About the Project
Our team initially conducted user interviews of journalists with different levels of data literacy to see how they consumed the rich trove of free data from the US Government. Our conversations with younger journalists illustrated a problem; while datasets were publically available, most newer journalists did not have the experience to analyze them on a deeper level – particularly economic data sets.
From there we developed the alpha version of Comparallel. Comparallel helps users find correlated datasets, quickly identify macro-level trends, and overall afford users to dive down the ‘rabbit hole’ of data.
My main impact was in the initial human-centered design research and product development.
Being the only person on my team with a design-thinking background, I designed and conducted most user tests/interviews. That research was used by our team to design features that would resolve the tensions uncovered.
Specific project details can be found on the product page.
Our team first reached to journalists of varying levels of data literacy – from FiveThirtyEight journalists that do deeper data dives for their stories to local economic reporters that report basic economic indices. We mapped the process journalists currently use to better visualize their issues.
Based on those issues, we found a design direction that would re-define the journey journalists took to find data.
Using that information we tested different ways to display data. First starting with a paper prototype to get initial formative feedback from users, then later moving to a static digital screen to get more UI feedback.
In parallel with testing UI elements, we made a prototype via Tableau to test interactive features (e.g. zoom, tooltips, etc.).