Dare to compare.

Data is hard to contextualize.

Let's walk through your average scenario exploring economic data. First, you might search for a term on FRED or another economic database. If you're analytically savvy, you might know how to interpret the graph you’re given. But generally, your search starts and ends with one graph, often lacking context for its curves. But what if there was a tool that allowed you to discover contextual clues about economic data? Meet Comparallel.

We want to help you better interpret data.

When you search for an economic dataset, Comparallel will provide you with a list of correlated datasets, ranked from highest to the lowest based upon number of correlations. Graphs of the correlated datasets will be displayed below the original dataset’s graph and each of the correlated datasets will highlight “key correlation areas.” Upon clicking these areas, a related Google search will open with automated tags related to your datasets. While correlations are not causations, Comparallel inspires you to start exploring whether these datasets may be related.

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How to Use Comparallel from Gauri Rangrass on Vimeo.

About Us

Nicholas Garbaty is a senior majoring in journalism and International Studies at Northwestern. His interests include video production, storytelling, and culture and society.

Raven Haynes is a senior majoring in journalism in the Medill School of Journalism at Northwestern.

Saurabh Rane is a first-year Masters student studying Engineering Design Innovation at Northwestern. His interests include human-centered design, data visualization, and product design.

Gauri Rangrass is a senior majoring in Journalism and Psychology at Northwestern. As an student in the Integrated Marketing Certificate program, she’s interested in content marketing, strategy, and analytics.

Caroline Vakil is a junior majoring in journalism at Northwestern. She’s interested in how data plays a role in investigative journalism.