Eva’s first 7 days of Snowflake+Tableau visualizations
Eva’s challenge: Produce a new daily visualization with Snowflake and Tableau for 30 days. Let’s check on her progress during her first week of this ongoing challenge.
Eva Murray is a Tableau Zen master and a leader for #MakeOverMonday — and she just joined Snowflake! She loves data storytelling, and when she found thousands of datasets in the Snowflake Data Marketplace — she decided to take this challenge. We taped a call discussing her first 7 days of visualizations — and you can find it here:
Let’s check some of her visualizations and my thoughts:
The visualizations
- Employment in Sports
- Sports Footwear Exports
- Stanley Cup Winners
- Employment in Sports 2019 (Gender Split)
- Twenty20 Cricket Rankings in 2020
- Starbucks visits during the start of the pandemic
- Tourism Brand Performance 2019
My Notes
First I’m in awe of her courage to attempt this challenge. As you can see above, every day she picks up a new domain — so she needs to quickly understand it enough to be work within it. Then she digs into the data to find an interesting story and a suitable visualization. And then she performs the most dangerous step: Posting the visualizations and findings on the Internet. This means she opens up to criticism and complaints, from anyone on the Internet and with the desire to reply. Fortunately her experience with the #MakeOverMonday project has prepared her exactly for this — leveraging the power of community to learn and improve.
My least favorite visualization: “Employment in Sports 2019 (Gender Split)”
As I looked at this visualization I started wondering — “why are these slopes and not bars?”, “what is the pattern?”, “is there an interesting story here?”.
As you look at this data you’ll find that the differences between countries and genders are pretty small — all around 0.3% and 2.3%. To me this looks almost like a random distribution, where any gender could have the lead by arbitrary reasons.
But then that becomes a highlight of Eva’s chosen visualization: It makes it easy for me to figure that out. Thanks to this visualization choice and annotations I could quickly figure out that all numbers are constrained to a small range — and the slope shows that when there’s a difference in many cases it’s almost non noticeable. And then, where there’s a significant difference, the slope quickly reveals it.
All these thoughts and ideas that first made this one my “least favorite” ended up making it one of the most interesting choices, and full of lessons for me.
My favorite visualization: “Sports Footwear Exports”
This one I found fascinating: By playing with the data, Eva was able to uncover a major unnoticed trend — in less than 10 years someone in Belgium was able to create an industry that makes almost 3 billion euros a year by importing and exporting shoes.
How did this happen and what’s the story behind? This chart has made me really curious to learn more. I foresee many business schools teaching this story for years, once they reach this same discovery.
How-to
Tableau and Snowflake
Connecting Tableau and Snowflake is really easy — and also navigating all the metadata to find interesting datasets. Check the video where we show how Eva managed to discover these datasets within Tableau.
Datasets
Most of the data for these visualizations came out of the Knoema listings in the Snowflake Data Marketplace. To learn more about Knoema — check my previous posts, and my conversation with Knoema’s CEO:
Want more?
Follow Eva on Twitter and LinkedIn.
I’m Felipe Hoffa, Data Cloud Advocate for Snowflake. Thanks for joining me on this adventure. You can follow me on Twitter, LinkedIn, and check reddit.com/r/snowflake for the most interesting Snowflake news.
