• Stephen J Mooney, Daniel J Westreich, and Abdulrahman M El-Sayed.
• From the aDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY; and bDepartment of Epidemiology, Gillings School of Public Health, University of North Carolina, Chapel Hill, NC.
• Epidemiology. 2015 May 1;26(3):390-4.

AbstractBig Data has increasingly been promoted as a revolutionary development in the future of science, including epidemiology. However, the definition and implications of Big Data for epidemiology remain unclear. We here provide a working definition of Big Data predicated on the so-called "three V's": variety, volume, and velocity. From this definition, we argue that Big Data has evolutionary and revolutionary implications for identifying and intervening on the determinants of population health. We suggest that as more sources of diverse data become publicly available, the ability to combine and refine these data to yield valid answers to epidemiologic questions will be invaluable. We conclude that while epidemiology as practiced today will continue to be practiced in the Big Data future, a component of our field's future value lies in integrating subject matter knowledge with increased technical savvy. Our training programs and our visions for future public health interventions should reflect this future.

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