, enabling real-time insights with AI-powered search and retrieval and overcoming traditional barriers like SQL, BI tool complexity, and rigid reporting structures.