Today was the fourth day of the dashboard week. If you’re unfamiliar with dashboard week, it is a week where the data schoolers in training have to build 5 dashboards along with 5 related blogs in 5 days. So that’s 1 dashboard + 1 blog a day. Every day the topic and requirements are provided in the morning and we have till the end of the day to finish our work.
Dashboard Week Day 4 Topic
Today we were given US Consumer Data to work with. I was expecting some surprise requirements but there weren’t any. The brief was pretty straight-forward.
- Create a visualisation using the US Consumer Dataset
- Supplement the dataset to enrich it
- Tableau & Alteryx use is allowed (so, the usual; no surprises)
The dataset is purely focused on the United States consumer habits and so any findings wouldn’t apply on a global level. So any points I make in this post or the visualisation is going to be from the US perspective and not reflective of how it is in Australia or globally.
I took my time looking through the dataset as it wasn’t a simple data cleaning job with this one. We had to develop a good understanding of what’s inside the dataset to ensure that we remove all the aggregated rows in the dataset. So, the dataset included consumer spending figures across different levels of categories and subcategories since 1959. However, there were a bunch of rows that had to be removed and figuring out which ones to keep and which ones to remove was a bit tricky.
The next challenge was to figure out what the adjusted values would be for 2019. It seems straight-forward but it can be tricky when it comes to financial numbers. The key is to validate on every step to ensure no wrong numbers pass through. I decided to go for US historic inflation data for every year which wasn’t too hard to find. The slightly more challenging part was to write a formula to get the multiplier for each year to be used to bring that year’s value to a 2019 normalised amount.
After getting a good understanding of things and sorting out the data cleaning and shaping process, it was time to put it in Tableau and start visualising. The visualisation bit is usually not a part of my job I find challenging. However, I did find this dataset to be difficult in terms of finding interesting ways to visualise it. Seeing it from the right angle is very important to me. So I spent a fair bit of time trying different iterations and experimenting with different perspectives.
In the end, I decided to focus on seeing the changes in Federal Rates and how it affects different segments of consumer spending. Then I thought it would be interesting to see how the spending habits on more granular categories like food & accommodation and healthcare has changed. In particular, I wanted to see if people are spending more on unnecessary things now than before as some people claim. Turns out, people are spending less on eating out and clothing than before and spending lots more on healthcare.
I have included a screenshot of a section of my viz. You can click here or on the image to be taken to the viz.
As always, there is much more I could do with the viz. I’d like to include more supplementary data to be able to see it from many different angles. Hopefully, in future iterations, I can include more interesting insights into my analysis.