Over 90% of the world's information is now unstructured, growing at a rate of 55-65% per year. This data is crucial for building AI-powered applications, but many challenges arise from its quality rather than algorithmic sophistication. Effective data scientists rely on intuition about their domain and data nuances to understand dataset curation strategies. However, curating datasets often involves more than just sampling data uniformly across labels, as it requires identifying necessary features to cover the variation in unstructured data. Domain expertise is also essential for understanding model behavior, annotating data, and fixing long-tail errors. With deep learning algorithms becoming commoditized, businesses are shifting focus from algorithmic improvements to dataset quality and using tools like Galileo to proactively find and fix errors in their datasets, ensuring models perform optimally in production.