• Von Hybrid-Modelle bis hin zu Data Mesh: die beste Lösung für verschiedene Sektoren?
• Richtige Data Governance Strategie implementieren (zentralisiert, federated Data Governance, u.a.)
• Daten-Domänenmodell und Kulturwandel bei einem datengetriebenen Unternehmen
• Elemente von Data Mesh verstehen und Nutzen: Data-as-a-Product, Data Self Service, Data Sharing und weitere wichtige Konzepte
• Wertsteigerung durch Datenprojekte messen und ROI berechnen

• What technologies fit best? DWH vs. Data Lake vs. Data Lake House
• Setting the base frame and connecting to strategy
• Connecting the engineering teams
• Success strategies towards next steps of implementation
• Domain-Oriented Data Ownership, Scalability and Performance, Federated Computational Governance

AI is taking an ever more important role in modern data-driven enterprises and the importance of having high quality and available data increases constantly. Until now the architectural and governance aspects of data and AI development were rather separate, since AI was developed through separate data science projects. However, with the new era of LLMs and ever-increasing complexity and demands on continuous development that might need to change in the future. In this discussion we talk about:

• How AI projects are developed, what data requirements are needed and communication between various stake holders
• The increasing demands and complexity in the AI strategy, the need for continuous development of LLM models and how this impacts the requirements on the data infrastructure
• Communication between AI leaders and Data Architecture leaders, common understandings and mindset shifts
• After the hype of Gen AI, what are the next steps in ensuring a proper data and governance foundation for implementing a long-term vision for an AI-driven company, which includes regulations (AI Act) and rules for ethical and responsible use