Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches [Correction added on Aug 30, 2020, after first online publication: Affiliation of author "Satya S. Sahoo" was changed and the rest of the affiliations were renumbered accordingly.] 1870 | LHATOO eT AL. 1 | INTRODUCTION Big data is an intuitive, colloquially used term 1-first in business, and latterly in science and health care. MetaGroup's 2014 definition describes big data as high-volume, high-velocity, and high-variety information assets that demand costeffective, innovative forms of information processing for enhanced insight and decision-making. In addition to these "3 V's," 2 the fourth "V" of data veracity is particularly pertinent, because suspect data produce suspect conclusions (Figure 1). In an era of unprecedented collaboration and resource pooling, big data's promise is both inviting and challenging, especially in epilepsy due to its inherent heterogeneity and the vast array of scientific disciplines it involves. This review examines big data aspects specific to epilepsy and describes the current state of the art as well as future directions. 2 | THE MEANING OF BIG DATA In epilepsy, a plethora of disparate data drive variety (phenotype, genotype, video-electroencephalographic [EEG], extracranial and intracranial physiological signal, structural to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data.