Top

Business & Information Systems Engineering

Published in:

01-10-2014 | Research Paper

Comparing Business Intelligence and Big Data Skills

A Text Mining Study Using Job Advertisements

Authors: Stefan Debortoli, Dr. Oliver Müller, Prof. Dr. Jan vom Brocke

Published in: Business & Information Systems Engineering | Issue 5/2014

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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.

previous article Taming Uncertainty in Big Data

next article Interview with Michael Feindt on “Prescriptive Big Data Analytics”

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

WIRTSCHAFTSINFORMATIK

WI – WIRTSCHAFTSINFORMATIK – ist das Kommunikations-, Präsentations- und Diskussionsforum für alle Wirtschaftsinformatiker im deutschsprachigen Raum. Über 30 Herausgeber garantieren das hohe redaktionelle Niveau und den praktischen Nutzen für den Leser.

inform now

Business & Information Systems Engineering

BISE (Business & Information Systems Engineering) is an international scholarly and double-blind peer-reviewed journal that publishes scientific research on the effective and efficient design and utilization of information systems by individuals, groups, enterprises, and society for the improvement of social welfare.

inform now

Wirtschaftsinformatik & Management

Texte auf dem Stand der wissenschaftlichen Forschung, für Praktiker verständlich aufbereitet. Diese Idee ist die Basis von „Wirtschaftsinformatik & Management“ kurz WuM. So soll der Wissenstransfer von Universität zu Unternehmen gefördert werden.

inform now

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

Buhl HU (2013) Interview with Martin Petry on ”Big data“. Bus Inf Syst Eng 5:101–102 CrossRef

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

Burton B, Geishecker L, Hostmann B et al (2006) Organizational structure: business intelligence and information management pp 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

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

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

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

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

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

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

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. Accessed, 2013–05–2013-03

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

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

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

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

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

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/. Accessed 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 CrossRef

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

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

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

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. Accessed 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, pp 1–10

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

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. Accessed 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 CrossRef

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

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

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

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

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

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

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

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/. Accessed 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 CrossRef

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

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. Accessed 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 CrossRef

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. Accessed 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. Accessed 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

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 CrossRef

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/. Accessed 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

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 CrossRef

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

Title: Comparing Business Intelligence and Big Data Skills
A Text Mining Study Using Job Advertisements
Authors: Stefan Debortoli
Dr. Oliver Müller
Prof. Dr. Jan vom Brocke
Publication date: 01-10-2014
Publisher: Springer Fachmedien Wiesbaden
Published in: Business & Information Systems Engineering / Issue 5/2014
Print ISSN: 2363-7005
Electronic ISSN: 1867-0202
DOI: https://doi.org/10.1007/s12599-014-0344-2

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

WIRTSCHAFTSINFORMATIK

Business & Information Systems Engineering

Wirtschaftsinformatik & Management

Other articles of this Issue 5/2014

Interview with Stefan Wrobel on “Applied Big Data Research”

BISE – Call for Papers Issue 1/2016

Taming Uncertainty in Big Data

Big Data and Information Processing in Organizational Decision Processes

Big Data

Big Data

Premium Partner