Building a community-first data ecosystem

This is the fifth post for the Community Data Hubs Documentation series. This series will document the thought and conversation trajectories within the process of creating the building blocks of our Community Data Hubs model and OEDP’s broader data stewardship work. The first of these blogs will document the progress of the Community Data Hubs Advisory Group, which is working alongside OEDP to tackle conceptual questions related to the model, including social and technical infrastructures, stewardship, and community data.

This post documents the third meeting of the Community Data Hubs Advisory Group on June 14, 2023. For this meeting, we asked the group to read a report from Digital Public and the Cape Cod Commercial Fisherman’s Alliance on “building a fisherman-first data ecosystem.” As electronic monitoring data on fishing vessels rapidly change and modernize, fishermen often feel excluded from data monopolies. There are opportunities for this fishing community to build a parallel data collection ecosystem. The report explores possible technical and legal models for the New England groundfish community to manage independent points of access to their electronic monitoring data, and potentially find and pursue additional uses for it, including independent research and new business development.

This case and the potential models provided ample material to discuss three main questions:

• What role do data standards play in community environmental data governance?
• How do a community’s data goals and shared purpose influence the design of data governance systems?
• How can we find the balance between community motivation and inclusion and implementing a minimum viable intervention?

What role do data standards play in community environmental data governance?

Advisory Group members began by discussing the various legal and technical models laid out by this report, including data trusts, standards bodies, repositories, and data clearinghouses. Group members noted that data standards are often seen as low-hanging fruit to improve interoperability, which can assist communities in integrating their data into regulatory processes they must comply with. But the group also discussed the often high costs of developing data standards, mentioning a ten, hundred, million rule: it takes $10,000 to prototype standards effectively, $100,000 to create a viable specification, and $1,000,000 a year to reach widespread adoption and establish supporting infrastructure for maintenance. Group members added that technical expertise and leadership are necessary components for this level of data stewardship; an external partner may be needed to provide technical expertise in support of data stewards who will make decisions around which standards to employ. It was also noted that examining the incentives of ways that community data stewards can buy into this work is essential for scoping what kinds of data standards could be contextually appropriate.

How do a community’s data goals and shared purpose influence the design of data governance systems?

The report sparked questions about how different models of data governance can serve distinct purposes. A critical first step is to answer questions like ‘what do you want your data to do for your community?’ and ‘do you want to make your data reusable for someone in the future?’. How an external partner approaches these conversations with a community is equally important: how can we set up communities to consider uses beyond their current goals while acknowledging that external data reuse may invoke the threat of knowledge and information extraction without benefit? This prompts a consideration for our co-design process, namely that conversations about future data reuse must reflect both the priorities and the historical context in which a community partner is situated.

Group members also noted the importance of understanding a community’s existing sense of data’s value, and how this might influence governance decisions. For example, in the Cape Cod Commercial Fishermen’s Alliance case study, data stewards recognize the data’s economic value¹, and the consequent need for conflict to be managed effectively; unresolved conflicts can upset perceptions of fairness amongst data stewards and the broader fisheries community contributing data. Group members further noted that the community’s understanding of a shared purpose and their existing decision-making processes will affect how those legal and technical models can be adapted. For example, developing a data trust as a legal model requires that fishermen can organize into specific roles, like managers or trustees, and determine what permissions and powers each of those roles have. Since the Alliance already has decision-making structures in place, it might be easier to collectively vote and decide on who should fill the roles in a data trust. A consideration to pull from this conversation is to partner with communities who 1) are trying to articulate and align their shared purpose with data governance tools and 2) have existing decision-making structures that can support data management.

How can we find the balance between community motivation and inclusion and implementing a minimum viable intervention?

Group members brought up the potential need for motivation and inclusion: how might we facilitate conversations around data governance so that community members feel empowered to participate? What kinds of oversight and planning are needed to ensure that power is distributed and shared so that the project’s sustainability isn’t predicated on 1–2 people’s involvement in the project (i.e., the technical developer, institutional partner, or a key community leader)? Group members recognized that in order for a partner to care, the data or the data governance tool must be valuable enough to the community’s priorities and malleable enough to have impact. On the other hand, one group member brought back an idea from a previous session: depending on how much time is available, what is the minimum viable impact a project like ours could aim for? For example, data visualization and artistic inputs, while technically simple, might be valuable enough to get people engaged and see the data in a new light.

These considerations prompted discussion around the incentives necessary to generate communities’ interest in questions of data governance (which, at first glance, may not seem all that interesting). Group members mentioned the importance of ensuring credit for data contributions (FathomNet being a good example of this), celebrating at significant milestones (e.g., after a first round of data has been collected), and making super technical or boring fun. For example, Detroit Digital Justice Coalition hosts DiscoTechs: in-person multimedia and mobile neighborhood workshop fairs that allow participants to learn more about the impact and possibilities of technology within their communities.

Insights and questions to revisit during the CDH Co-Design Process:

• To address power-sharing and understand incentives, it is necessary to ask prospective partners about their communities’ shared purpose, what they hope to achieve with their environmental data, and what incentives would support group cohesion and momentum.
• As we conduct outreach, it will be critical to understand what decision-making processes and governance structures already exist and might be a part of the hub’s data governance system.
• It is critical to apply incentives like credit and contribution, but also to foster those that introduce fun, instill joy, and reflect a community’s shared connections outside of data. How can we build camaraderie into community data workflows in order to create more sustainable tools?

Other resources mentioned:

• Data Collaboratives with the Use of Decentralised Learning, Maciej Zuziak, et al, this article was mentioned as an example of describing data governance having layers: the regulatory layer, the organizational layer, and the technological layer.
• Polycentric governance of organizational data ventures, Olivia Benfeldt, an article that provides an organizing logic for data governance
• Standards webcomic from XKCD, a webcomic mentioned alongside the standards conversation
• Storytelling with Data, organization that helps people create compelling stories with data
• The work of Catherine Madden, consultant who supports organizations in creating a collective story shaping process
• The Data Science by Design series, articles that explore the creativity of data science

[1]: Electronic monitoring data from fishing vessels carries economic value in that this data is necessary for certain regulatory measures, and without it, the fishermen run the risk of not being in compliance and needing to pay large fines.