Data Quality in the Age of AI and Beyond

Learn how to build a data ecosystem that's ready for AI and future challenges. This webinar explores the critical role of data quality in AI success and provides practical guidance for preparing your data infrastructure for the demands of machine learning and generative AI. ## Key Takeaways AI models are only as good as the data they're trained on. This session covers the specific data quality requirements for AI workloads, including completeness, accuracy, consistency, and bias detection. Speakers share real-world examples of how poor data quality has derailed AI projects and how proactive quality management prevents these failures. The webinar also looks ahead to emerging challenges, including data quality for large language models, synthetic data generation, and the governance requirements of responsible AI development.