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.
Unlock the future of trustworthy AI. This seminar helps you connect AI Governance, Responsible AI, and Data Governance into one actionable framework. Learn how to mitigate risks, ensure compliance, and embed transparency, fairness, and accountability in AI initiatives. Packed with real-world examples and practical tools, this session is ideal for leaders who want to align innovation with ethics and create lasting business value.
Data Mesh is something every organization talks about but few are actually doing. This framework for federated data management and governance promises a lot and demands even more, but some elements in the paradigm can be beneficial for every organization: especially, data products & domains. Thinking in terms of products and domains also demands new approaches from data modeling – join this session to find out how data modeling works in the Data Mesh!
We are used to managing data before deploying AI: carefully collecting, cleaning and structuring it. But that is changing. AI now helps to improve data itself: automatically enriching, validating, integrating and documenting it. We are moving from static management to dynamic improvement: AI brings data to life and changes how we deal with it.