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A community-based approach to data governance^[1], in a quest to re-imagine our relationships with data and technology, is gaining traction. As it currently stands, the dominant model of data governance is such that a data controller dictates use, access, and control over the data it collects. The idea of “community data governance” stems from a desire to restore agency and rights to individuals who find themselves on the other side of the digital divide, are in unequal relationships with platforms, and unable to advocate for their own digital rights. As a result, user-led, cooperative frameworks for data governance can re-balance power dynamics in the digital economy.

There are several arguments for stewarding data in this way: (1) Data is more valuable in the aggregate, and thus a community or collective should own its economic rights; (2) Externalities on decisions of use, access, and sharing of data are usually felt by a range of individuals; and (3) As such, communities should be allowed to collectively take decisions on their data.

“Community data governance” is rooted in critical work at the intersection of community ownership, data justice, and economic rights. In Elinor Ostrom’s radical imagination of cooperative alternatives to existing market structures, she theorizes governance structures for “common pool” resources such as land or water. Ostorm’s work has been widely extended to understanding and developing frameworks for data governance. The idea of “data justice” discusses vulnerabilities created and amplified by digital technologies; in her work, Linnet Taylor discusses the need to push for “fairness in the way people are made visible, represented, and treated as a result of their production of digital data”. As a result, rights and individual control in the context of data is intricately linked to social and economic justice, especially for communities who lack agency and knowledge to do so.

These ideas have been widely adapted to re-imagine current scenarios of data and technology. Nathan Schneider discusses an alternative to the monopolization of Big Tech, in which late-stage startups “exit” to a model of cooperative-ownership by a community with vested interests, redistributing wealth to users that built it. Workers and platform users are vying for economic justice through collectivization; data unions and platform cooperativism are two such ideas.

As economic interests in the digital economy rise, so does global policy discourse on community and non-personal data regulation. The European Strategy for Data surfaces the importance of data for community interests, creating “data spaces” that can improve healthcare, enable job creation, or create safer transport systems. Early August, the Indian government released a draft Non Personal Data Governance Framework that defines community data: “Non-Personal Data, whose source or subject pertains to a community of natural persons”.

Alongside these policy debates are developments in alternative thought on data governance structures that are deeply rooted in the idea of “community”. Sylvie Delacroix and Neil Lawrence highlight the need to stray away from conventional regulatory frameworks for data sharing, arguing that power that stems from shared and aggregated data should be returned to individuals through a “bottom-up” approach that provides heightened transparency, agency, and control. In addition, a community-centric orientation is highly visible in Indigenous data sovereignty;^[2] enduring colonial structures, concerns over collective privacy, etc. and have sparked a movement to reimagine data governance structures between Indigenous communities and governments. Similarly, data cooperatives enable community empowerment: incorporating ideas of data justice to shift the narrative from an asset-based control framework to one centered on agency, rights, and bargaining power.

However, several gaps remain in the current imagination of community data governance: (1) The idea of a “community” in itself is vague, and social, physical, or digital boundaries of these communities are undefined and context-dependent; (2) Communities are inherently heterogeneous, and as a result, individuals will likely belong to multiple communities; and (3) These communities may have overlapping interests; if users are part of multiple communities with conflicting interests, who assumes decision-making responsibilities?

This summer, we explored the idea of community data governance through the lens of migrant communities in India. After the government of India mandated a nation-wide lockdown in response to the COVID-19 pandemic, thousands of migrant workers in urban India were left without work, critical supplies, or support. The scarcity of data on migrant workers left the government and civil society unable to provide relief and grasp the scale of the issue at hand. As a result, there was, and continues to be, an enormous paucity of information about migrant communities in India. Stronger data drives stronger policy – at The Data Economy Lab, we believe a community-led, bottom-up data governance structure can help vulnerable communities, such as migrants, re-claim agency and power in their relationships with technology. Drawing from global discourse on community data governance and data stewardship, we chart considerations for such a structure.

There are several complexities in designing a data sharing system for migrant communities in Indian urban cities. As a result, the nature of this question, in particular, lies at the intersection of data justice, platform work, the nature of urban dwellers, and migrant movement. Many migrant workers rely on digital technologies for communication and safe access to information, or use the biometric identification system, Aadhaar, to access rations and financial support from the state. In addition, migrant workers in India are largely employed by the gig economy.While these platforms enable flexible and accessible work, workers often lack protection against social and technical harm. The flexible and transient nature of the migrant existence, coupled with poor information architectures, leaves this population largely invisible to data systems. Often, migrants are only accounted for through informal, offline structures of support and community.

Based on the migrant experience, below are important considerations for a structure of community data governance for migrant communities in India:

1. Most migrant workers are unlikely to be able to take active decisions about the use and access of data generated about them. A data steward, an intermediary who works in the interest of these users, is critical in this regard.
2. Many migrant workers are in unequal relationships with platforms that typically employee them (Uber, Dunzo, etc) and lack protection from both social and technological harm. As a result, for a population that is frequently on the other side of the digital dive, physical touchpoints are critical.
3. Any data sharing system about vulnerable communities should be held accountable to the public. The involvement of local or community stakeholders allows for transparency in the lifecycle of data.
4. Migrant workers who live in cities are unseen by mainstream data systems. To account for this, data collection from a worker’s place of origin can help paint a clearer picture to understand migratory patterns across the country, rather than in the cities they often move to.
5. The involvement of other intermediary organizations, especially civil society organisations, can serve as critical access points for data collection and sharing. Civil society organisations can play a strong role in building trust amongst vulnerable populations for the safeguarding of their rights.

A sustainable data sharing architecture for migrant communities requires both pilot testing and further consideration, yet provides a valuable framework for thousands of communities who may benefit from such an approach. Technology is and will continue to permeate communities around the world, and it is more critical than ever to rethink data sharing structures that avoid techno-solutionism, are designed in the interest of users, and enable service delivery in a meaningful way. Data stewardship is one such approach through which the idea of community data governance can be realized as a steward can help build corridor-corridor data streams between geographies, coordinate data response at the local and community level, and reinforce a system that protects vulnerable populations such as migrants.

^[1] Data governance is practice by which data is accessed, controlled, and shared.

^[2] Indigenous data sovereignty is the right of a nation to govern the collection, ownership, and application of its own data. It derives from tribes’ inherent right to govern their peoples, lands, and resources.