DataBasic’s development was generously supported by a grant from the Knight Foundation’s Prototype Fund. The key hypothesis they funded us to test? That building online tools for working with data for learners is different than building for users. While the output is available here at databasic.io, sadly our grant has ended 🙁 We wanted to take this opportunity to share some of our experience from the close-out event Knight just hosted with Maya Design.
Wrap-Up and Reflection
The event began with some scaffolded reflection on the 6-month grant cycle. We spoke with other grantees in small groups about goals, pivots, successes, failures, learnings, and next steps. Many sticky notes were used!
One aspect of the event was the amazing set of short 5 minute talks describing the wide variety of projects. Here’s Rahul speaking about what we’ve learned so far:
The key here was the focus on learnings. All these small projects Knight funded create an aggregate picture of learnings from prototyping, a methodology we deeply believe in.
With prototypes built, how do you get folks to use them? Our approach with DataBasic involved our audiences in the development of the tools, via multiple workshops, so we had investment from them already. The focus of this Knight event was to explore the answer to this question more, to help grantees refocus on outreach and marketing.
The folks from Maya led us in an exploration of stakeholders, their needs, and the benefits we provide. While we had done much of that thinking before, formalizing it on paper was incredibly helpful to think about holes in the outreach we are doing post-launch.
Then they led some great hands-on exercises to help develop elevator pitches from a template. Here’s one we wrote about why DataBasic is useful for community activists:
For community activists, who need friendlier tools that help them learn how to work with data, DataBasic.io is a suite of online tools for working with data that provide playful and focused, on- and off-screen activities unlike other online tools for finding stories in data, because we have designed and built them with and for learners.
These pitches are a great way to clearly describe what your project brings to its intended audiences, even if you have already thought about it a ton.
As we’ve written before, this type of work is more like a marathon than a sprint. While we are excited about the enthusiastic response after our launch, we realize that we have to pay close attention to feedback from our various audiences to keep iterating. We of course hope DataBasic can be useful tools and activities, but we also want it to evolve into a model for how to build data-centric tools for the huge population of learners starting to work with data. Many thanks to Knight for helping us start down this road, and we look forward to getting their, and your, feedback!