Description
What You’ll Learn
As generative AI technologies continue to evolve and permeate various industries, the old adage “garbage in, garbage out” is just as true as it’s ever been. In fact, ensuring data quality has become more critical than ever. In this webinar, guest speaker Michele Goetz from Forrester joins Kurt Muehmel from Dataiku to explore the unique challenges that arise in maintaining data quality, including issues of bias, accuracy, and relevance.
We’ll also cover practical best practices and strategies for ensuring data quality in the context of generative AI. Join us as we navigate this exciting yet complex terrain, equipping you with the knowledge and tools to ensure data quality in this new era of AI technology.
Key Topics:
An overview of generative AI and its impact on data quality
Key challenges in maintaining data quality in the age of generative AI
Opportunities presented by generative AI for improving data quality
Best practices for incorporating quality assurance processes into generative AI workflows
