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.

Data governance can feel like rules slowing you down – but done well, it does the opposite. It gives clarity, builds trust, and lets teams move faster with less risk.

This interactive session will explore how to make governance practical, human, and useful in your day-to-day work. We’ll focus on the real decisions people make every day, how to clarify ownership, and how to overcome common blockers without adding unnecessary overhead.

Through scenario-based exercises and a simple governance mapping activity, you’ll leave with actionable steps to take back to your organisation – helping you turn governance from a box-ticking exercise into a genuine business enabler.

Following on from the Building consumable data products keynote, we will dive deeper into the interactions around the data product catalog, to show how the network effect of explicit data sharing relationships starts to pay dividends to the participants. Such as:

For the product consumer:

• Searching for products, understanding content, costs, terms and conditions, licenses, quality certifications etc

• Inspecting sample data, choosing preferred data format, setting up a secure subscription, and seeing data provisioned into a database from the product catalog.

• Providing feedback and requesting help

• Reviewing own active subscriptions

• Understanding the lineage behind each product along with outstanding exceptions and future plans

For the product manager/owner:

• Setting up a new product, creating a new release of an existing product and issuing a data correction/restatement

• Reviewing a product’s active subscriptions and feedback/requests from consumers

• Interacting with the technical teams on pipeline implementations along with issues and proposed enhancements

• For the data governance team

• Viewing the network of dependencies between data products (the data mesh) to understand the data value chains and risk concentrations

• Reviewing a dashboard of metrics around the data products including popularity, errors/exceptions, subscriptions, interaction

• Show traceability from a governance policy relating to, say data sovereignty or data privacy to the product implementations.

• Building trust profiles for producers and consumers

The aim of the demonstrations and discussions is to explore the principles and patterns relating to data products, rather than push a particular implementation approach.

Having said that, all of the software used in the demonstrations is open source. Principally this is Egeria, Open Lineage and Unity Catalog from the Linux Foundation, plus Apache Airflow, Apache Kafka and Apache SuperSet from the Apache Software Foundation.  

Videos of the demonstrations will be available on YouTube after the conference and the complete demo software can be downloaded and run on a laptop so you can share your experiences with your teams after the event.

Explore how AI and data governance create a positive feedback loop that benefits both. Learn actionable strategies from real case studies to transform your data governance practices and maximise value for your organisation's success.

Data Governance is broodnodig voor datagedreven werken. Maar het is vaak het domein van dikke beleidsstukken, verplichte velden en verdwaalde Excelletjes. Wat als je het eens helemaal ander, leuker en makkelijker aanpakt? In deze sessie nemen we je mee in het verhaal van Gemeente ’s Hertogenbosch, die haar Dataplatform niet alleen technisch heeft ingericht, maar deze ook daadwerkelijk heeft verbonden met de rest van de organisatie.

AI is redefining the future. Technology is changing faster than ever; people have new ways of interacting with technology, and organizations are adapting and adopting this change. However, Trusted AI can only be built on trusted data. We will dive deep into how AWS is helping customers build a trusted data foundation as they embark on their AI journey to build outcomes that are tailored to their business needs. HEMA will present their journey towards a strong Data Platform and Data Governance strategy on AWS, and the business outcomes they achieved.

Scaling a data platform faces many pitfalls and risks. To be successful you need to cover 3 bases: build on foundation of data governance, establish transparency and insights, design for user experience.

Naast de Data Governance Act (DGA, sinds september 2023) geldt vanaf september 2025 ook de Dataverordening. De ACM houdt toezicht op deze regels. Deze Europese wetten moeten veiliger en eerlijker datadelen mogelijk maken. In deze presentatie bespreken we de belangrijkste regels, schetsen we de kansen voor bedrijven en geven we aan waar de ACM in haar toezicht de nadruk op legt. Meer info: https://www.acm.nl/nl/digitale-economie/data

Organizations are creating and managing more data than ever. As stewards of this data, we are tasked with ensuring that it is highly available, secure from threats, and only accessible to those that it is intended for.

This session dives into the many areas that keep security officers awake at night, including: • Principle of least privilege • Data governance • Data compliance laws and regulations • Common exploits • Security best practices for developers • Encryption • Industry-specific security guidelines

As data platforms grow and evolve, the benefit of centralizing and standardizing security solutions is greater than ever. The frequency of data breaches has increased over time, and despite continuing to improve our security posture, the complexity and effectiveness of attacks continues to keep pace.

Data security is a key implementation of risk management. All organizations are targeted by cyber threat actors. Success is dissuading those malicious parties from persisting in their attacks. Knowing how to effectively layer security and create effective access methods between users and data will provide the highest chances of success given an ever-changing threat landscape.

Please note that we will be using Microsoft Teams for the online portion of this meeting. You may want to join a few minutes early to ensure you do not have any issues. If you are attending in person, there are large TVs at the office, and you do not need to bring a laptop or use Teams.

There's no better time to become a data engineer. And acing the AWS Certified Data Engineer Associate (DEA-C01) exam will help you tackle the demands of modern data engineering and secure your place in the technology-driven future. Authors Sakti Mishra, Dylan Qu, and Anusha Challa equip you with the knowledge and sought-after skills necessary to effectively manage data and excel in your career. Whether you're a data engineer, data analyst, or machine learning engineer, you'll discover in-depth guidance, practical exercises, sample questions, and expert advice you need to leverage AWS services effectively and achieve certification. By reading, you'll learn how to: Ingest, transform, and orchestrate data pipelines effectively Select the ideal data store, design efficient data models, and manage data lifecycles Analyze data rigorously and maintain high data quality standards Implement robust authentication, authorization, and data governance protocols Prepare thoroughly for the DEA-C01 exam with targeted strategies and practices