New Report: “Leveraging Private Data for Public Good”

Michelle Winowatan
Oct 31 · 4 min read

A Descriptive Analysis and Typology of Existing Data Collaborative Practices

By Andrew J. Zahuranec and Michelle Winowatan

A new decade is on the horizon and, with it, an opportunity to revisit old practices and embrace new ones. For data-driven organizations looking for new ways to work more openly, effectively, and legitimately, much of this reflection has centered on data collaboration. Coined by The GovLab in 2015, data collaboratives are new forms of partnership in which participants from different sectors — in particular companies — exchange data to create public value. Though the value of collaboration is clear, how data collaboratives operate is often not.

Today, The GovLab is releasing a new report to fill that gap: “Leveraging Private Data for Public Good: A Descriptive Analysis and Typology of Existing Practices” by Stefaan Verhulst, Andrew Young, Michelle Winowatan, and Andrew J. Zahuranec. The document updates our prior work, dramatically expanding our understanding of data collaboration. Looking at over 150 real-world examples, it describes past and current data-collaboration practices and the models under which they occur.

At its core is a typology that describes how and under what circumstances private-sector data collaboration occurs. It uses two axes: engagement, whether actors co-design the initiative, and accessibility, whether the data is available to external parties. This framework provides six models for data collaboration:

  • Public Interfaces: Companies provide open access to certain data assets, enabling independent uses of the data by external parties.
  • Trusted Intermediary: Third-party actors support collaboration between private-sector data providers and data users from the public sector, civil society, or academia.
  • Data Pooling: Companies and other data holders agree to create a unified presentation of datasets as a collection accessible by multiple parties.
  • Research and Analysis Partnership: Companies engage directly with public-sector partners and share certain proprietary data assets to generate new knowledge with public value.
  • Prizes and Challenges: Companies make data available to participants who compete to develop apps; answer problem statements; test hypotheses and premises; or pioneer innovative uses of data for the public interest and to provide business value.
  • Intelligence Generation: Companies internally develop data-driven analyses, tools, and other resources, and release those insights to the broader public.

Within each of these models, we provide approaches for implementation which organizations might use. We also list specific examples from our Data Collaborative Explorer to illustrate. In total, the report outlines 11 different data collaboration approaches across these six models and describes 47 real-world examples. While not fully comprehensive, these cases illustrate how public value can be generated from data collaboration.

The document also includes additional variables that enhance our understanding of the mechanics of data collaboration. Presented as a checklist, those who want to design a data collaborative can refer to it to determine what best suits their needs.

As this work shows, choosing a data collaborative approach is complex and context-sensitive. However, there are a few overarching takeaways. First, the research shows the need for a new scoping methodology to assess the variables in a data collaborative. Such methodology, which The GovLab is now developing, would provide a deeper understanding of the value and limitation in choosing one approach over the other.

Second, it shows a need to better establish and empower data stewards, responsible data leaders from the private sector empowered to seek new ways to create public value through cross-sector data collaboration. Given the complexity of the approaches outlined, dedicated teams and individuals are essential to sustaining collaboration.

Finally, it suggests we need new intermediaries to lower transaction costs among data suppliers and users. New intermediaries would provide essential support to move from one-off initiatives to more sustainable, larger-scale ventures.

Moving forward, The GovLab will further explore these threads. While the need for collaboration is clear, we are only now understanding the ways in which that collaboration can occur.

Read the full report here.

Data Stewards Network

Responsible Data Leadership to Address the Challenges of the 21st Century

Michelle Winowatan

Written by

Data Stewards Network

Responsible Data Leadership to Address the Challenges of the 21st Century

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