Real-world data is data relating to a patient health status or the delivery of healthcare collected during the course of clinical care and captured in a variety of data sources. Given the increasing availability of real-world health data, the development of credible analyses to help fill the knowledge gap between clinical trials and actual clinical practice is more important than ever. This paper discusses the expanding use of real-world evidence in healthcare decision-making, as well as the importance of clean and reliable data, robust disease identification algorithms, consistent patient attribution methodologies, and relevant outcomes metrics that can be generated from reliably reported data.