Intended audience: DataKind Volunteers
The process of exploring a potential project with a partner organization should begin with discovery calls. These calls are an opportunity to deeply understand the organization and the form a potential project may take. Since finding problems can often be harder than finding solutions, productive and creative conversations at this stage can uncover information that will shape a potential project’s focus, impact, and sustainability.
Note: Remember that these calls can lead to an engagement exit point, and that’s okay! Not all potential project partners that we meet with will be a good fit for a DataKind project.
This article contains resources from across DataKind’s network for conducting discovery calls. Feel free to use whatever parts you find most useful, or add your own resources to this article for others to take advantage of!
If you use the standard folder structure, check out the “Discover” folder of your project before getting started. You will find resources you can share with your project partner organization in advance to frame the conversation (such as the 6 components summary), as well as resources to guide the conversation (such as this Jamboard template for collaborative conversation and activities contained within).
Agenda
To create the agenda for your discovery calls, review the steps in the Discover Stage and begin working your way through the list. See if you can check off any steps with the information you have on-hand or can find online about the partner organization, and prepare questions to ask in the call to address the remaining steps. You can also choose from the Discovery question bank.
Some DataKinders like to use a POP framework for calls with partners. For example, here’s a template used to determine if data.org applicants would be good partners for Impact Practices. You can also find a sample agenda and talking point notes for a generic discovery call here. Feel free to make a copy of either template and customize it to fit your needs.
Example call series
Here is an example sequence of discovery calls for an organization where you have some amount of information on their data maturity (e.g. they are a repeat project partner, competed in a data science challenge, applied in as a lead, etc.)
| Session topics | Sample framing questions |
|---|---|
| Problem identification Activity/tool: pre-mapped logic models based on a shared theory of change (from initial case note) and publicly available theories of change; draft problem statement(s) | What are the pain points the organization faces? (confirmation \& expansion of existing concept note) * Who experiences these pain points (e.g., a specific program, specific role/position affected, specific geography)? * Over what timeframe is the pain experienced? * What does this pain prevent the team from achieving? / How does this pain point limit success/organizational ability to create change? What else keeps you up at night? ** Cautionary note: It is easy to hone in on the first problem and/or proposed solution during the first discovery call. Avoid this problem by closely following the points below. |
| Solution design Activity/tool: Pre-mapped feedback loops based on an understanding of how solutions are designed in the space (from initial case note) | What might the shape of a solution look like?* What are the metrics of success? What tradeoffs are acceptable to achieve certain measures of success? + When does a solution need to be in place by? + What level of usage or accuracy needs to be established? * Who is the user (at the organization)? Who is the beneficiary? * What are the environmental conditions of the final solution? (e.g. is this on a mobile phone? A place with no electricity?, conflict zone, other constraints?) * What amount of adoption support might be required? How fast would the solution be outdated? What are pilot or intermediate approaches to creating those solutions? What other organizations or entities could benefit from this proposed solution? |
| Proposed data tools conversation Activity/tool: stakeholder mapping and needs assessment | Initially proposed end-state solutions were a series of data tools. Is that still of interest as the end state?* How do we know that data tools solve for the stated pain points? * Absent these data tools - how is data currently being accessed and used? What informs decision-making? * What are the end-users’ levels of comfort with data? What is their level of trust of data, and what is that trust based on? * What are the obvious gaps in the data and how do you solve for the gaps? Proxies? * What timeline would data tools need to be created and implemented by? * What collaborating partners would need to be on board for data tools? * How fast would the solution be outdated? What would it take to sustain it? * What is the worst that could happen if we fail? If we succeed? |
Contributer(s): Caitlin Augustin, Caroline Charrow, Jack Craft, Erin Antcliffe, Matthew Harris, Arina Igumenshcheva, Ben Lebovitz, Sebastien Ouellet, Seward Lee, Srivalya Elluru, Dulcie Vousden, Rachel Wells
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