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We make sense of the world around us by turning data into information. For years, research in fields such as machine learning (ML), data mining, databases, information retrieval, natural language processing, and speech recognition have steadily improved their techniques for revealing the information lying within otherwise opaque datasets. But computer science is now on the verge of a new era in data analysis because of several recent developments, including: the rise of the warehouse-scale computer, the massive explosion in online data, the increasing diversity and time-sensitivity of queries, and the advent of crowdsourcing. Together these trends — often referred to collectively as Big Data — have the potential for ushering in a new era in data analysis, but to realize this opportunity requires us to confront several significant scientific challenges. This talk will discuss some of these challenges in the context of academic and industrial research in the United States.