An increasing number of initiatives have emerged around the world to help facilitate data sharing and collaborations to leverage different sources of data to address societal problems. They are called “data collaboratives”. Data collaboratives are seen as a novel way to match real life problems with relevant expertise and data from across the sectors. Despite its significance and growing experimentation by practitioners, there has been limited research in this field. In this article, the authors report on the outcomes of a panel discussing critical issues facing data collaboratives and develop a research and development agenda. The panel included participants from the government, academics, and practitioners and was held in June 2017 during the 18 ^th International Conference on Digital Government Research at City University of New York (Staten Island, New York, USA). The article begins by discussing the concept of data collaboratives. Then the authors formulate research questions and topics for the research roadmap based on the panel discussions. The research roadmap poses questions across nine different topics: conceptualizing data collaboratives, value of data, matching data to problems, impact analysis, incentives, capabilities, governance, data management, and interoperability. Finally, the authors discuss how digital government research can contribute to answering some of the identified research questions.

@en

This panel is dedicated to the theme of 'data collaboratives', a novel form of public private partnership to leverage data for addressing societal challenges. The panel brings together prolific researchers and practitioners to share lessons and discuss how value is created from data collaboratives for the solving of public problems. The panel will highlight prominent examples of data collaboratives at international, national, and regional/city-levels and discuss the value creation mechanisms underlying them, as well as more broadly best practices and challenges associated with data collaboratives. The panel offers an opportunity for conference attendees to engage with this emerging new theme through interactive discussions and presentations of cutting-edge research and practice.@en

Purpose: In “data collaboratives”, private and public organizations coordinate their activities to leverage data to address a societal challenge. This paper aims to focus on analyzing challenges and coordination mechanisms of data collaboratives. Design/methodology/approach: This study uses coordination theory to identify and discuss the coordination problems and coordination mechanisms associated with data collaboratives. The authors also use a taxonomy of data collaborative forms from a previous empirical study to discuss how different forms of data collaboratives may require different coordination mechanisms. Findings: The study analyzed data collaboratives from the perspective of organizational and task levels. At the organizational level, the authors argue that data collaboratives present an example of the bazaar form of coordination. At the task level, the authors identified five coordination problems and discussed potential coordination mechanisms to address them, such as coordination by negotiation, by third party, by standardization, to name a few. Research limitations/implications: This study is one of the first few to systematically analyze the phenomenon of “data collaboratives”. Practical implications: This study can help practitioners better understand the coordination challenges they may face when initiating a data collaborative and to develop successful data collaboratives by using coordination mechanisms to mitigate these challenges. Originality/value: Data collaboratives are a novel form of data-driven initiatives which have seen rapid experimentation lately. This study draws attention to this concept in the academic literature and highlights some of the complexities of organizing data collaboratives in practice.

@en

Data collaboratives present a new form of cross-sector and public-private partnership to leverage (often corporate) data for addressing a societal challenge. They can be seen as the latest attempt to make data accessible to solve public problems. Although an increasing number of initiatives can be found, there is hardly any analysis of these emerging practices. This paper seeks to develop a taxonomy of forms of data collaboratives. The taxonomy consists of six dimensions related to data sharing and eight dimensions related to data use. Our analysis shows that data collaboratives exist in a variety of models. The taxonomy can help organizations to find a suitable form when shaping their efforts to create public value from corporate and other data. The use of data is not only dependent on the organizational arrangement, but also on aspects like the type of policy problem, incentives for use, and the expected outcome of data collaborative. @en