Understanding data collaboratives ten years after their definition: Distinctive features, impacts and research priorities

Abstract The use of data for social good has received increasing attention from institutions, practitioners and academics in recent years. Data collaboratives are cross-sectoral partnerships that aim to foster the use of data for societal purposes. However, the proliferation of initiatives on the topic of data sharing has created confusion regarding their nature and scope. To advance research on the topic, using existing literature, this paper offers a refinement of the concept of data collaboratives ten years after their first definition. This enables the distinction between data collaboratives and other forms of initiatives such as open platforms and data ecosystems. Through the analysis of a dataset of 171 data collaboratives, the paper proposes an enhanced categorisation that identifies five clusters of data collaboratives. Each cluster is described with a focus on its individual characteristics and development challenges. The holistic approach adopted and the maturity of the field allowed us to gain valuable insights into the domains and scopes that these types of partnership may serve and their potential impact. The results highlight the heterogeneity of initiatives falling under the concept of data collaboratives and the necessity to address their development challenges by either concentrating on a specific cluster or conducting comparative and horizontal studies. These findings also enable comparability and improve the identification of benchmarks, which is a valuable resource for the development of the field.