**6. Conclusions**

In this paper we analyzed two data science case studies in the asset managemen<sup>t</sup> domain in order to understand the role of data governance as a boundary condition for creating trust in data science decision outcomes. The first case under study was a data science project which predicts the maintenance requirements of asphalt on national highways over time. The second case study was a data science project which discovers the fraudulent use of electricity in a middle- and low-level voltage grid. The results of the case studies sugges<sup>t</sup> that data science decision outcomes are more likely to be accepted if the organization has an established data governance capability. Furthermore, the results sugges<sup>t</sup> that organizations with an established data governance capability are more likely to have a well-functioning data science capability, are more likely to generate trusted data science

outcomes, are more likely to ensure that organizational conditions of data science are met, and are more likely to be able to manage organizational and process changes introduced by the data science decision outcomes. These results confirm the propositions of the research and we conclude that data governance is a boundary condition for managing the organizational consequences of data science outcomes. Viewing the acceptance of data science decision outcomes for decision-making in organizations as a socio-material challenge in which trust plays a central role implies that the analysis and interpretation of data is tightly coupled with the governance and proper managemen<sup>t</sup> of that data. Simply "throwing data" at a problem without regard for the quality or bias of the data or the algorithm itself does not necessarily lead to acceptance of the decision outcomes. Rather, it is necessary to look at the development of trustworthy data science decision outcomes not as a purely technical problem, requiring a technical solution, but as one in which human agency and organizational forces play a significant role. This approach also has practical implications, as managers responsible for data science should ensure that the data governance capability of the organization is well established before the focus is placed on the development of the data science capability. The research was limited to two data science projects in (semi)-government organizations within the asset managemen<sup>t</sup> domain. Further investigation with regards to data science projects with di fferent scopes, domains, and organizations is recommended.

**Author Contributions:** Conceptualization, P.B. and M.J.; methodology, P.B.; validation, M.J.; formal analysis, P.B.; investigation, P.B.; resources, P.B.; data curation, P.B.; writing—original draft preparation, P.B.; writing—review and editing, M.J.; supervision, M.J.; project administration, P.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.
