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Vergleich von Kompetenzanforderungen an Business-Intelligence- und Big-Data-Spezialisten

Eine Text-Mining-Studie auf Basis von Stellenausschreibungen

Comparing Business Intelligence and Big Data Skills

A Text Mining Study Using Job Advertisements

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WIRTSCHAFTSINFORMATIK

Zusammenfassung

Während sich die meisten wissenschaftlichen Studien zum Thema „Big Data“ mit den technischen Möglichkeiten zur Bewältigung von riesigen Datenmengen beschäftigen, sind empirische Untersuchungen in Bezug auf die von Fachleuten verlangten Kompetenzen für das Management and die Analyse von Big Data bislang noch nicht durchgeführt worden. Gleichzeitig diskutiert man in Wissenschaft und Praxis heftig über die Unterschiede und Gemeinsamkeiten von Big Data (BD) einerseits und „traditionellem“ Business Intelligence (BI) andererseits. Der vorliegende Artikel beschreibt die Durchführung einer Latenten Semantischen Analyse (LSA) von Stellenanzeigen auf dem Online-Portal monster.com, um Informationen darüber zu gewinnen, welche Anforderungen Unternehmen an Fachkräfte in den Bereichen BD und BI stellen. Auf Basis einer Analyse und Interpretation der statistischen Ergebnisse der LSA wird eine Taxonomie von Kompetenzanforderungen für BD bzw. BI entwickelt. Die wichtigsten Ergebnisse der Untersuchung lauten: (1) für beide Bereiche, BD und BI, ist Businesswissen genauso wichtig wie technisches Wissen; (2) kompetent sein im Bereich BI bezieht sich vorwiegend auf Wissen und Fähigkeiten in Bezug auf die Produkte der großen kommerziellen Softwareanbieter, während im Bereich BD eher Wissen und die Fähigkeiten in Bezug auf die Entwicklung von Individualsoftware und die Anwendung statistischer Methoden im Vordergrund steht; (3) die Nachfrage nach Kompetenz im Bereich BI ist immer noch weitaus größer als die Nachfrage nach Kompetenz im Bereich BD; und (4) BD-Projekte sind gegenwärtig wesentlich humankapital-intensiver als BI-Projekte. Die Ergebnisse und Erkenntnisse der Studie können Praktikern, Unternehmen und wissenschaftlichen Einrichtungen dabei helfen, ihre BD- bzw. BI-Kompetenz zu bewerten und zu erweitern.

Abstract

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.

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Literatur

  • Barney J (1991) Firm resources and sustained competitive advantage. J Manage 17:99–120

    Google Scholar 

  • Buhl HU (2013) Interview with martin petry on ”big data“. Bus Inf Syst Eng 5:101–102

    Article  Google Scholar 

  • Buhl HU, Röglinger M, Moser F, Heidemann J (2013) Big data. Bus Inf Syst Eng 5:65–69

    Article  Google Scholar 

  • Burton B, Geishecker L, Hostmann B et al (2006) Organizational structure: business intelligence and information management, S 1–11

  • Casey T, Krishnamurthy K, Abezgauz B (2013) Who should own big data? strategy+business

  • Cates J, Gill S, Zeituny N (2005) The ladder of business intelligence (LOBI): a framework for enterprise IT planning and architecture. Int J Bus Inf Syst 1:220–238

    Google Scholar 

  • Chen H, Chiang R, Storey V (2012) Business intelligence and analytics: from big data to big impact. MIS Q 36:1165–1188

    Google Scholar 

  • Coussement K, Van den Poel D (2008) Improving customer complaint management by automatic email classification using linguistic style features as predictors. Decis Support Syst 44:870–882

    Article  Google Scholar 

  • Cox M, Ellsworth D (1997) Application-controlled demand paging for out-of-core visualization. In: Proc 8th conf vis, S 235–244

    Google Scholar 

  • Davenport TH (2006) Competing on analytics. Harv Bus Rev 84:98–107

    Google Scholar 

  • Davenport TH, Patil D (2012) Data scientist. Harv Bus Rev 90:70–76

    Google Scholar 

  • Davenport TH, Barth P, Bean R (2012) How „big data“ is different. MIT Sloan Manag Rev 54:22–24

    Google Scholar 

  • de Lange C (2013) So you want to be a data scientist? Nat Jobs Blog. http://blogs.nature.com/naturejobs/2013/03/18/so-you-want-to-be-a-data-scientist. Abruf am 2013-05-03

  • Dhar V (2013) Data science and prediction. Commun ACM 56:64–73

    Article  Google Scholar 

  • Dinter B (2012) The maturing of a business intelligence maturity model. In: Proc Am conf inf syst, Seattle, S 1–10

    Google Scholar 

  • Eckerson W (2004) Gauge your data warehouse maturity. http://www.information-management.com/issues/20041101/1012391-1.html. Abruf am 2013-04-29

  • Evangelopoulos N, Visinescu L (2012) Text-mining the voice of the people. Commun ACM 55:62–69

    Article  Google Scholar 

  • Evangelopoulos N, Zhang X, Prybutok VR (2012) Latent semantic analysis: five methodological recommendations. Eur J Inf Syst 21:70–86

    Article  Google Scholar 

  • Forbes (2013) A very short history of big data. http://www.forbes.com/sites/gilpress/2013/05/09/a-very-short-history-of-big-data/. Abruf am 2014-03-23

  • Gallivan MJ, Truex DP, Kvasny L (2004) Changing patterns in IT skill sets: a content analysis of classified advertising. Data Base Adv Inf Syst 35:64–87

    Article  Google Scholar 

  • Golden B (2013) Does big data spell the end of business intelligence as we know it? In: CIO. http://www.cio.com/article/print/730774 Abruf am 2013-10-04

    Google Scholar 

  • Herbst A, Simons A, vom Brocke J et al. (2014) Identifying and characterizing topics in enterprise content management: a latent semantic analysis of vendor case studies. In: Proc 22nd Eur conf inf syst

    Google Scholar 

  • Hostmann B, Hagerty J (2010) IT-score overview for business intelligence and performance management. http://www.gartner.com/id=1433813. Abruf am 2013-04-29

    Google Scholar 

  • IBM (2012) Analytics: the real-world use of big data. http://www-935.ibm.com/services/us/gbs/thoughtleadership/ibv-big-data-at-work.html. Abruf am 2013-10-04

  • Lahrmann G, Marx F, Winter R, Wortmann F (2011) Business intelligence maturity: development and evaluation of a theoretical model. In: Proc Hawaii int conf syst sci, Koloa, S 1–10

    Google Scholar 

  • Landauer TK, Foltz PW, Laham D (1998) An introduction to latent semantic analysis. Discourse Process 25:259–284

    Article  Google Scholar 

  • Laney D (2001) 3D Data Management: controlling data volume, velocity, and variety. In: META gr. http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf. Abruf am 2013-10-04

  • Larsen KR, Monarchi DE, Hovorka DS, Bailey CN (2008) Analyzing unstructured text data: using latent categorization to identify intellectual communities in information systems. Decis Support Syst 45:884–896

    Article  Google Scholar 

  • LaValle S, Lesser E, Shockley R et al. (2011) Big data, analytics and the path from insights to value big data, analytics and the path from insights to value. MIT Sloan Manag Rev 52:21–31

    Google Scholar 

  • Litecky C, Aken A (2010) Mining for computing jobs. IEEE Softw 27:78–85

    Article  Google Scholar 

  • Loukides M (2012) What is data science? O’Reilly Media, Sebastopol

    Google Scholar 

  • Manning CD, Raghavan P, Schutze H (2008) Introduction to information retrieval. Cambridge University Press, New York

    Book  Google Scholar 

  • Manyika J, Chui M, Brown B et al (2011) Big data: The next frontier for innovation, competition, and productivity, S 1–156

  • Marchand D, Peppard J (2013) Why IT fumbles analytics. Harv Bus Rev 91:104–112

    Google Scholar 

  • Melville N, Kraemer KL, Gurbaxani V (2004) Review: information technology and organizational performance: an integrative model of IT business value. MIS Q 28:283–322

    Google Scholar 

  • Polock R (2013) Forget big data, small data is the real revolution. Open Knowl Found Blog. http://blog.okfn.org/2013/04/22/forget-big-data-small-data-is-the-real-revolution/. Abruf am 2013-10-04

  • Power D (2007) A brief history of decision support systems

  • Provost F, Fawcett T (2013) Data science and its relationship to big data and data-driven decision making. Big Data 1:51–59

    Article  Google Scholar 

  • Reuter N, Vakulenko S, vom Brocke J et al. (2014) Identifying the role of iInformation systems in achieving energy-related environmental sustainability using text mining. In: Proc 22nd Eur conf inf syst

    Google Scholar 

  • Van Roekel H, Linders J, Raja K et al (2009) The BI framework: how to turn information into a competitive asset. http://www.tdwi.eu/wissen/whitepaper/?no_cache=1&tx_mwknowledgebase_pi1[showUid]=45. Abruf am 2013-04-29

  • Russell S, Haddad M, Bruni M, Granger M (2010) Organic evolution and the capability maturity of business intelligence. In: Proc Am conf inf syst

  • Russom P (2011) Big data analytics. TDWI Res

  • Salton G, Wong A, Yang CS (1975) A vector space model for automatic indexing. Commun ACM 18:613–620

    Article  Google Scholar 

  • SAP (2014) SAP HANA integrates predictive analytics, text and big data in a single package. http://www.sap.com/pc/tech/in-memory-computing-hana/software/analytics/big-data.html. Abruf am 2014-03-23

  • Schlegel K, Sallam RS, Yuen D, Tapadinhas Y (2013) Magic quadrant for business intelligence platforms. http://www.gartner.com/technology/reprints.do?id=1-1DZLPEP&ct=130207&st=sb. Abruf am 2013-04-29

  • Sidorova A, Evangelopoulos N, Valacich JS, Ramakrishnan T (2008) Uncovering the intellectual core of the information systems discipline. MIS Q 32:467–482

    Google Scholar 

  • Todd PA, McKeen JD, Gallupe RB (1995) The evolution of IS job skills: a content analysis of IS job advertisements from 1970 to 1990. MIS Q 19:1–27

    Article  Google Scholar 

  • Varon E (2012) Rethink your org chart for big data analytics teams. http://data-informed.com/rethink-your-org-chart-for-big-data-analytics-teams/. Abruf am 2014-03-13

  • vom Brocke J, Debortoli S, Müller O, Reuter N (2014) How in-memory technology can create business value: insights from the Hilti case. Commun Assoc Inf Syst 34:151–168

    Google Scholar 

  • Waller Ma, Fawcett SE (2013) Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. J Bus Logist 34:77–84

    Article  Google Scholar 

  • Wixom B, Ariyachandra T, Goul M et al. (2011) The current state of business intelligence in academia. Commun Assoc Inf Syst 29:299–312

    Google Scholar 

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Authors and Affiliations

  1. Institut für Wirtschaftsinformatik, Universität Lichtenstein, Fürst-Franz-Josef-Strasse, 9490, Vaduz, Liechtenstein

    Stefan Debortoli,  Oliver Müller &  Jan vom Brocke

Authors
  1. Stefan Debortoli
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  2. Oliver Müller
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  3. Jan vom Brocke
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Correspondence to Stefan Debortoli.

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Angenommen nach zwei Überarbeitungen durch die Herausgeber des Schwerpunktthemas.

This article is also available in English via http://www.springerlink.com and http://www.bise-journal.org: S Debortoli, O Müller, J vom Brocke (2014) Comparing Business Intelligence and Big Data Skills. A Text Mining Study Using Job Advertisements doi: 10.1007/s12599-014-0344-2

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Debortoli, S., Müller, O. & Brocke, J.v. Vergleich von Kompetenzanforderungen an Business-Intelligence- und Big-Data-Spezialisten. Wirtschaftsinf 56, 315–328 (2014). https://doi.org/10.1007/s11576-014-0432-4

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