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Comparing Business Intelligence and Big Data Skills

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While many studies on big data analytics describe the data deluge and potential applications for such analytics, the required skill set for dealing with big data has not yet been studied empirically. The difference between big data (BD) and traditional business intelligence (BI) is also heavily discussed among practitioners and scholars. We conduct a latent semantic analysis (LSA) on job advertisements harvested from the online employment platform monster.com to extract information about the knowledge and skill requirements for BD and BI professionals. By analyzing and interpreting the statistical results of the LSA, we develop a competency taxonomy for big data and business intelligence. Our major findings are that (1) business knowledge is as important as technical skills for working successfully on BI and BD initiatives; (2) BI competency is characterized by skills related to commercial products of large software vendors, whereas BD jobs ask for strong software development and statistical skills; (3) the demand for BI competencies is still far bigger than the demand for BD competencies; and (4) BD initiatives are currently much more human-capital-intensive than BI projects are. Our findings can guide individual professionals, organizations, and academic institutions in assessing and advancing their BD and BI competencies. Copyright Springer Fachmedien Wiesbaden 2014

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  • Stefan Debortoli & Oliver Müller & Jan Brocke, 2014. "Comparing Business Intelligence and Big Data Skills," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(5), pages 289-300, October.
    • Handle: RePEc:spr:binfse:v:6:y:2014:i:5:p:289-300
    DOI: 10.1007/s12599-014-0344-2
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    1. Hans Buhl & Maximilian Röglinger & Florian Moser & Julia Heidemann, 2013. "Big Data," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 5(2), pages 65-69, April.
    2. Hans Buhl, 2013. "Interview with Martin Petry on “Big Data”," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 5(2), pages 101-102, April.
    3. James E. Cates & Sam S. Gill & Natalie Zeituny, 2005. "The Ladder of Business Intelligence (LOBI): a framework for enterprise IT planning and architecture," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 1(1/2), pages 220-238.
    4. K. Coussement & D. Van Den Poel, 2007. "Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/481, Ghent University, Faculty of Economics and Business Administration.
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    6. Nimmagadda, Shastri L. & Reiners, Torsten & Wood, Lincoln C., 2018. "On big data-guided upstream business research and its knowledge management," Journal of Business Research, Elsevier, vol. 89(C), pages 143-158.
    7. Rosita Capurro & Michele Galeotti & Stefano Garzella, 2018. ""Mondo reale-tradizionale" e "mondo digitale", strategie aziendali e web intelligence: il futuro del controllo e della gestione delle informazioni," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2018(2 Suppl.), pages 83-111.
    8. Kala C. Seal & Linda A. Leon & Zbigniew H. Przasnyski & Greg Lontok, 2020. "Delivering Business Analytics Competencies and Skills: A Supply Side Assessment," Interfaces, INFORMS, vol. 50(4), pages 239-254, July.
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