**5. Discussion**

Case study methodology was used in this research to identify the role that data governance plays as a success factor for data science. The choice for an in-depth case study was based on the contemporary nature of both data science and data governance. The study was conducted on the basis of two case studies in different organizations, and the results should be regarded in this light. The study was conducted in the asset managemen<sup>t</sup> domain as asset managemen<sup>t</sup> organizations by nature are often data-rich due to the need to monitor the state of the infrastructure assets. This may limit the applicability of the study for domains which are less data intensive, however the essence of generating value from data is likely to be the same in other domains.

### *5.1. Proposition 1. Organizations with an Established Data Governance Capability Are More Likely to Have a Well-Functioning Data Science Capability*

With regards to Proposition 1, which proposes that organizations with an established data governance capability have better functioning data science capabilities, the results of the case studies sugges<sup>t</sup> that when data governance has been actively implemented before the start of a data science project, the complexity of issues such as access to data and the understanding of the data is greatly reduced. The use of big data in data science projects often leads to serious data quality (Saha and Srivastava 2014) and compliance (Narayanan et al. 2016) issues which can be difficult to manage in a timely manner (Hazen et al. 2014). Data governance policies and principles (Madera and Laurent 2016) and a responsible data governance strategy (Kroll 2018) should therefore be key components of data science technologies. This suggests that data governance plays an important role in ensuring the effectiveness and efficiency of the data science capability in an organization.

### *5.2. Proposition 2. Organizations with Established Data Governance Capability Are More Likely to Generate Trusted Data Science Outcomes*

Proposition 2 suggests that organizations with an established data governance capability are better positioned to produce trusted data science decision outcomes. The results of the case studies confirm that data science projects in which data owners have a direct influence on the project from start to finish are more likely to generate trusted outcomes. Data governance is important for creating value and moderating risk in data science initiatives (Foster et al. 2018), as the trustworthiness of data science outcomes in practice is often affected by tensions arising through ongoing forms of work (Passi and Jackson 2018). This suggests that data governance plays an important role in creating trust in data science outcomes and positively influencing the use and acceptance of data science outcomes in the organization.

### *5.3. Proposition 3. Organizations with an Established Data Governance Capability Are More Likely to Ensure that Organizational Conditions of Data Science are Met*

Successful data science outcomes require data governance mechanisms beginning with policy development to define governance goals and strategies (Wang et al. 2019), followed by the establishment of organizational data governance structures. Top managemen<sup>t</sup> support (Gao et al. 2015), well-defined roles and responsibilities (Saltz and Shamshurin 2016), and the choice of the data governance approach (Koltay 2016) are considered critical. Proposition 3 proposes that organizations having an established data governance capability are more likely to be in a position to meet organizational conditions. In this regard, the case studies sugges<sup>t</sup> that a regime of coordination and control of data managemen<sup>t</sup> processes, following a regular cycle, as well as well-defined roles and responsibilities, play important roles in developing ecosystems in which data science projects are more likely to be successful.

### *5.4. Proposition 4. Organizations with an Established Data Governance Capability Are More Likely to Be Able to Manage Organizational and Process Changes Introduced by Data Science Outcomes*

Data governance establishes data managemen<sup>t</sup> processes which manage data quality (Passi and Jackson 2018) and compliance with relevant laws, directives, and policies (Cato et al. 2015). Data governance aligns policies and principles with business strategies in an enterprise data strategy (Cato et al. 2015). Proposition 4 proposes that organizations with mature data governance are more likely to be able to manage changes introduced by data science decision outcomes. In this regard, the results of the case studies sugges<sup>t</sup> that organizations which have a well-developed data governance capability are more likely to be able to manage new costs arising from changes in staff and technology, manage changing risks arising from changes in primary processes, and manage organizational and process changes introduced by the acceptance of data science outcomes within the business.
