Stefan Debortoli

Comparing Business Intelligence and Big Data Skills

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.

Utilizing big data analytics for information systems research: challenges, promises and guidelines

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 user-generated 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.

Text Mining For Information Systems Researchers: An Annotated Topic Modeling Tutorial

It is estimated that more than 80 percent of today’s data is stored in unstructured form (e.g., text, audio, image, video); and much of it is expressed in rich and ambiguous natural language. Traditionally, the analysis of natural language has prompted the use of qualitative data analysis approaches, such as manual coding. Yet, the size of text data sets obtained from the Internet makes manual analysis virtually impossible. In this tutorial, we discuss the challenges encountered when applying automated text-mining techniques in information systems research. In particular, we showcase the use of probabilistic topic modeling via Latent Dirichlet Allocation, an unsupervised text mining technique, in combination with a LASSO multinomial logistic regression to explain user satisfaction with an IT artifact by automatically analyzing more than 12,000 online customer reviews. For fellow information systems researchers, this tutorial provides some guidance for conducting text mining studies on their own and for evaluating the quality of others.

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

ZusammenfassungWährend 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.AbstractWhile 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.

MUSE: implementation of a design theory for systems that support convergent and divergent thinking

It has been asserted that information systems (IS) can both enhance and undermine creativity. Earlier, we have proposed an IS design theory for systems that support individual creativity through fostering convergent and divergent thinking. In this paper we outline how we have transformed this abstract blueprint into a running software prototype. We chose cooking --- a familiar creative process --- as an exemplary domain to illustrate the form and function of the prototype. In future work, the prototype and the underlying design theory will be empirically evaluated using focus groups and laboratory experiments.

Tweeting Like Taylor Swift? Affordances, Status Production, and Online Platforms

This paper explores affordances of status production provided by Twitter as they are perceived and enacted under different institutional logics. We analyze tweets by IS academics (institutional logic of the IS academic profession) and celebrities (institutional logic of the popular music profession). We describe how the communicative features of Twitter are used to enhance identities and produce status in these two different fields. While we find evidence that both groups perceive and enact affordances of status production, we identify important differences in how these affordances are enacted: (1) certain affordances of status production are enacted under one institutional logic, while they are not under another, (2) the same affordance is enacted under different institutional logics with different intensity, and (3) different features afford the same opportunities of status production to different users. We explain the differences through the influence of offline cultural capital on online status production.

How Can Growing Energy Demands Be Met Safely and Efficiently

A United Nations survey identified environmental sustainability as the most important issue of the future Watson et al. (MIS Quarterly, 34(1), 23–38, 2010).