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.

Governance thrived on human nuance. But when AI’s the main consumer, what does ‘understanding’ even mean now?

In today’s financial sector, the continuous accuracy and reliability of machine learning models are crucial for operational efficiency and effective risk management. With the rise of MLOps (Machine Learning Operations), automating monitoring mechanisms has become essential to ensure model performance and compliance with regulations. This presentation introduces a method for continuous monitoring of model drift, highlighting the benefits of automation within the MLOps framework. This topic is particularly interesting because it addresses a common challenge in maintaining model performance over time and demonstrates a practical solution that has been successfully implemented in the bank.

This talk is aimed at data scientists, machine learning engineers, and MLOps practitioners who are interested in automating the monitoring of machine learning models. Attendees will be guided on how to continuous monitor model drift within the MLOps framework. They will understand the benefits of automation in this context, and gain insights into MLOps best practices. A basic understanding of MLOps principles, and statistical techniques for model evaluation will be helpful but not strictly needed.

The presentation will be an informative talk with a focus on the design and implementation. It will include some mathematical concepts but will primarily be demonstrating real-world applications and best practices. At the end we encourage you to actively monitor model drift and automate your monitoring processes to enhance model accuracy, scalability, and compliance in your organizations.

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®

See how GenAI + graph algorithms enable AI agents to reason, learn, and uncover insights—beyond static data or simple retrieva

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.

Microlog is a lightweight continuous profiler and logger for Python that helps developers understand their applications through interactive visualizations and AI-powered insights. With extremely low overhead and a 100% Python stack, it makes it easy to trace performance issues, debug unexpected behavior, and gain visibility into production systems.

Card testing is one of the largest growing fraud problems within the payments landscape, with fraudsters launching millions of attempts globally each month. These attacks can cost companies thousands of euros in lost revenue and lead to the distribution of private card details. Detecting this type of fraud is extremely difficult without confirmed labels to train standard supervised ML classifiers. In this talk, we’ll describe how we built a production-ready ML model that now processes hundreds of transactions per second and share the key take-aways from our journey.

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.

Explore the evolution from traditional to AI-autonomous data governance, with real-world insights from DataHub customers.

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.

We’ll explore applications across Controlling, Tax, Procurement, Marketing, Legal, and Support.

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.