Trusted decision-making: Data governance for creating trust in data science decision outcomes

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Administrative Sciences ; ISSN: 2076-3387 ; Volume: 10 ; Year: 2020 ; Issue: 4 ; Pages: 1-19 ; Basel: MDPI

Classification and Topics

• Classification: Öffentliche Verwaltung
• Subject: data lake ; data governance ; data quality ; big data ; digital transformation ; data science ; asset management ; boundary condition

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Brous, Paul ; Janssen, Marijn
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2020

Further information

• DOI: doi:10.3390/admsci10040081
• Handle: http://hdl.handle.net/10419/240071
• Last update: 10.03.2025, 11:44 AM CET