I recently completed my first project using Tableau, and it turned out to be one of the most challenging data visualization experiences I have had so far. My assignment was to build a dashboard using more than a decade’s worth of FAA bird strike records, a dataset large enough to be interesting but also inconsistent from reporting variances.
I started by dragging a few fields onto the screen and making some simple charts. What I learned is that Tableau is powerful, but only after understanding how Tableau expects data to flow through rows, columns, and marks. My early visualizations were extremely basic and, to be honest, pretty meaningless. Although I think there are times when one variable is on the x-axis and one on the y-axis.
Without help from YouTube tutorials and ChatGPT, my dashboard would have been little more than a collection of bar charts. I could link to the dataset, drag and drop fields, and produce tables or simple visuals. The numerous YouTube videos showed me that meaningful dashboards require more than simply putting variables side by side.
The turning point came when I learned how to use Tableau’s internal data handlers. Simple tools like the COUNT function changed how I could work with tens of thousands of records. For example, each record has an ID. When there are 10k+records the ID number doesn’t mean much. But, if you count the records in relation to another variable now the RecordID can be used. Almost every sheet I built I used COUNT and one of the measurable fields. Additionally, dual-axis charts allow me to compare measures, such as bird strikes per year, alongside total damage costs, all on a single view. These two tips along helped the dashboard become a dynamic tool rather than a spreadsheet with color.
Working with a large dataset also helped. The FAA bird strike data spans more than ten years and includes enough variables to explore patterns across time, geography, and aircraft types. Even with missing or inconsistent fields, there was still enough high-quality information to build seven different visualizations that work together to tell a story. Here is an iFrame of the finished dashboard.
I am looking forward to how Tableau could support my work in agriculture. I plan to start collecting site-specific growing data from weather stations and field sensors. Tableau appears to be a natural next step for transforming long-term environmental records into visual insights. My next step is to connect a dynamic database instead of a static spreadsheet. At some point, AI interpretation will be included. I hope to help develop the use cases of this technology.