I argue that seven major claims arise from a consideration of the emergence of "the age of big data [that] is coming of age" in literary studies and that is "making it suddenly possible to see more and learn faster" (Lohr 2013:3). These claims are presented, often provocatively, under the following headings: (1) Big and distant are better than small and close; (2) Burn the canon; (3) The future of literary studies is information not evaluation; (4) Machine reading extends human reading; (5) Let's be scientific, above all else; (6) What matters is form; (7) The beauty is in the analysis. Each of these seven claims is critically assessed, with the work of Franco Moretti and Matthew L. Jockers providing the key points of focus in a discussion that seeks to engage with the foundational theoretical assumptions underpinning such approaches. Big data literary studies do not simply promise to do better than what was done before by literary criticism but to radically challenge its key assumptions and objectives, especially in terms of the relation between the literary scholar and his object of study, literature. The implications of this shift for our study of literature are discussed in detail.