Conceptualizing Big Data: Analysis of Case Studies

• Author: Jari Porras,Ossi Ylijoki
• Date: 01 October 2016
• Published date: 01 October 2016
• DOI: http://doi.org/10.1002/isaf.1393

CONCEPTUALIZING BIG DATA: ANALYSIS OF CASE STUDIES

OSSI YLIJOKI*AND JARI PORRAS

School of Business and Management, Lappeenranta University of Technology, Lappeenranta, Finland

SUMMARY

Digitization and the related datafication produce huge amounts of data. Organizations have started to exploit these

new data in order to gain benefits. Exploring this ‘big data jungle’is a new area for both scholars and practitioners,

and the experiences of early adopters are valuable. This paper analyses big data use cases described in the academic

literature by using computerized content analysis methods. Based on the analysis results, we have conceptualized

themes and guidelines of big data in the context of an organization, thus contributing to the emerging research of

big data. In addition to the realized benefits, the case studies reveal issues regarding technology, skills, organiza-

tional culture and decision-making processes. The paper also points out several new research avenues,acts as a ref-

erence collection to big data case studies found in academic sources, and bridges theory and practice by pointing

out several topics that practitioners should consider. Copyright © 2016 John Wiley & Sons, Ltd.

1. INTRODUCTION

Today, new digital technologies produce vast amounts of various types of data (Gantz & Reinsel, 2011), often

referred to as big data. From the point of view of technology, big data are different from traditional transaction

data, requiring new data management and analysis technologies (Laney, 2001). More importantly, several

sources, including Davenport (2014); Manyika et al. (2011) and Mayer-Schönberger and Cukier (2013),

claim that big data have potentially huge effects on many industries. Technology and data drives change,

and as Dehning, Richardson, and Zmud (2003) and Sainio (2005), for example, suggest, companies must

link their strategy with technology.The business environment is changing. However, it is difficult to forecast

the impacts at the micro level, as digitization and data deluge are a new, emerging phenomenon.

The effects of this phenomenon are different for each company. As an example, self-driving cars,

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which will invade the markets in the future, will have significant effects on various firms, like car dealers

and insurance companies. However,the potential and the challenges that a car dealer faces will differ sig-

nificantly from those of an insurance company.Realizing the potential implies that this new, data-driven

paradigm will affect companies’strategies and business models heavily. Several excellent pieces of work

exist on business transformation. Venkatraman (1994) builds a framework that helps understand the ef-

fects of the transformation. Christensen (2013) explains clearly how incumbent companies fail con-

stantly in utilizing new, disruptive technologies. Sainio (2005) shows that companies are often well

aware of new, emerging technologies, but neglect linking the technologies with their strategies.

There are some trailblazers, Google and Amazon being the most obvious examples, which have built

their business models around data. These kinds of examples, as well as some previous studies (e.g. Porter

* Correspondence to: Schoolof Business and Management,LappeenrantaUniversity of Technology, Lappeenranta,Finland. E-mail:

ossi.ylijoki@phnet.fi

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For example, Google: http://googleblog.blogspot.fi/2015/05/self-driving-vehicle-prototypes-on-road.html; Nissan: http://

abcnews.go.com/Technology/nissan-driving-car-ready-2020-ceo/story?id=31120512; Volvo: http://www.wired.com/2015/02/

volvo-will-test-self-driving-cars-real-customers-2017/.

Copyright © 2016 John Wiley & Sons, Ltd.

INTELLIGENT SYSTEMS IN ACCOUNTING, FINANCE AND MANAGEMENT

Intell. Sys. Acc. Fin. Mgmt. 23, 295–310 (2016)

Published online 11 May 2016 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/isaf.1393

& Millar, 1985; Dehning et al., 2003; McAfee & Brynjolfsson, 2012) indicate that companies utilizing

data heavily gain a competitive advantageover their less data-driven rivals. However,the data-driven ap-

proach is still a new paradigm for most organizations (Shen & Varvel, 2013). In addition, established

companies have their own history, processe sand capabilities. They just cannot turn their existing struc-

tures and business models upside down at once. The transformation takes time. When established firms

start to explore the possibilities of big data, they can learn from the experiences and methods of the early

adopters. Several studies (e.g. De Mauro, Greco, & Grimaldi, 2015; Wamba et al., 2015) recognize the

need for guidelines and a conceptual framework for big data. Oneway towards this goal is to examine the

experiences of real big data projects. In this ar ticle weuse computerized text analysis methods to analyse

a number of big data case studies documented in academic publications.

The key contribution of this article is that we synthesize the findings (benefits and challenges) of our case

study analysis to a set of generic themes and guidelines. This contributes to the research on big data by

conceptualizing existing practices and pointing out several new research avenues. In addition, this work

bridges practice and theory, acts as a reference collection to currently known, peer-reviewed big data case

studies, and benefits practitioners by providing guidelines and experiences from the earlyadopters of big data.

2. BIG DATA CASE STUDIES

This section describes the research process we used to identify big data case studies. We used the systematic

mapping study approach presented by Kitchenham (2007). Our goal was to identify well-documented big

data case studies in the academic literature. Well-documented in this context means a peer-reviewed, high

quality source. In order to cover the area broadly, we performed a systematic mapping study. According to

Kitchenham (2007), mapping studies are designed to give a broad overviewof a research area. Mapping stud-

ies typically have broad research questions. Our target (research question) was simple: to locate as manybig

data case studies documented in peer-reviewedsources as possible, and to capture the common concepts and

lessons learned in these use cases. Figure 1 gives an overviewof the search process.

2.1. Search Strategy

Big data is a multidisciplinary phenomenon. Unlike some other subject areas, big-data-related articles

cannot be found only in certain highly focused forums. Although there are some new journals that

Figure 1. Search process for big data case studies

296 O. YLIJOKI AND J. PORRAS

Copyright © 2016 John Wiley & Sons, Ltd. Intell. Sys. Acc. Fin. Mgmt., 23, 295–310 (2016)

DOI: 10.1002/isaf