It’s no secret that AI is reliant on ‘rock solid’ data. However given the vast amounts of data that companies now have spread across a distributed SaaS, on-premises and multi-cloud data estate, many companies they are a million miles away from this. We are also well past the point where people can govern data on their own. They need help and a total rethink is now needed to conquer data complexity and create a high quality, compliant data foundation for AI Success.

In this watershed keynote, conference char Mike Ferguson details what needs to be done to govern data in the era of AI, how companies can conquer the complexity they face, by implementing an always on, active and unified approach to data governance to continuously detect, automate and consistently enforce multiple types of policies across a distributed data estate. The session will cover:

• Current problems with data governance today and why old approaches are broken

• Requirements to dramatically improve data governance using AI and AI automation

• The need for an integrated and unified data governance platform

• Why a data catalog, data intelligence, data observability, AI Agents and orchestration all need to be integrated for AI-Assisted active data governance

• Understanding the AI-assisted data governance services and AI-Agents you need

• Establishing health metrics to measure effectiveness of your data governance program

• Creating a Data Governance Action Framework for your enterprise

• Monitoring the health and security of your data using data governance observability

• Enabling continuous reporting and AI-Assisted data governance action automation

• Implementing data governance AI Agents for different data governance disciplines

While 95% of enterprise AI pilots fail to deliver business value, Secoda's customers are seeing a different reality: with 76% of AI usage focused on core business intelligence workflows rather than isolated experiments. 

Join Etai Mizrahi, Co-Founder & CEO of Secoda, as he shares how companies like Dialpad achieved company-wide AI adoption by moving 200+ employees from traditional dashboards to natural language analytics. Learn how Secoda's multi-agent AI architecture transforms data governance from manual overhead into automated workflows, and learn practical strategies for scaling AI beyond pilots to become essential infrastructure that delivers measurable ROI.

When Virgin Media and O2 merged, they faced the challenge of unifying thousands of pipelines and platforms while keeping 25 million customers connected. Victor Rivero, Head of Data Governance & Quality, shares how his team is transforming his data estate into a trusted source of truth by embedding Monte Carlo’s Data + AI Observability across BigQuery, Atlan, dbt, and Tableau. Learn how they've begun their journey to cut data downtime, enforced reliability dimensions, and measured success while creating a scalable blueprint for enterprise observability.

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.

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.

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.

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.

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.

As organisations scale their data ecosystems, ensuring consistency, compliance, and usability across multiple data products becomes a critical challenge. This session explores a practical approach to implementing a Data Governance framework that balances control with agility.

Key takeaways:

- We will discuss key principles, common pitfalls, and best practices for aligning governance with business objectives while fostering innovation.

- Attendees will gain insights into designing governance policies, automating compliance, and driving adoption across decentralised data teams.

- Real-world examples will illustrate how to create a scalable, federated model that enhances data quality, security, and interoperability across diverse data products.

Three out of four companies are betting big on AI – but most are digging on shifting ground. In this $100 billion gold rush, none of these investments will pay off without data quality and strong governance – and that remains a challenge for many organizations. Not every enterprise has a solid data governance practice and maturity models vary widely. As a result, investments in innovation initiatives are at risk of failure. What are the most important data management issues to prioritize? See how your organization measures up and get ahead of the curve with Actian.

For years, data engineering was a story of predictable pipelines: move data from point A to point B. But AI just hit the reset button on our entire field. Now, we're all staring into the void, wondering what's next. While the fundamentals haven't changed, data remains challenging in the traditional areas of data governance, data management, and data modeling, which still present challenges. Everything else is up for grabs.

This talk will cut through the noise and explore the future of data engineering in an AI-driven world. We'll examine how team structures will evolve, why agentic workflows and real-time systems are becoming non-negotiable, and how our focus must shift from building dashboards and analytics to architecting for automated action. The reset button has been pushed. It's time for us to invent the future of our industry.

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.

Data governance often begins with Data Defense — centralized stewardship focused on compliance and regulatory needs, built on passive metadata, manual documentation, and heavy SME reliance. While effective for audits, this top-down approach offers limited business value. 

Data Governance has moved to a Data Offense model to drive Data Monetization of Critical Data Assets in focusing on analytics and data science outcomes for improved decision-making, customer and associate experiences. This involves the integration of data quality and observability with a shift-left based on tangible impact to business outcomes, improved governance maturity, and accelerated resolution of business-impacting issues.

The next iteration is to move to the next phase of Data Stewardship in advancing to AI-Augmented and Autonomous Stewardship — embedding SME knowledge into automated workflows, managing critical assets autonomously, and delivering actionable context through proactive, shift-left observability, producer–consumer contracts, and SLAs that are built into data product development.

For years, data engineering was a story of predictable pipelines: move data from point A to point B. But AI just hit the reset button on our entire field. Now, we're all staring into the void, wondering what's next. While the fundamentals haven't changed, data remains challenging in the traditional areas of data governance, data management, and data modeling, which still present challenges. Everything else is up for grabs.

This talk will cut through the noise and explore the future of data engineering in an AI-driven world. We'll examine how team structures will evolve, why agentic workflows and real-time systems are becoming non-negotiable, and how our focus must shift from building dashboards and analytics to architecting for automated action. The reset button has been pushed. It's time for us to invent the future of our industry.

Join Tom Pryor, Principal Data Engineer, as he shares how his team has harnessed the power of Snowflake to transform their data strategy into a robust, scalable foundation for digital innovation and AI enablement. This session will explore how Snowflake has unified data across the enterprise, enabling real-time insights, powering customer-facing digital applications, and laying the groundwork for advanced AI capabilities. Tom will walk through key architectural decisions, data governance practices, and the evolution from legacy systems to a modern data platform.

Many data teams are under pressure to organizations leverage AI, but few recognize that AI is only as powerful as the data behind it. In this session, industry veteran Adam Greco will use humor and storytelling to explore why companies often struggle to centralize customer data and how fragmented, team-owned datasets undermine AI’s potential. He will highlight how executives sometimes turn to AI as a shortcut, overlooking the fundamental need to fix core data challenges first.

Drawing on decades of experience, Adam will explain how AI inevitably magnifies the quality of your current data—accelerating either insights or errors. He will show how the urgency of AI adoption can serve as a catalyst for improving data governance, centralization, and accessibility. Once organizations establish a single source of truth, AI can be harnessed to deliver meaningful efficiencies, empower marketers with real-time access, and drive smarter decision-making across the enterprise.

Attendees will walk away with a practical understanding of why clean, centralized data is the foundation of AI success—and how to position AI as an enabler of transformation, not a distraction from data realities.

Institutions are drowning in complex, fragmented data ecosystems, slowing down AI adoption, increasing compliance risks, and making data governance a burden.

Without clear data lineage automation, organizations struggle with data trust, explainability, and operational inefficiencies. In this session, Adam Segal, Senior Solutions Engineer of Cloudera Octopai Data Lineage, will reveal how automated lineage and metadata intelligence can:

•  Unravel the data mess by mapping end-to-end lineage across hybrid environments
•  Ensure AI readiness with traceable, high-quality metadata flows
•  Simplify compliance with real-time visibility into data movement and ownership
•  Empower teams by making data accessible without IT bottlenecks 

Discover how leading organizations turn automated data lineage into a competitive advantage, ensuring data clarity, compliance, and increased adoption of AI-driven innovation.

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.

Penguin Random House, the world’s largest trade book publisher, relies on data to power every part of its global business, from supply chain operations to editorial workflows and royalty reconciliation. As the complexity of PRH’s dbt pipelines grew, manual checks and brittle tests could no longer keep pace. The Data Governance team knew they needed a smarter, scalable approach to ensure trusted data.

In this session, Kerry Philips, Head of Data Governance at Penguin Random House, will reveal how the team transformed data quality using Sifflet’s observability platform. Learn how PRH integrated column-level lineage, business-rule-aware logic, and real-time alerts into a single workspace, turning fragmented testing into a cohesive strategy for trust, transparency, and agility.

Attendees will gain actionable insights on:

- Rapidly deploying observability without disrupting existing dbt workflows

- Encoding business logic into automated data tests

- Reducing incident resolution times and freeing engineers to innovate

- Empowering analysts to act on data with confidence

If you’ve ever wondered how a company managing millions of ISBNs ensures every dashboard tells the truth, this session offers a behind-the-scenes look at how data observability became PRH’s newest bestseller.

Data leaders sharing the good, the bad and the ugly about governing data across company boundaries and even outside of the company walls. Key topics to unlock are Data Governance, Data Products, AI for Data

In today’s rapidly evolving data landscape, organisations face increasing pressure to maintain control and sovereignty over their data. After a quick introduction to Apache Iceberg and Apache Polaris (Incubating), this session will dive into a real world use case demonstrating how these technologies can power a robust, governance focused data platform. We’ll explore strategies to secure access to data, discuss upcoming roadmap features like RBAC, FGAC, and ABAC, and show how to build custom extensions to tailor governance to your organisation’s needs.