Medallion architecture is a popular pattern for progressively refining data, but successful implementation takes more than setting up Bronze, Silver and Gold. Piethein Strengholt shares practical best practices, do’s and don’ts, and a concrete implementation roadmap. He also examines Medallion through the lens of AI. A practical session for anyone looking to apply Medallion effectively and think beyond the familiar three-layer model.
Organizations are increasingly required to share data with one another and with government bodies. This trend is largely driven by Europe, which emphasizes the removal of legal and technical barriers to data exchange. Data spaces (or data ecosystems) are being established for specific sectors. Various data ecosystems are also emerging in the Netherlands—such as the Federated Data Ecosystem (*Federatief Datastelsel*), which focuses on the reuse of data within the public sector. Danny Greefhorst provides an overview of these developments and clarifies what they mean in practical terms for your organization. After this session, you will know which developments to monitor, what choices need to be made regarding your data architecture, and where to begin.
Stakeholders and data teams speak different languages, and it shows in requirements that take months, arrive ambiguous and still don’t tell the team what to build. In this hands-on workshop Shane Gibson teaches a pattern template for capturing the requirements for one Data Product in 30 minutes, in a shared language using the Information Product Canvas. Half the day is spent completing real canvases in small groups. Leave able to run it with your own stakeholders on Monday.
That’s not quite what I wanted.” is unfortunately still heard. This session shows you how to change your Information Value Stream. Starting from a completed Information Product Canvas, a light but well-formed set of requirements captured in 30 minutes, Shane shows how to use common GenAI tools to generate a working prototype and put it in front of the stakeholder in hours, not months.
Almost every day, articles appear warning that AI can only be successfully implemented if organizations have their data and metadata in order. Unfortunately, many authors fail to specify exactly what needs to be done. The crucial follow-up question “What does an AI-ready data architecture look like?” often remains unanswered.
It has become obvious and generally accepted now that AI agents can’t function properly without enough context. As organizations scale up their AI use and strive for new, agentic workflows, various technical and architectural solutions for context management have emerged. But what do these semantic layers, context planes, and knowledge management systems actually contain? And where do we get all that context from?
Discover why Data Mesh and “Data as a Product” continue to thrive beyond the AI hype. This session explores how the Linux Foundation’s Open Data Product Specification turns vision into practice, covering architecture, governance, lifecycle management, and key lessons for building trustworthy, scalable data products.
