The ML data problem is a significant challenge in building and deploying machine learning models, with errors and biases creeping into datasets and causing catastrophic repercussions. Data curation is complex and often overlooked, leading to model blindspots. Labels are also prone to error, and reused datasets can lead to data staleness. The lack of tools to address these issues has hindered the development of machine learning in enterprises, but a new solution called Galileo aims to provide insights and answers necessary to rapidly identify and fix data errors.