Data science roadmapping: An architectural framework for facilitating transformation towards a data-driven organization

Kerem Kayabay, Mert Onuralp Gökalp, Ebru Gökalp, P. Erhan Eren and Altan Koçyiğit

Technological Forecasting and Social Change, 2022, vol. 174, issue C

Abstract: Leveraging data science can enable businesses to exploit data for competitive advantage by generating valuable insights. However, many industries cannot effectively incorporate data science into their business processes, as there is no comprehensive approach that allows strategic planning for organization-wide data science efforts and data assets. Accordingly, this study explores the Data Science Roadmapping (DSR) to guide organizations in aligning their business strategies with data-related, technological, and organizational resources. The proposed approach is built on the widely adopted technology roadmapping framework and customizes its context, architecture, and process by synthesizing data science, big data, and data-driven organization literature. Based on industry collaborations, the framework provides a hybrid and agile methodology comprising the recommended steps. We applied DSR with a research group with sector experience to create a comprehensive data science roadmap to validate and refine the framework. The results indicate that the framework facilitates DSR initiatives by creating a comprehensive roadmap capturing strategy, data, technology, and organizational perspectives. The contemporary literature illustrates prebuilt roadmaps to help businesses become data-driven. However, becoming data-driven also necessitates significant social change toward openness and trust. The DSR initiative can facilitate this social change by opening communication channels, aligning perspectives, and generating consensus among stakeholders.

Keywords: Technology roadmapping; Technology management; Data science; Digital transformation; Data-driven organization; Big data (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:174:y:2022:i:c:s0040162521006983

DOI: 10.1016/j.techfore.2021.121264

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