Data Collaboratives as a New Frontier of Cross-Sector Partnerships in the Age of Open Data: Taxonomy Development

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

A Collaborative Governance Approach to Partnerships Addressing Public Problems with Private Data

The recent explosion of data, which is generated, collected, and exchanged, opens up new opportunities and poses new challenges. Actors in different sectors have recently began to explore how they can work together and leverage these data to help address ‘wicked’ problems. A novel form of cross sector partnership emerges, labelled “data collaborative”, which is normally focused on accessing private sector data and using it to address public problems. While there is emerging knowledge about how data can be shared in such partnerships, less is known about the collaboration dynamics of these partnerships. Are there any distinct collaboration mechanisms and challenges that come into play? In this paper, we examine this problem from the perspective of collaborative governance and propose a framework for understanding collaboration around data sharing for public good

Business-to-Government Data Sharing for Public Interests in the European Union: Results of a Public Consultation

Lately governments and companies began experimenting with voluntary data sharing of business data for addressing public problems (so-called Data Collaboratives). This early practice revealed a number of challenges impeding business-to-government (B2G) data sharing and thus limiting the potential of data to provide answers and guide policies and action. One of the key challenges is the lack of a clear regulatory framework for B2G data sharing. To tackle this issue, the European Commission is taking regulatory action and preparing the Data Act which aims to spell out the rules and conditions for B2G data sharing for public interest. These developments, however, are met with resistance. While there is a strong push from the public sector for more private sector data, the private sector is less enthusiastic about the prospective mandatory B2G data sharing. In our study we zoom in on this issue in more detail and pose the following research question: How do public and private sector actors in the European Union view the prospect of mandatory B2G data sharing for public interest? To answer this question, we analyze the open dataset of responses to the public consultation of the European Commission. We find statistically significant results of business opposition to regulatory action and to mandating B2G data sharing, particularly among telecom and finance sectors. We also conclude that opposition to mandatory data sharing varies depending on the public interest purpose and is lowest among businesses with regards to emergencies and highest with regard to education, inclusion, and statistics

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

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

Comparing open data benchmarks: Which metrics and methodologies determine countries' positions in the ranking lists?

An understanding of the similar and divergent metrics and methodologies underlying open government data benchmarks can reduce the risks of the potential misinterpretation and misuse of benchmarking outcomes by policymakers, politicians, and researchers. Hence, this study aims to compare the metrics and methodologies used to measure, benchmark, and rank governments' progress in open government data initiatives. Using a critical meta-analysis approach, we compare nine benchmarks with reference to meta-data, meta-methods, and meta-theories. This study finds that both existing open government data benchmarks and academic open data progress models use a great variety of metrics and methodologies, although open data impact is not usually measured. While several benchmarks’ methods have changed over time, and variables measured have been adjusted, we did not identify a similar pattern for academic open data progress models. This study contributes to open data research in three ways: 1) it reveals the strengths and weaknesses of existing open government data benchmarks and academic open data progress models; 2) it reveals that the selected open data benchmarks employ relatively similar measures as the theoretical open data progress models; and 3) it provides an updated overview of the different approaches used to measure open government data initiatives’ progress. Finally, this study offers two practical contributions: 1) it provides the basis for combining the strengths of benchmarks to create more comprehensive approaches for measuring governments’ progress in open data initiatives; and 2) it explains why particular countries are ranked in a certain way. This information is essential for governments and researchers to identify and propose effective measures to improve their open data initiatives

Characterizing Data Ecosystems to Support Official Statistics with Open Mapping Data for Reporting on Sustainable Development Goals

Reporting on the Sustainable Development Goals (SDGs) is complex given the wide variety of governmental and NGO actors involved in development projects as well as the increased number of targets and indicators. However, data on the wide variety of indicators must be collected regularly, in a robust manner, comparable across but also within countries and at different administrative and disaggregated levels for adequate decision making to take place. Traditional census and household survey data is not enough. The increase in Small and Big Data streams have the potential to complement official statistics. The purpose of this research is to develop and evaluate a framework to characterize a data ecosystem in a developing country in its totality and to show how this can be used to identify data, outside the official statistics realm, that enriches the reporting on SDG indicators. Our method consisted of a literature study and an interpretative case study (two workshops with 60 and 35 participants and including two questionnaires, over 20 consultations and desk research). We focused on SDG 6.1.1. (Proportion of population using safely managed drinking water services) in rural Malawi. We propose a framework with five dimensions (actors, data supply, data infrastructure, data demand and data ecosystem governance). Results showed that many governmental and NGO actors are involved in water supply projects with different funding sources and little overall governance. There is a large variety of geospatial data sharing platforms and online accessible information management systems with however a low adoption due to limited internet connectivity and low data literacy. Lots of data is still not open. All this results in an immature data ecosystem. The characterization of the data ecosystem using the framework proves useful as it unveils gaps in data at geographical level and in terms of dimensionality (attributes per water point) as well as collaboration gaps. The data supply dimension of the framework allows identification of those datasets that have the right quality and lowest cost of data extraction to enrich official statistics. Overall, our analysis of the Malawian case study illustrated the complexities involved in achieving self-regulation through interaction, feedback and networked relationships. Additional complexities, typical for developing countries, include fragmentation, divide between governmental and non-governmental data activities, complex funding relationships and a data poor context

Open Data Research in the Nordic Region: Towards a Scandinavian Approach?

Part 2: Open GovernmentInternational audienceSince 2009 open data has been growing into a specialized research area, including in the Nordic countries. Historically Information Systems research from this region has managed to develop a distinct identity on the international research arena. Hence, the expectation is that also in the context of open data there exists room for unique contributions of Nordic researchers. However, no systematic overview exists yet of the open data research conducted in these countries or of the emerging research community. This paper, therefore, aims to fill this gap by conducting a comprehensive literature review. Our study focuses on the following aspects: (1) which perspectives and topics are examined and (2) which empirical settings and methods are applied in Nordic open data research. Finding answers to these questions will enable us to propose a future research agenda and thereby stimulate debate in the Nordic open data research community