Generative AI (Gen AI) is transforming industries, but its success hinges on the quality of the data fueling it. While the impact of data quality on traditional analytics and machine learning is well-understood, the stakes are even higher for Gen AI. Forrester's assertion that "Data Quality is Now the Primary Factor Limiting Gen AI Adoption" underscores the critical role of data in realizing the full potential of this transformative technology.Join Austin as he explores the specific challenges and opportunities posed by data quality in the context of Gen AI. From bias and fairness to accuracy and reliability, we'll delve into the key factors that can make or break a Gen AI project. Discover practical strategies and tools to help your organization navigate these complexities and move from proof of concept to enterprise-wide deployment.By the end of this session, you will have a deeper understanding of:

·       The unique data quality challenges specific to Gen AI

·       The critical role of data quality in ensuring reliable and ethical Gen AI models

·       Practical strategies for improving data quality and mitigating risks

·       Tools and techniques for effective data management in Gen AI environments