#FastFriday – 6 Minute Vizzes – 4th Episode

Welcome back to #FastFriday!

I know, I wrote my latest blog post about this serie on the 12th of June. I have been quite busy in the last weeks, but I missed it a lot!

So I am back with this experiment and I am quite proud this time. Probably the dataset was easier to analyse this time (and also I am getting more used to it, maybe). Anyway I am happy about this viz so I made just a few adjustments on the “remake”.

As usual, here the video I uploaded on youtube and here the viz.


[Something “strange” in the data this time, I will figure out why there are some negative values…]

So, following the rules about this challenge if you do not know them:

  • Andy Kriebel or Chris Love give us (Ben Moss, Ravi Mistry and me) a dataset usually with a generic name (for example,”data.tde”)
  • We have 6 minutes to understand the data and to do a data visualisation – without knowing or having seen the data before
  • We record our screens while we do it
  • We publish on tableau public the 6 mins viz and also the “adjusted” viz
  • We want to get down to 4 mins

Tableau is the perfect instrument for this, because it gives quick insights about data.

How did it go this time?


This time, the dataset had only 3 columns, it was easier to explore and also I decided quite quickly what I wanted to do.


I build 3 different charts, one running total splitted by item, a bar chart that summarises all and a “bump chart” to represent the variation of the ranking by year. So, this time table calcs are my gambling despite the small amount of time. As you can see, it is pretty easy to calculate ranking and running total in Tableau, just a few clicks…

I also decide to limit the time period filtering away the years before 1990 (otherwise the viz looked too busy).


It was quite hard this time to give a title to the dashboard because the columns of the dataset this time were not so clear (name was for year and value for the measure), so I am not sure what value is (also because I discovered there were negative values).

It was pretty interesting that this time I was much quicker and happier about the result, probably the smallest amount of column helped as well.

“See” you next #FastFriday!


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