The vast amount of health data generated and stored around the world each day offers significant opportunities for advances such as the real-time tracking of diseases, predicting disease outbreaks, and developing health care that is truly personalized. However, capturing, analyzing, and sharing health data is difficult, expensive, and controversial. This article explores four central questions that policy makers should consider when developing public policy for the use of "big data" in health care. We discuss what aspects of big data are most relevant for health care and present a taxonomy of data types and levels of access. We suggest that successful policies require clear objectives and provide examples, discuss barriers to achieving policy objectives based on a recent policy experiment in the United Kingdom, and propose levers that policy makers should consider using to advance data sharing. We argue that the case for data sharing can be won only by providing reallife examples of the ways in which it can improve health care.