A data collaborative is often what you have when private sector data is combined to help inform public sector decisions. At least this is one aspect. Data in a collaborative could be shared strictly between partners, with an independent third party who manages access to the data, or publicly online. If you feel confused about overlapping approaches at this point, keep in mind that the 7 approaches explored here are simply frequently mentioned in literature. These are not definitive categories from which to understand all data governance. What has long been known as the ‘open data movement’ has often centered on ‘opening’ government data to the public. With data collaboratives, on the other hand, the usual idea is to take data that is proprietary or siloed and make it available to inform research or policy. At the city level, for instance, collaboratives of mobility data from private sources (say, from rideshare companies) could be used to help inform urban planning. At the global level, humanitarian data can help United Nations agencies respond to emergencies like the COVID-19 pandemic. Data collaboratives are not entirely uncommon, but it’s hard not to see untapped potential for more. As part of an extensive research project, GovLab has assembled a database of 200 data collaboratives. Among the listed projects is Global Fishing Watch. They combine satellite vessel tracking data from six countries to create an online map that tracks fishing activity for better ocean management. Such big data analysis of ships and ports is also done by others, for profit. But baked into the notion of data collaboratives, as explored here, is that they act as responsible data stewards to empower their members or the general public to solve societal problems. Why would companies want to participate? It could be for “reciprocity, revenue, research, regulatory compliance, reputation, or responsibility,” suggests GovLab in their research. Meanwhile, even proponents agree that a raft of privacy concerns and questions about consent and ‘big data bias’ arise from repurposing data collected in a corporate context. That is why further study and analysis is necessary. Technical platforms for coordination and exchange of data between providers and users are a necessity, as are interoperable data standards and frameworks. SharedStreets is an example of a non-profit provider of open source software for the “seamless exchange of transport data”. BrightHive is an example of a for-profit startup that offers consulting on legal frameworks and software for data collaboratives.
Global Forest Watch combines datasets about forests with satellite imagery of Earth to visually demonstrate the effects of deforestation from above. It’s a partnership of dozens of companies, organizations and research institutes convened by the World Resources Institute. Since 2014, the platform has helped thousands of people monitor and manage forests and stop illegal deforestation and fires. Since 2019, they have also operated a “pro” service for businesses concerned about deforestation in their supply chain. Their online maps currently show that the equivalent of a football pitch of primary forest is lost every 6 seconds.