Dataset
For this dashboard challenge, we were given injury data in U.S from the NEISS (National Electronic Injury Surveillance System) dataset.
I worked with six Excel files covering 2019 to 2024, each containing thousands of records on emergency department visits. Most fields were coded for example, Sex = 1 meant Male, Diagnosis = 59 indicated Laceration, and Product = 3452 referred to Cheerleading. Each record also included a weight column, which I used to calculate national estimates. Since the dataset was huge, I decided to cover only the primary injuries from school.
The Data Structuring in Alteryx
I used Alteryx to clean and prepare the data:
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Input tools for each of the six annual files
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A Union tool to combine them into a single dataset
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A Filter to keep only records where Location = 8 (school-related injuries)
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A Formula tool to decode fields like Sex, Diagnosis, and Disposition.
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Joins to a product fact table and a custom product categorisation file I built to handle uncategorised codes
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An Output tool to export the final dataset to. hyper for use in Tableau
Exploration And Story – What’s Hurting Kids in Schools?
Once the data was clean, it was time to visualize it. My main questions were:
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Are injuries going up or down over time?
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Who’s getting injured the most (by age, gender)?
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What activities or products are causing these injuries?
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Can we identify trends or spikes worth further investigation?
This became the backbone of my Tableau dashboard.
Final Thoughts – From Chaos to Clarity
This project reminded me that data transformation is often the hardest part. The real insights only came after I’d spent hours understanding fields and aligning everything just right in Alteryx.
But once that was done, Tableau allowed me to tell a story through my dashboard.
