Big data business models: Challenges and opportunities

Ralph Schroeder,Jamie Halsall

doi:10.1080/23311886.2016.1166924

Abstract

AbstractThis paper, based on 28 interviews from a range of business leaders and practitioners, examines the current state of big data use in business, as well as the main opportunities and challenges presented by big data. It begins with an account of the current landscape and what is meant by big data. Next, it draws distinctions between the ways organisations use data and provides a taxonomy of big data business models. We observe a variety of different business models, depending not only on sector, but also on whether the main advantages derive from analytics capabilities or from having ready access to valuable data sources. Some major challenges emerge from this account, including data quality and protectiveness about sharing data. The conclusion discusses these challenges, and points to the tensions and differing perceptions about how data should be governed as between business practitioners, the promoters of open data, and the wider public.

Highlights

Big data is increasingly seen as an essential element of a well-functioning economy
If we combine our findings about types of business models with the challenges we have outlined, it is clear that those pursuing the three models will have quite different bottlenecks in going forward strategically: data quality affect all three, but addressing this issue may be costly for suppliers, may lead users to discount or factor in the reliability of data and prompt facilitators to seek the best available sources
This complex web of dependencies is bound to crystallise in the coming years, but it behoves businesses to ask themselves how they are placed to overcome these challenges, given that the type of business model they pursue is bound to be already largely determined by its capacities and resources

Summary

Introduction

Big data is increasingly seen as an essential element of a well-functioning economy. A number of reports and academic publications have pointed to the growing use of big data across economic sectors (Brynjolfsson, Hitt, & Heekyung, 2011; Bulger, Taylor, & Schroeder, 2014; George, Haas, & Pentland, 2014; Manyika et al, 2011; Schroeck, Shockley, Smart, Romero-Morales, & Tufano, 2012; Taylor & Schroeder, 2014; Taylor, Schroeder, & Meyer, 2014; Thomas & McSharry, 2015) and its potential to bolster productivity, efficiency, and growth. Before coming to Oxford University, he was a professor in the School of Technology Management and Economics at Chalmers University in Gothenburg (Sweden). His recent books are Rethinking Science, Technology and Social Change (Stanford University Press, 2007) and, co-authored with Eric T. Knowledge Machines: Digital Transformations of the Sciences and Humanities (MIT Press 2015). He is the author of 6 books, editor and co-editor of 4 volumes, and has published more than 125 papers on virtual environments, Max Weber, sociology of science and technology, e-Research, and other topics

Objectives

Methods

Findings

Conclusion