Comparallel

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.

• Correlated graphs:

  For each dataset searched, a list of correlated datasets appear below. You can explore the suggested list or click on another dataset to see its recommended list. By having a generated list of datasets for each economic indice, it allows you to explore a variety of factors. You will also be able to see where the strongest correlations exist between data. Time increments will remain consistent between graphs and all axes and graphs will be labeled.

• Correlation zones:

  To better visualize where datasets are the most correlated, the segments of data that have the highest number of correlations will be highlighted in a reddish tint to emphasize these segments.

• Data traceability:

  Clicking on the “Source” hyperlink underneath each graph title will take you to the respective original dataset in FRED.

• Google search:

  Once you click on a correlation zone, a Google search engine will appear with an automated list of tags relating to that zone and its related datasets. From here, you can add, delete or modify the tags you want to plug into the Google search engine. Through this feature, you can find relationships between datasets that you didn’t know existed, or better understand why datasets are correlated.

• Hover glossary:

  You can hover over key terms to reveal a small pop-up box with a short definition and description. This will help you understand economic jargon that may make the data harder to digest.

How to Use Comparallel from Gauri Rangrass on Vimeo.