Every month Aapti Institute with Digital Commoners Network hosts Data Governance Deep Dives. An informal session that connects practitioners and theorists, while we together explore real-world data governance models.

This deep dive was an attempt at understanding data unions and how they are operationalised through specific organisational models. The specific model conceives data unions as frameworks for monetisation which allows people to easily bundle and sell their real-time data and earn revenue. On its own, data does not hold much value, but when combined in a data union, it aggregates into an attractive product for buyers to extract insights. Data unions and crowd-selling allow for the creation of unique data sets by incentivising trade directly from the data producer.

Unpacking Data Unions

The organisation in question is an ecosystem enabler that provides infrastructure for constituting data unions. It consists of three elements – a network, the marketplace, and a payment interface.

• The network: The network is a scalable, real-time messaging system, which enables applications and devices- such as IoT sensors, connected cars, etc- to share and trade their data. Real-time data streams from members of a data union and is carried through the decentralised network directly to buyers, so data union administrators do not have to be custodians of permissioned data. The real-time component is important because this allows for data to be sent without it being censored or disturbed or deleted.
• The marketplace: Monetisation of data is possible because data becomes attractive after bundling through crowdsourcing otherwise siloed data sets. Members can choose to join the data unions that they may be interested in and data unions can price the data according to their goals. The intention is that different data unions with different types of data and different pricing and privacy options would create a variety of options and platforms for users to choose from. The marketplace consists of real-time data products- ranging from cryptocurrency feeds to pollution data. Data union products can be created and managed within the marketplace and buyer payment rails.
• Payment interface: This is a mechanism to ensure that people get micropayments for their data.

This framework ensures that data buyers can buy data in the marketplace. When a data package is bought, a part of the payment goes to the data union (admin), as per their determined fee.

How are data unions structured under this organisation?

Unions are free to structure themselves as per their choices. The creators of unions decide on their pricing, consent mechanism, what type of data is collected, which third parties may have access to collected data, and what options users have. While the organisation provides the infrastructure to data unions, its interference with governance is limited or absent. Instead, the organisation provides toggles/ recommendations for governance, but these are largely advisory. Internally, the organisation follows a token governance model, where token holders can vote on token governance decisions. Specific unions constituted through this organisation/platform can also issue their own governance token, enabling member participation for governance.

What types of unions/ applications are currently in operation?

The platform/organisation engages significantly with indie developers, who are very small teams or just a few people, who develop unions part-time. Certain unions are growing well as they have expanded to 5-6 employees and are currently raising VC money.

They also have a consultancy arm that is collaborating with the World Wide Fund (WWF), and the Union Bank (Philippines), in developing an application that has data union components. The platform has also instituted a data fund that issues small grants for data projects.

Concerns

The platform/organisation’s focus is on the development of an underlying infrastructure that enables a plethora of different data-sharing arrangements. However, during the session, some critics voiced concern that the design of the infrastructure preferences some use cases over others.

That is to say, infrastructure is not neutral. For one, the strong focus on monetisation inherent in the model could lead to equity issues, and become exploitative due to commodification of data, forcing users to share more data for more money. The infrastructure could potentially be exploited by companies to build unions or applications that do not adequately address issues of consent or privacy, due to the open-source nature of the software.

If the aforementioned were to occur, then the burden of choosing the right data union falls upon the user. A significant portion of decision-making in respect of privacy and consent, and the creation of standards and protections, are left to the unions, therefore it may become necessary for more specific regulations in respect of data unions to be put in place to adequately safeguard users.

Relatedly, we would like to understand what measures the platform can implement to allow the unions that use the platform to build collective governance models that truly consider the needs and risks to their members, both individually and collectively.

Remaining questions around regulation

The Data Governance Act and the Data Markets Act, which are yet to come into force in the EU, are likely to have a significant impact on the functioning and use of both data unions and data trusts. The approval of Proposition 24 in California is also significant for technology businesses in light of the start-up concentration in California.

These regulations could potentially unlock new opportunities in terms of funding for new start-ups that aim to leverage new technologies for data. However, it was noted that balance becomes important where regulation is concerned, as over-regulation of the data sector is likely to adversely impact innovation, according to the speaker. Data unions can also play an important role in alerting legislators about the opportunities in open data ecosystems and open data spaces, and the use cases that may arise out of them.

While compliance with the GDPR may address some privacy concerns, data unions are new. Therefore, regulations may overlook novel use cases. Data ownership is a key issue that remains untackled. Creating sandboxing environments may give opportunities for governments to understand new technologies and use cases and accordingly regulate them. The lack of uniformity of data regulations, even within the EU, across different sectors, creates significant legal compliance barriers.