In modern AI systems, “high-quality data” is more than accuracy - it’s about relevance, diversity, governance, and adaptability over time. This panel brings together data scientists, engineers, and AI leaders to define what quality truly means in the era of generative and agentic AI. Panelists will share practical methods for building robust data pipelines, leveraging synthetic data, and implementing governance frameworks that support scalable, compliant, and high-performing AI workloads.