The central challenge is trust. Data integration and reuse at scale can create significant value for all parties – data contributors, and data reusers – but only if people can create and maintain a high-trust relationship in regard to the transactions they are participating in.
The Data Commons working group has concluded that existing models for enabling data integration and reuse fail because they do not address this central challenge. The dominant approach tends to build technically focused point solutions that are highly specific to the particular context they are operating in. Moreover, data reuse interests tend to address only their own needs – frequently overlooking the interests of the data contributor. At best there is lip service to consent, minimal personal control for the contributor, or at worst coercive harvesting of data. Because these attempts fail at trust, they become costly and hard to scale.
The alternative proposed here is to establish a Data Commons. A commons-based approach builds trust and scalability into the DNA of the solution. This is achieved by adhering to a set of principles and goals which embed an inclusive and open approach to data for everyone who is participating in the commons. In addition, by setting up a “protocol-based approach” the Data Commons is scalable and lower-cost.
The Data Commons exists primarily to maximise the value of the participants’ data for the participants, and it is co-designed and co-governed by them. Moreover, the design aims at creating a data reuse ecosystem that rewards and encourages data reuse, rather than the on-selling or trading of data. The benefits of specific data reuse are valued (and in some cases sold for profit), but the data itself is not traded or owned.
In our proposal, data is treated as a common-pool resource. This is quite different from existing models that seek to either control or trade data based on agencies with exclusive monopolising interests in data reuse. The principle of universality encourages a protocol-based approach to the rules and technology, such that the solution becomes low-cost and easy to scale.
The work of building a Data Commons approach will involve two parallel processes.
Co-designing the Commons Protocols:
Community-forming and alignment around the Data Commons principles, and then co-design of data reuse protocols – from technology protocols through to social protocols.
Kick-starting the Commons:
Deploying specific high-value data reuse solutions that use the Data Commons protocols as the basis for their relationship with the commons community.
A Data Commons approach requires forming a Community of Interest around the high-level Data Commons design principles, and then facilitating more detailed conversations about how that community wants to manage data sharing and reuse through developing the community standards, institutions, and protocols to make high-trust sharing easy.
The outcome of these conversations about “how we do things around here” is a set or “stack” of protocols that participating organisations and individuals can commit to. The Data Commons Blueprint outlines seven challenges (or layers) that make up the “Protocol Stack” that underpins the Data Commons. This is how we enable high-trust and high-value data reuse transactions to take place across the community and between its various interests.
At the same time, there is another kind of work that needs to take place which involves building value in the commons. This is done by identifying, inviting,and supporting innovators and entrepreneurs to kick-start specific data reuse solutions that are based on these commons protocols. We need to build some data reuse opportunities that are valuable for members of the community, so that they will use them. This involves recruiting people and organisations who have pressing data integration and reuse challenges, and supporting them to use the Data Commons protocols to build their data solutions. This adds both data and users to the Data Commons and makes it more valuable for the next innovator, who now has even more data to work with, and so grows the value of the commons.
We need to build some data reuse opportunities that are valuable for members of the community.
Key questions here are how and where to start to create early value, and how to enable the commons-based approach to be attractive to other participants? How best to add value to the community to establish a network effect to grow the value of their shared asset? How to convince potential data reusers that it is more valuable to build a commons-based approach rather than a one-off point solution? This will be harder in the first instance and will take a leap of faith, since there is initially no valuable data in the commons. It will get easier over time as the perceived risk of being in the commons is seen to be outweighed by having access to a wide variety of integrated data to develop high-value products and services.