2023-11-03
Dr Keyao Li
Authors: Li, K., Griffin, M.A., Barker, T., Prickett, Z., Hodkiewicz, M.R., Kozman, J., and Chirgwin, P.
Publication
Cambridge Core Data-Centric Engineering
Volume 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.
Quality Indicators
Peer Reviewed
Relevance to the Centre
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.
