 Data-driven science is revolutionizing materials science by utilizing data as a new resource to discover and improve materials or phenomena through tools such as machine learning and high throughput methods, but challenges such as data veracity, integration, longevity, standardization, and the gap between industrial and academic interests impede progress. This article was authored by Lori Himinan, Amber Jertz, Adam Stewart Foster, and others.