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Information Systems Research

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Editorial—Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research Ritu Agarwal, Vasant Dhar

To cite this article: Ritu Agarwal, Vasant Dhar (2014) Editorial—Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research. Information Systems Research 25(3):443-448. https://doi.org/10.1287/isre.2014.0546

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Information Systems Research Vol. 25, No. 3, September 2014, pp. 443–448 ISSN 1047-7047 (print) � ISSN 1526-5536 (online) http://dx.doi.org/10.1287/isre.2014.0546

© 2014 INFORMS

Editorial

Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research

Ritu Agarwal Center for Health Information and Decision Systems, Robert H. Smith School of Business, University of Maryland,

College Park, Maryland 20742, ragarwal@rhsmith.umd.edu

Vasant Dhar Center for Business Analytics, Stern School of Business, New York University, New York, New York 10012,

vdhar@stern.nyu.edu

We address key questions related to the explosion of interest in the emerging fields of big data, analytics, and data science. We discuss the novelty of the fields and whether the underlying questions are fundamentally

different, the strengths that the information systems (IS) community brings to this discourse, interesting research questions for IS scholars, the role of predictive and explanatory modeling, and how research in this emerging area should be evaluated for contribution and significance.

Introduction It is difficult, nay, impossible to open a popular pub- lication today, online or in the physical world and not run into a reference to data science, analytics, big data, or some combination thereof. To use a Twitter-esque phrase, that’s what’s trending now. A search on Google in the middle of August 2014 for the phrases “Big data,” “Analytics,” and “Data science” yielded 822 million, 154 million, and 461 million results, respectively. Our major journals (Management Science, MIS Quarterly) have commissioned special issues on these topics, and a large number of position announce- ments in the information systems field are specifying knowledge of, and skills in, one or more of these areas as desirable, if not a requirement for the job. A new journal called Big Data,1 launched just over a year ago, is already seeing thousands of downloads of articles. It would not be hyperbole to claim that big data is possi- bly the most significant “tech” disruption in business and academic ecosystems since the meteoric rise of the Internet and the digital economy.

What does this tsunami mean for information sys- tems researchers? Rather than simply rely on our own view of the world, we invited other accomplished scholars with a history of publication in this arena to participate in the conversation. We posed five ques- tions to frame our thinking about the domain:

1 See http://www.liebertpub.com/overview/big-data/611.

1. Are big data, analytics, and data science, as being described in the popular outlets, old wine in new bot- tles or is it something new?

2. What are the strengths that the information systems (IS) community brings to the discourse on business analytics? In other words, what is our com- petitive advantage?

3. What are important and interesting research questions and domains that may “fit” with on-going research in our community? How might we push the envelope by extending or modifying our existing research agendas? What about new areas of inquiry?

4. To what extent should robust prediction prowess be used as a criterion in evaluating data-driven mod- els versus current criteria that favor “explanatory” models without subjecting them to rigorous tests of future predictability?

5. As editors and reviewers, how should we evalu- ate research in this domain? What constitutes a “sig- nificant” contribution?

We would like to acknowledge the contributions of Galit Shmueli and Martin Bichler whose thought- ful responses to these questions gave us much food for contemplation. In the rest of this commentary we offer a synthesis of our collective reflection on the five questions.

On the Novelty of Big Data, Analytics and Data Science We believe that some components of data science and business analytics have been around for a long time,

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Agarwal and Dhar: The Opportunity and Challenge for IS Research 444 Information Systems Research 25(3), pp. 443–448, © 2014 INFORMS

but there are significant new questions and oppor- tunities created by the availability of big data and major advancements in machine intelligence.2 While the notion that analytical techniques can be used to make sense of and derive insights f