Yetkin Ozkucur

For decades, data modeling lived quietly in the back office — essential, but overlooked. AI has changed that. Today, data modeling is no longer a technical afterthought — it is the difference between AI that works and AI that hallucinates, breaks, or fails compliance. Yet most organizations are still applying yesterday’s modeling practices to tomorrow’s AI problems. This session challenges that mindset. We’ll explore how data modeling is evolving into a strategic, AI-era discipline — powering trusted data products, enabling semantic clarity for GenAI, and more! This is not your grandfather’s data modeling. Learn how modern approaches — augmented by automation, metadata intelligence, and AI — are transforming modeling from static documentation into a dynamic, living system that drives business outcomes. See how solutions like Quest Erwin Data Modeler help organizations move faster, reduce risk, and turn data models into a competitive advantage in the age of AI.

Step 12 months into the future - where AI agents create data products, enforce rules, and act in real time. In that world, anyone can request a new data product with a simple form and natural language prompt: “I need to predict customers likely to drop after purchasing” - and get it instantly. Who controls the pace of production so value isn’t buried under maintenance? Are data products even relevant in this new paradigm? How are AI-built assets folded back into a trusted delivery lifecycle? This session reverse-engineers what might soon be reality, exposing the issues these capabilities could bring - and the guardrails needed to keep them in check.