 Data-driven material science has been rapidly expanding over the past few years due to advances in open science, national funding, and technological developments. This includes the creation of materials databases, machine learning, and high throughput methods. Despite these advancements, there are still many challenges that need to be addressed before the field can reach its full potential. These include data veracity, integration of experimental and computational data, data longevity, standardization, and the gap between industry and academia.