If it weren’t for what you’re about to read next, Leo here would probably have not toasted to you on Christmas Eve in the beginning, and in the end of this
blog post, movie script. Long story short, I spent quite some time polishing up the first act during Christmas Eve, and when I was about to publish my draft, all those carefully crafted ideas disappeared into thin ice… Long gone are the days when we could blame icebergs for such unfortunate events. WordPress can’t be blamed either, because everything disastrous, and unexpected that happens nowadays in the world is mostly linked to climate change, and Leo knows that too! So cheers to that realization, and let’s rewind to my second act of the Data.World #MakeoverMonday Christmas Data Visualization script. Enjoy it folks!
Disclaimer: Leo was never added as a character in the first act; he made an appearance only after I found it dreadfully necessary to have someone cheer me up while I rewrote the whole blog post again… I mean, it’s Leo guys…
Everyone has a crush on him! His foundation just celebrated 20 years of standing up for our planet!!
My GE friend and colleague Gary Adashek recently read a book called #Makeover Monday by authors Andy Kriebel (Data School, Makeover Monday, Tableau Tip Tuesday, Workout Wednesday, datavizdoneright), and Eva Murray (trimydata). He got to know my love for data visualization after my restless attempts to visualize Oracle DRM master data using Oracle DV. I must say that with all the big amount of data readily available to be analyzed in a large enterprise, those who dare visualize master data, must really be into the Data Viz. part of the exercise. wink wink there 😉
I used Tableau during a data visualization grad school class to analyze, visualize, and derive insights out of NYC Mount Sinai hospital patient data, however since then I rarely had to do hands on DV work. That is why I was very excited to join Gary in his #MakeoverMonday journey. See below his first go at the Week 52 data set.
Roughly how much money do you think you personally spent on Christmas gifts this year? According to the most recent results of a survey about the estimated Christmas spending of U.S. consumers from 1999 to 2018, U.S. consumers are expected to spend approximately 794 U.S. dollars on average on Christmas gifts.
On Week 52 of #MakeoverMonday we aim to critically review the original visualization representing the data set of the above survey, and then propose our own unique approach to visualizing, and deriving meaning out of the same data set.
1. What works, and what doesn’t work with this chart?
The author of the original visualization has used a Line Chart to display the fluctuation of the average Christmas consumer spend in the US. It’s a simple, and effective way for users to understand the changes across 20 years.
To me, it seems a bit redundant to display both the Title, and Label Values in the Y-axis. We’re only supposed to be looking at USD amounts across years, and on top of that the USD amounts are already displayed over each data point. Something else that the author should have considered pointing out is whether or not the data set has been adjusted for inflation.
2. How can you make it better?
I used Oracle DV to visualize the original data set in three approaches:
- Consider the Radar Bar as a circular visual depiction of the expression “What goes around, comes around“. You can see that in the span of 20 years, the average Christmas consumer spend trends repeat themselves every few years. Having said that, let’s hope that we won’t see a 2008 and 2009 recurrence until a very far away date in the future…
- In the Tree Map, the data is organized in pairs ranging from the set of years where the average Christmas consumer spend was the highest to the years where it was the lowest. The visualization starts with the 2007 US economic peak, and ends with the 2008 US financial crisis. Let’s not complain about where we stand today in 2018: there’s always better, but there’s also always much worse.
- The Box Plot is my favorite visualization out of all three. It is similar to the original Line Chart without the Y-axis title, the data point values, and without the connecting lines between data points. I like this visualization better because it is less cluttered, and the viewer can easily make sense of the fluctuations through the years by referencing the adjusted scale of the min data labels displayed in the Y-axis, as well as the color variations. 2008: what a faint year!
3. Oracle DV Tips & Tricks
In the below images you will see a few visualization settings I had to modify in Oracle DV.
4. New Year’s #DataViz Resolution
As I stated in my previous blog post: “Oracle DV to the Rescue! Adding Emotion to the Data: When One Visualization Feels Like ‘1000’ Stories“, along with other #orclBI enthusiasts, I hope to use Oracle DV in 2019 to:
- Bring to life data sets published in the Data.World #MakeoverMonday community with the goal of improving how we visualize, and analyze data one chart at a time.
- Harness the power of data visualization for social change, and empower nonprofits through data stories via the #VizForSocialGood community.
- Visualize the natural disaster occurrences of the past year, and showcase how that trend directly correlates with the effects of Climate Change. Whether or not you’re a Titanic, Gatsby, Leo, Dem or Rep fan… Climate Change is for real! Next year let’s take climate action together. I am starting with @fabe:forallabeautiful.earth because reducing consumption (Christmas consumer spend included) on an individual level is the first, and the most important step towards healing our planet, and creating a better world! Can Techies save the planet? We @fabe do think so 😉