Time horizons, expectations, and determining minimum viable interventions
This is the third 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 first full meeting of the Community Data Hubs (CDH) Advisory Group on May 17, 2023. There were three main questions¹ that drove the conversation:
- What kinds of scenarios would assist us in tackling conceptual questions² related to environmental data stewardship and governance?
- What kind of data uses will the CDH model be working with — i.e., data meant for integration into regulatory processes or for increasing data awareness?
- How can you manage expectations with communities or data holders when the government has strict policies that contradict community data needs?
What kinds of scenarios would assist us in tackling conceptual questions?
This was mainly a logistical question for the group, but it sparked some key considerations about choosing scenarios that look at managing community expectations and time horizons for data usage, storage, and management. The group pulled out some guiding principles and questions when it came to these scenarios:
- How do you set expectations for how data will be used?
- How can we build processes that maintain or increase data’s value for uses not determined during collection, in the future, or for other communities?
Time was also a recurring theme: it matters how communities collect data now for how they might use it now, as well as in the future. Our Advisory Group encouraged us to envision how to have community conversations around how current data collection will support potential future uses. The question of changing expectations over time — and the need to address those expectations — also came up, since the data’s value might be different on different time horizons, i.e., a piece of data might be collected in 2023, but used as evidence or research in 2025 to demonstrate change or cause for action.
“Be the ancestor of your future data self.”
What kind of data uses will the CDH model be working with — i.e., data meant for integration into regulatory processes or for increasing data awareness?
How can you manage expectations when the government has strict policies that undermine community data needs?
The second and third questions were discussed jointly, as they interact with one another. Considering these questions introduced additional questions:
- What are the user stories that data seeks to represent?
- What intake processes with communities would help us answer these questions?
- How can we determine minimum viable interventions for responding to a community’s priorities with data?
Understanding the user stories and valuation linked to the data with any potential data stewardship or governance tool is a key consideration moving forward. User stories will help us determine what questions to ask, what aspects of the CDH model could be helpful, and what kinds of additional expertise we might need to seek out. An example we discussed concerned a fishery network in Sitka, Alaska decided to create a data management and governance system that was so easy to use and useful (for sensemaking) that the local government and regulatory agency would want to use it, interact more with the data, and cater regulations with it in mind. One guiding insight from this example is that the group had clarity on how they wanted to use and exchange information from the beginning, so the data and infrastructure ultimately worked for them and fulfilled their goals in helping the network; influencing regulation would be a secondary benefit but not a need. While this is a very specific user story, it demonstrates how data storage and usage can reflect a community’s perception of data’s value.
In our conversation on the intake processes with communities, we looked at two resources: GovLab’s Data Responsibility Journey and the Data Patterns Catalogue by IF. The Data Responsibility Journey from GovLab identifies questions that can be asked at different data stewardship waypoints, such as planning, collecting, or processing data, while the Data Patterns Catalogue includes guides to help teams make decisions about collecting and using data about people. These guides could assist us and the communities we’ll work with in determining what technical tools are available and adaptable for different purposes, e.g., giving and removing consent, giving or getting access, or doing security checks.
Potential minimum viable interventions for a community’s priorities with data can look differently depending on the context — understanding what capacity already exists is essential in creating or adapting data governance systems that are appropriately sized and purposed. Some communities may need an intervention like a set of data standards and a digital platform; another community might have sharing priorities that require more complex infrastructure that requires consent and privacy controls. Determining what is appropriately sized and purposed for a given context can help us avoid creating overly complex or redundant processes, or creating something that requires cost- or capacity-restrictive maintenance. Not all data contexts need a complex hub or governance structure — a major priority in designing a CDH model will be understanding what questions to ask and what is available to create a tool that is appropriately sized and purposed.
Insights and questions to revisit during the CDH Model Co-Design Process:
- What is the minimum viable intervention that maintains or improves on already existing processes with data? How can we determine what this is?
- What kind of intake processes can we employ to ensure purposeful interaction with communities and data stewards? What can we learn from existing resources, and what needs to be created or adapted for this model?
- These originated from the general “bike lot” of questions that surfaced from our introductory calls with Advisory Group members. The full list can be read in our second blog.
- Environmental data stewardship and governance involve complex interactions between social, technical, and political layers, with questions regarding consent, privacy, incentives, storage, accessibility, and ownership/control, and sharing. These interactions differ based on the data, its preferred usage, steward values and priorities, and political landscape.