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Data science in Sweden Exploring the state of data science use in Swedish businesses

Adam Prytz ; Tom Pettersson
Göteborg : Chalmers tekniska högskola, 2018. 70 s. Master thesis. E - Department of Technology Management and Economics, Chalmers University of Technology, Göteborg, Sweden, 2018.
[Examensarbete på avancerad nivå]

Data science brings with it vast opportunities. New technology enables advanced analytics to an increased extent and organizations seek to extract business value by becoming increasingly data driven. On behalf of the Technology Management and Economics department at Chalmers University of Technology this thesis seeks to explore the current adoption of data science and its implications among Swedish businesses. The focus of the study lies on mapping the current state, aiming to understand what the future holds and which challenges rms will have to deal with in the process of adopting data science into their organizations. Drawing upon data from interviews with representatives from a diverse set of Swedish rms, along with current research in the eld of data science, opportunities and challenges within the three areas competence, organization, and business impact are identi ed. With regard to business impact, there is a substantial gap between potential and actual value extracted from data science among Swedish businesses. And while it is clear that Swedish rms seek to develop their capabilities within the eld, the gap between potential and real value extracted is deemed to grow unless considerable action is taken within two areas: (1) develop the right managerial competence to bridge between technology and management, and (2) address the organizational challenges related to integrating data science to the operations of the rm. Drawing upon literature on data science and change management, the study provides clarity in what competencies should be focused on in order to foster 'data translators', and it also conceptualizes a seven step organization process which should be leveraged as a source of orientation for rms under the process of transition from 'no data science operations' to 'full-scale data science operations'.



Publikationen registrerades 2018-06-29.

CPL ID: 255384

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