In data-driven materials science, experts analyze big and complex data to find new or better materials and understand material behavior. They use materials databases, machine learning, and high-throughput methods to help in this process. Open science, funding, and technology advances have pushed this field forward. Challenges include making sure data is accurate, combining experimental and computer data, maintaining data quality, creating standards, and connecting industry needs with academic research. This area has evolved from open science to bigger data collections for materials research. Researchers find successes and problems, giving insights into where the science is heading in the future.