Knight Lab Comparallel

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About the Project

Comparallel is a project in conjunction with Northwestern’s Knight Lab. Comparallel helps journalist extract richer insights from economic datasets such as those found on FRED.

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.

Comparallel can be found here. The product page is here.

Personal Impact

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.

At the tail end of the project, I developed the script to find sub-correlations in data (via R). Additionally, I and another teammate developed the actual concept in JavaScript.

Tools Used

javascript-cardimages     R


Project Overview

Specific project details can be found on the product page.


Compare JourneyMap

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.


final journey map

Based on those issues, we found a design direction that would re-define the journey journalists took to find data.

Digital Proto

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.).