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Big Data

Author

Listed:
  • Michael Schermann
  • Holmer Hemsen
  • Christoph Buchmüller
  • Till Bitter
  • Helmut Krcmar
  • Volker Markl
  • Thomas Hoeren

Abstract

“Big data” describes technologies that promise to fulfill a fundamental tenet of research in information systems, which is to provide the right information to the right receiver in the right volume and quality at the right time. For information systems research as an application-oriented research discipline, opportunities, and risks arise from using big data. Risks arise primarily from the considerable number of resources used for the explanation and design of fads. Opportunities arise because these resources lead to substantial knowledge gains, which support scientific progress within the discipline and are of relevance to practice as well. From the authors’ perspective, information systems research is ideally positioned to support big data critically and use the knowledge gained to explain and design innovative information systems in business and administration – regardless of whether big data is in reality a disruptive technology or a cursory fad. The continuing development and adoption of big data will ultimately provide clarity on whether big data is a fad or if it represents substantial progress in information systems research. Three theses also show how future technological developments can be used to advance the discipline of information systems. Technological progress should be used for a cumulative supplement of existing models, tools, and methods. By contrast, scientific revolutions are independent of technological progress. Copyright Springer Fachmedien Wiesbaden 2014

Suggested Citation

  • Michael Schermann & Holmer Hemsen & Christoph Buchmüller & Till Bitter & Helmut Krcmar & Volker Markl & Thomas Hoeren, 2014. "Big Data," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(5), pages 261-266, October.
    • Handle: RePEc:spr:binfse:v:6:y:2014:i:5:p:261-266
    DOI: 10.1007/s12599-014-0345-1
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    References listed on IDEAS

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    1. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
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