Data collaboratives (sometimes called “corporate data philanthropy”) are a form of collaboration in which participants from different sectors—including private companies, research institutions, and government agencies—can exchange data and data expertise to help solve public problems.
Types
Data collaboratives can take many forms. They can be organized as: Public Interfaces: Private firms publish select data assets to be public for use by external parties. Firms typically present this information as Application Programming Interfaces (APIs) or data platforms. Trusted Intermediary: Private sector firms share data with partners from public, civil society actors, and academia. Data can be brokered by third parties, who provide valuable data under fixed terms and time limits to non-private organizations. It can also be run through third-party analytics, which shares data with data stewards to run analysis and share those findings with external actors, providing the outcomes of the data without exposing the sensitive information. Data Pooling: Multi-sectoral stakeholders join “data pools” to share data resources. Public data pools allow partners to openly access and independently use the data, while private data pools limit access and contribution to the information. Research and Analysis Partnership: Organizations share data and “proprietary data assets with public and academic institutions to analyze and advance a public objective. Through these data transfers and data fellowships, access to and terms for use of data are highly controlled. Prizes and Challenges: Organizations make data available to qualified applicants through competition for innovative use or platform design to add value to the firm. Open innovation competitions, like LinkedIn’s Economic Graph Challenge, allow for open and broader use of data by many independent users, while selective innovation challenges give limited data access, narrowing the scope of its application to a specific situation. Oftentimes, competition members are bound to data responsibility guidelines. Intelligence Generation: Companies use data to build shareable tools and release them for public use. Although no formal, direct cross-sector sharing occurs, it lays the foundation for knowledge transfer and a culture of open, data-driven analysis.
Reasons for data collaboratives
The big data boom has demonstrated the power of data to inform and design public projects in an accountable and iterative manner. However, unequal access to certain data across sectors limits the ability of groups to find, access, or be made aware of valuable information, hindering social innovation. Data collaboratives create networks that bridge access and knowledge gaps by bringing different sectors together to share data to address social challenges.
The GovLab argues data collaboratives wherein a private sector data holder shares data with other groups tend to be motivated by a desire for: Reciprocity: Sharing data with others can guide mutually beneficial business decisions. Research and Insights: Sharing data can spark new and innovative approaches to issues. Reputation and Public Relations: Sharing data, especially to advance public issues, can bolster the image and reputability of a firm, attracting new socially-conscious clients, talent, and followers. Revenue Generation: Corporate data can be sold to data collaboratives, generating novel revenue streams. Regulatory Compliance: Data collaboratives can help corporations advance transparency and trust by establishing and following data sharing protocols. Responsibility and Corporate Philanthropy: Data collaboratives allow businesses to drive meaningful corporate social responsibility programs.
Data collaboratives can help respond to service delivery and emergency preparedness and disaster response problems. Robert Kirkpatrick, Director of UN Global Pulse noted that “the lack of innovation [in these sectors have] resulted in a failure to protect the public from what turns out to be preventable harms.”