Embedding data science innovations in organizations: a new workflow approach
There have been consistent calls for more research on managing teams and embedding processes in data science innovations. Widely used frameworks (e.g., the cross-industry standard process for data mining) provide a standardized approach to data science but are limited in features such as role clarit...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Published: |
2023
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| Online Access: | http://purl.org/au-research/grants/arc/IC180100030 http://hdl.handle.net/20.500.11937/96078 |
| _version_ | 1848766091497570304 |
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| author | Li, Keyao (Eden) Griffin, Mark Barker, T. Prickett, Z. Hodkiewicz, M.R. Kozman, J. Chirgwin, P. |
| author_facet | Li, Keyao (Eden) Griffin, Mark Barker, T. Prickett, Z. Hodkiewicz, M.R. Kozman, J. Chirgwin, P. |
| author_sort | Li, Keyao (Eden) |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | There have been consistent calls for more research on managing teams and embedding processes in data science innovations. Widely used frameworks (e.g., the cross-industry standard process for data mining) provide a standardized approach to data science but are limited in features such as role clarity, skills, and cross-team collaboration that are essential for developing organizational capabilities in data science. In this study, we introduce a data workflow method (DWM) as a new approach to break organizational silos and create a multi-disciplinary team to develop, implement and embed data science. Different from current data science process workflows, the DWM is managed at the system level that shapes business operating model for continuous improvement, rather than as a function of a particular project, one single business unit, or isolated individuals. To further operationalize the DWM approach, we investigated an embedded data workflow at a mining operation that has been using geological data in a machine-learning model to stabilize daily mill production for the last 2years. Based on the findings in this study, we propose that the DWM approach derives its capability from three aspects: (a) a systemic data workflow; (b) multi-disciplinary networks of collaboration and responsibility; and (c) clearly identified data roles and the associated skills and expertise. This study suggests a whole-of-organization approach and pathway to develop data science capability. |
| first_indexed | 2025-11-14T11:45:38Z |
| format | Journal Article |
| id | curtin-20.500.11937-96078 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:45:38Z |
| publishDate | 2023 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-960782024-11-07T01:03:56Z Embedding data science innovations in organizations: a new workflow approach Li, Keyao (Eden) Griffin, Mark Barker, T. Prickett, Z. Hodkiewicz, M.R. Kozman, J. Chirgwin, P. There have been consistent calls for more research on managing teams and embedding processes in data science innovations. Widely used frameworks (e.g., the cross-industry standard process for data mining) provide a standardized approach to data science but are limited in features such as role clarity, skills, and cross-team collaboration that are essential for developing organizational capabilities in data science. In this study, we introduce a data workflow method (DWM) as a new approach to break organizational silos and create a multi-disciplinary team to develop, implement and embed data science. Different from current data science process workflows, the DWM is managed at the system level that shapes business operating model for continuous improvement, rather than as a function of a particular project, one single business unit, or isolated individuals. To further operationalize the DWM approach, we investigated an embedded data workflow at a mining operation that has been using geological data in a machine-learning model to stabilize daily mill production for the last 2years. Based on the findings in this study, we propose that the DWM approach derives its capability from three aspects: (a) a systemic data workflow; (b) multi-disciplinary networks of collaboration and responsibility; and (c) clearly identified data roles and the associated skills and expertise. This study suggests a whole-of-organization approach and pathway to develop data science capability. 2023 Journal Article http://hdl.handle.net/20.500.11937/96078 10.1017/dce.2023.22 http://purl.org/au-research/grants/arc/IC180100030 https://creativecommons.org/licenses/by-nc/4.0/ fulltext |
| spellingShingle | Li, Keyao (Eden) Griffin, Mark Barker, T. Prickett, Z. Hodkiewicz, M.R. Kozman, J. Chirgwin, P. Embedding data science innovations in organizations: a new workflow approach |
| title | Embedding data science innovations in organizations: a new workflow approach |
| title_full | Embedding data science innovations in organizations: a new workflow approach |
| title_fullStr | Embedding data science innovations in organizations: a new workflow approach |
| title_full_unstemmed | Embedding data science innovations in organizations: a new workflow approach |
| title_short | Embedding data science innovations in organizations: a new workflow approach |
| title_sort | embedding data science innovations in organizations: a new workflow approach |
| url | http://purl.org/au-research/grants/arc/IC180100030 http://hdl.handle.net/20.500.11937/96078 |