Establishing and implementing data collaborations for public good: A critical factor analysis to scale up the practice

摘要

Data analytics for public good has become a hot topic thanks to the inviting opportunities to utilize 'new' sources of data, such as social media insights, call detail records, satellite imagery etc. These data are sometimes shared by the private sector as part of corporate social responsibility, especially in situations of urgency, such as in case of a natural disaster. Such partnerships can be termed as 'data collaboratives'. While experimentation grows, little is known about how such collaborations are formed and implemented. In this paper, we investigate the factors which are influential and contribute to a successful data collaborative using the Critical Success Factor (CSF) approach. As a result, we propose (1) a framework of CSFs which provides a holistic view of elements coming into play when a data collaborative is formed and (2) a list of Top 15 factors which highlights the elements which typically have a greater influence over the success of the partnership. We validated our findings in two case studies and discussed three broad factors which were found to be critical for the formation of data collaboratives: value proposition, trust, and public pressure. Our results can be used to help organizations prioritize and distribute resources accordingly when engaging in a data collaborative.