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

Author

Listed:
  • Paul Brous

    (Legend Data Management, 3053 WX Rotterdam, The Netherlands)

  • Marijn Janssen

    (Faculty of Technology, Policy and Management, Delft University of Technology, 2628 BX Delft, The Netherlands)

Abstract

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.

Suggested Citation

  • Paul Brous & Marijn Janssen, 2020. "Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes," Administrative Sciences, MDPI, vol. 10(4), pages 1-19, October.
    • Handle: RePEc:gam:jadmsc:v:10:y:2020:i:4:p:81-:d:427798
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    References listed on IDEAS

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    1. Yogesh K. Dwivedi & Marijn Janssen & Emma L. Slade & Nripendra P. Rana & Vishanth Weerakkody & Jeremy Millard & Jan Hidders & Dhoya Snijders, 2017. "Driving innovation through big open linked data (BOLD): Exploring antecedents using interpretive structural modelling," Information Systems Frontiers, Springer, vol. 19(2), pages 197-212, April.
    2. Cecere, Grazia & Le Guel, Fabrice & Soulié, Nicolas, 2012. "Perceived Internet privacy concerns on social network in Europe," MPRA Paper 41437, University Library of Munich, Germany.
    3. van den Broek, Tijs & van Veenstra, Anne Fleur, 2018. "Governance of big data collaborations: How to balance regulatory compliance and disruptive innovation," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 330-338.
    4. Vincenzo Morabito, 2015. "Big Data Governance," Springer Books, in: Big Data and Analytics, edition 127, chapter 0, pages 83-104, Springer.
    5. Ayoung Yoon, 2017. "Data reusers' trust development," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(4), pages 946-956, April.
    6. Gerd Gigerenzer & Reinhard Selten (ed.), 2002. "Bounded Rationality: The Adaptive Toolbox," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262571641, April.
    7. Jonathan Foster & Julie McLeod & Jan Nolin & Elke Greifeneder, 2018. "Data work in context: Value, risks, and governance," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(12), pages 1414-1427, December.
    8. Vincenzo Morabito, 2015. "Big Data and Analytics," Springer Books, Springer, edition 127, number 978-3-319-10665-6, December.
    9. Yogesh K. Dwivedi & Marijn Janssen & Emma L. Slade & Nripendra P. Rana & Vishanth Weerakkody & Jeremy Millard & Jan Hidders & Dhoya Snijders, 0. "Driving innovation through big open linked data (BOLD): Exploring antecedents using interpretive structural modelling," Information Systems Frontiers, Springer, vol. 0, pages 1-16.
    10. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
    11. Wanda J. Orlikowski, 1992. "The Duality of Technology: Rethinking the Concept of Technology in Organizations," Organization Science, INFORMS, vol. 3(3), pages 398-427, August.
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    2. Rainer Alt, 2022. "Electronic Markets on platform dualities," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 1-10, March.

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