DATA festival Munich
The data and AI topics we’ll explore in Munich
Date: March 17 – 18, 2027
Muffatwerk, Munich
Early-bird ticket: €790 plus VAT
What will DATA festival Munich 2027 cover?
Explore the technical, organizational and strategic choices behind data and AI in practice: where they create measurable business value, what it takes to run them reliably, and how teams manage risks, trade-offs and organizational change.
Agentic AI & GenAI in Production
Where do agents add value, and when are deterministic workflows or traditional AI methods the better choice? Explore use-case selection, hybrid approaches, evaluation, guardrails and the operational costs of running GenAI and agentic systems in production, including how teams validate business value before scaling.
Physical & Industrial AI
Explore AI applications in manufacturing, robotics and other physical environments. What data, integration and operational requirements must teams address, and how can they assess the business value of industrial AI use cases?
AI Governance, Security & Accountability
From the EU AI Act to agent autonomy: how do teams define responsibilities, establish accountability when AI systems act, manage access and maintain human oversight? Explore practical controls for responsible AI, including security risks such as prompt injection, protecting organizational knowledge as agents interact within and across organizations, and the governance of citizen and AI-assisted development.
Data & AI Strategy, Culture & Organization
How do data and AI initiatives translate into measurable business value? Explore operating models, adoption, data literacy and changing roles and skills, with a focus on connecting technical capabilities to business needs. How can organizations bridge adoption gaps and support teams through rapid change?
Data Products, Data Engineering & AI-Ready Data
Build the foundations for analytics, GenAI and agents: data products, pipelines, platforms, quality and observability. Explore how to make enterprise data usable in production and provide the business context AI applications need.
Data & AI Sovereignty
What control should organizations retain over their data, AI systems and infrastructure? Explore cloud and deployment choices, data residency, open source and vendor dependencies, including how much control is practical when relying on external AI platforms and agents, balancing control with operational needs.
Data Science, Machine Learning & Analytics
From predictive models and optimization to operational decisions: explore forecasting, optimization, self-service and AI-assisted analytics. Compare these approaches with GenAI and agent-based methods: how do teams deploy and maintain models, measure their impact and help people act on the results?
AI-Assisted & Agentic Software Development
How do AI coding tools and agents change the way teams build and maintain software? Explore development workflows, testing and quality assurance, alongside the evolving responsibilities of developers, product owners and architects. Examine how roles and delivery practices may need to change, whether established agile approaches can keep pace, and how teams assess quality and productivity.
Go deeper on the topics that matter to you.
Submit your own topic and bring it to the table.
Any questions? Contact us!
