A collaborative environment among partners, or chefs, is necessary to enable data sharing. To do this requires considering partners’ interests and needs, including the motivations and resistance factors described in the previous section, and placing them front and center in the partnership. Seeking to address the problems and pain points of data sharing partners requires listening and understanding their points of view. Being responsive to members’ needs ensures that many of the fears and concerns around data sharing are directly addressed.
Focused on broad mandates for social good, many data sharing partnerships struggle to get buy-in from partners in their early stages. Developing applicable use cases early on in the planning stage of a partnership can help potential partners see the value of participating in data sharing.
For example, the California Data Collaborative (CaDC) chose to leverage its limited starter funding by prioritizing workstreams that provided value to data sharing partners and that fostered engagement and a sense of ownership among organizational champions and data stewards. “We could have prioritized creating more robust software and technical infrastructure,” said CaDC’s Chief Data Officer Christopher Tull, “but we wanted to create concrete value for our members who were providing us with the data and communicate that value to them and engage them in the process so they felt a sense of ownership over the Collaborative [to ensure its survival].”
A flexible approach to providing value to partners
Nascent data sharing partnerships that are seeking to demonstrate value to members must make crucial decisions about where to invest limited time and financial resources. Vital to this approach is the ability to pivot to new use cases when things are not working or are not paying off. Building on success and abandoning dead-end projects ensures that, while a specific use case may not continue, the partnership as a whole succeeds.
The CaDC learned this early on when the partnership sought to build out projection tools to model the changes in revenue and payments that would result from utilities adjusting their rates. The tool (RateComparison, available on Github) was used much less widely than CaDC expected. In fact, the primary module that CaDC observed partners accessing was being used, not to model rate change effects, but for budget forecasting. The CaDC abandoned the time and resource-incentive project and shifted to focus on budget forecasting to meet users’ needs. A related tool developed by CaDC, the Open Rate Water Specification Tool, was more successful and helped to power a state-wide rate survey of utilities in California in 2017-18.