This essay discusses the use of big data analytics (BDA) as a strategy of enquiry for advancing information systems (IS) research. In broad terms, we understand BDA as the statistical modelling of large, diverse, and dynamic data sets of usergenerated content and digital traces. BDA, as a new paradigm for utilising big data sources and advanced analytics, has already found its way into some social science disciplines. Sociology and economics are two examples that have successfully harnessed BDA for scientific enquiry. Often, BDA draws on methodologies and tools that are unfamiliar for some IS researchers (e.g., predictive modelling, natural language processing). Following the phases of a typical research process, this article is set out to dissect BDA's challenges and promises for IS research, and illustrates them by means of an exemplary study about predicting the helpfulness of 1.3 million online customer reviews. In order to assist IS researchers in planning, executing, and interpreting their own studies, and evaluating the studies of others, we propose an initial set of guidelines for conducting rigorous BDA studies in IS.
With in-memory technology, all data and applications are kept in the computer's main memory to avoid expensive mechanical hard-drive I/O access, reduce latency times, and increase the ability to process large volumes of data or complex data. In this "innovation and novel concepts" article, we discuss how in-memory technology may create business value. Based on our experiences in collaborating with the Hilti Corporation, one of the first adopters of SAP's in-memory technology appliance (SAP HANA), we describe and discuss illustrative application scenarios that are made possible through the increased computing power offered by in-memory technology. Based on these scenarios, we identify principles of value creation through in-memory technology: the first-order effects of reduced latency times and increased ability to process large volumes of complex data (big data processing) that lead to the second-order effects of advanced business analytics and the convergence of OLTP and OLAP that themselves lead to business value through improved organizational performance.
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