 Data-driven material science has been rapidly expanding over the past few decades due to advances in open science, national funding, and technological developments. This includes the creation of materials databases, machine learning, and high throughput methods, which have become integral components of the materials research tool set. Despite these advancements, however, several challenges remain, such as data veracity, integration of experimental and computational data, data longevity, standardization, and the gap between industry and academia. This paper reviews the history of data-driven material science, discusses key successes and challenges, and provides a perspective on the future of the field. This article was authored by Lori Himanan, Amber Schertz, Adam Stewart Foster, and others.