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Cambridge Core Data-Centric EngineeringVolume 4 , 2023 , e26 This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial licence (http://creativecommons.org/licenses/by-nc/4.0), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original article is properly cited. The written permission of Cambridge University Press must be obtained prior to any commercial use. |
Peer Reviewed |
This study could contribute to the community of data science in the following aspects: (a) introducing a new data workflow method (DWM) to address the limitations of current process workflows, enhancing both social and individual capital that is necessary for successful embedding; (b) more practically, with the support of interview findings, this study points to a whole-of-organization pathway to foster data science capability for the digital future in industrial operations; and (c) presenting an in-depth analysis to enhance the understanding of the DWM and its application in operational contexts. This insight showcases how data science could be adopted not only as technical solutions for a single project, but also to improve organization data strategy, and in turn overall business operations. |