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Article

Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes

by 1,* and 2
1
Legend Data Management, 3053 WX Rotterdam, The Netherlands
2
Faculty of Technology, Policy and Management, Delft University of Technology, 2628 BX Delft, The Netherlands
*
Author to whom correspondence should be addressed.
Adm. Sci. 2020, 10(4), 81; https://doi.org/10.3390/admsci10040081
Received: 9 September 2020 / Revised: 30 September 2020 / Accepted: 30 September 2020 / Published: 14 October 2020
(This article belongs to the Special Issue Managerial and Entrepreneurial Decision Making: Emerging Issues)
Organizations are increasingly introducing data science initiatives to support decision-making. However, the decision outcomes of data science initiatives are not always used or adopted by decision-makers, often due to uncertainty about the quality of data input. It is, therefore, not surprising that organizations are increasingly turning to data governance as a means to improve the acceptance of data science decision outcomes. In this paper, propositions will be developed to understand the role of data governance in creating trust in data science decision outcomes. Two explanatory case studies in the asset management domain are analyzed to derive boundary conditions. The first case study is a data science project designed to improve the efficiency of road management through predictive maintenance, and the second case study is a data science project designed to detect fraudulent usage of electricity in medium and low voltage electrical grids without infringing privacy regulations. The duality of technology is used as our theoretical lens to understand the interactions between the organization, decision-makers, and technology. The results show that data science decision outcomes are more likely to be accepted if the organization has an established data governance capability. Data governance is also needed to ensure that organizational conditions of data science are met, and that incurred organizational changes are managed efficiently. These results imply that a mature data governance capability is required before sufficient trust can be placed in data science decision outcomes for decision-making. View Full-Text
Keywords: data lake; data governance; data quality; big data; digital transformation; data science; asset management; boundary condition data lake; data governance; data quality; big data; digital transformation; data science; asset management; boundary condition
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MDPI and ACS Style

Brous, P.; Janssen, M. Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes. Adm. Sci. 2020, 10, 81. https://doi.org/10.3390/admsci10040081

AMA Style

Brous P, Janssen M. Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes. Administrative Sciences. 2020; 10(4):81. https://doi.org/10.3390/admsci10040081

Chicago/Turabian Style

Brous, Paul, and Marijn Janssen. 2020. "Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes" Administrative Sciences 10, no. 4: 81. https://doi.org/10.3390/admsci10040081

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