Visualizing Decades of Labor Data in Tableau Dashboards
Interested in understanding the evolution of labor unions in the US, I wondered if there was an better way to view and interact with historic labor and employment data.
Using Tableau, I built an interactive dashboard for users to get familiar with historic labor and employment data.
01 Challenge: Familiarize viewers without overwhelming them
Visualizing hundreds and hundreds of data points in such a way that it does not visually overwhelm the viewer would be my main challenge. Selecting visualization types and employing the right tools would be key to doing so.
02 Solution: Provide context, keep it simple, offer interaction, and keep things as consistent as possible
Using Tableau, I built an interactive dashboard visualizing historic labor and employment data. Using a separate, static dashboard, I captured specific snapshots of data to zoom in and illustrate to the user the impact of micro trends. Lastly, leveraging R’s geographic libraries, I mapped out key labor and economic variables to offer further perspective around the topic.
Above, Left: Historic Labor & Employment Data. Users can select a particular state or states on the heatmap to viewer specific membership rates and observe visual trends in the changes over time. Tableau dashboard linked here.
Above, Right: Snapshot, Before & After COVID-19. In contrast to the steady decline in union membership and steady climb in employment rate we see in the previous dash, a static view of the years before and after COVID-19 began shows a more volatile run of membership and employment. Tableau dashboard linked here.
03 Process: An iterative approach
Working with such a large amount of data, I knew organization and iteration would be key:
Start broad: explore union-specific data to understand historical patterns, relationships, and outliers within the dataset.
Employ best practices: leverage common visualization techniques that best communicate general historic trends.
Zoom in: dive deeper into sector data to uncover potential micro trends among labor union membership and employment.
Afterwards, peer review and user testing revealed key insights to inform dashboard revisions.
Heat map color scheme & key: user feedback indicated that color choice was impacting the viewer’s ability to interact with and understand the visual.
Line graphs: user feedback also suggested that it was less clear how viewers were supposed to interpret the line graphs when they appeared stacked vs. side-by-side.
Before & After Left: original Tableau visualizations; right: updated Tableau visualizations
04 Reflections
Exploratory Analysis is Essential: Before I even thought about opening Tableau or R, I had to consider what my data needed to look like in order to create the type of visualizations I envisioned. Understanding the data structure and cleaning the data was a foundational step in the process.
Conduct User Testing Whenever Possible: Everyone is going to interpret visualizations in a different way, so incorporating feedback from perspectives other than my own helped me revise and ultimately improve initial versions of my visualizations.
Data-driven vs. Story-driven: Approaching a project like this where this is a lot of existing narrative surrounding the topic was difficult. I had to maintain perspective and let the data tell the story, and not the other way around.
Library Imports & Documentation
INFO 658 Information Visualization
Professor John Lauermann
Pratt Institute School of Information | Spring 2025
