Abstract
The benefits of adopting data science are increasingly clear in a variety of industries, yet adoption rates remain low. In this paper we examine the barriers faced by organizations in adopting data science approaches in the context of service innovation. We first characterize three types of barriers: legal framework, organizational challenges, and risks. The legal framework around data science is in a state of change, and certain aspects are outdated and fragmented. Organizational issues include recruitment and a lack of diversity. Finally, risk is inherent in any business, but data science investments may be especially uncertain due to the fundamental role that datasets play and the lack of familiarity that those making decisions may have with data science. We present results in which we identify and expand on the links between these barriers and service innovation using data science.
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References
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Acknowledgments
The authors thank colleagues L. Austin and M. Alexopolous for their input on these ideas as well as the participants of the 2019 CASCON Workshop on Barriers to Data Science Adoption [1].
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Alexander, R., Lyons, K. (2020). Barriers to Service Innovation Using Data Science. In: Spohrer, J., Leitner, C. (eds) Advances in the Human Side of Service Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1208. Springer, Cham. https://doi.org/10.1007/978-3-030-51057-2_9
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DOI: https://doi.org/10.1007/978-3-030-51057-2_9
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