Tax-Aid Data Visualization
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Tax-Aid’s data collection was used to create a dashboard that provides important insights into resource allocation and past trends over time for the organization. The data was also used to create an algorithm that cleans and organizes future data for the organization, thereby relieving the client of a significant bottleneck.
The team worked on producing the following deliverables:
- Tableau Dashboard: A visualization of around a decade’s worth of data. The dashboard shows the geographical distribution of volunteers and financial tax information.
- Colab File: Algorithm that cleans Tax-Aid’s data so that the client no longer has to manually organize and clean future data.
- Demo video: A video that walks the client through the process of using the cleaning algorithm, as well as how to plug future data into Tableau
Our client needed to visualize data in order to drive important future decisions for the organization, so they were our primary users. The swaths of data were hard for the client to manually clean especially since the data did not follow a uniform organizational structure, a problem we solved with automation. We needed to create something that our clients could use easily, a constraint we had to keep in mind as we moved forward with the project.
Tanya Hao, Columbia ‘22
Katherine Yu, UCLA ‘21
Justina Tran, Fordham ‘20
Cameron Ines O’hara Linhares-Huang, Stanford ‘23
Byeongjo Kong, MIT ‘21
Katie Chang, UCLA ‘23
Keeping in mind that we needed to create a painless solution for the client, we chose to use Python in Colab and Tableau; both relatively comprehensive to use for those that are not familiar with code. To create our dashboard, we followed an iterative process of design in Figma and development. We ensured client satisfaction by regularly receiving feedback on our prototypes, as well as running demo meetings to make sure the client was able to use our creation.
Due to NDA, we are unable to disclose any project screenshots or demo.
Tax-Aid will be using our dashboard to run future resource allocation decisions, and will save a significant amount of time not having to manually process data.