The DS6 had to do this week’s Makeover Monday along with a room full of attendants in the Exasol’s Xperience conference in Berlin. We had one hour to complete the task live. The data set we were given were the tourist data in Berlin and Brandenburg area.

Looking at the data, one of the first problems we encountered was that all the names were in German. Due to the constrained in time, this put even more pressure on us. Another problem was the grouping of countries i.e. ‘Arabic Countries’ tourists were all aggregated. Translating the countries and other variables also made it a bit difficult to find a story within the data.

Despite the small issue, Tableau recognized most of the geographical names in the data set.

Initially, I looked the cumulative number of tourist from around the world.

 

 

The map shows the origins of tourists 

I could not find a lot of story within this analysis, partially because of the grouping of the countries which were not recognized Tableau. Instead, I started looking at the districts within Berlin and Brandenburg. As I explored the data, I wanted to find out when/where the tourist usually travel to within the Berlin and Brandenburg area.

To answer this question I used two charts. One was the map of Berlin and Brandenburg to visualize where exactly tourist visit.

Next, I used a bar chart to determine which months were particularly popular amongst the tourists.

 

The visualization is relatively simple and clean, each of the regions is clickable and filters the rest of the data.  The BAN (Total Guest and Total Nights Stayed) changes as the regions are clicked. The dual axis bar chart shows the total number of tourist and the total number of nights, which is filtered when a user clicks on the specific region. I decided to go with the red and white theme, as a homage to the colour of the flags of Berlin as well as Brandenburg.

So, the question is what did I learn from finishing the Monday makeover in an hour. Firstly, the limited meant I only focused on the relevant audience, questions, story and subsequently the data. I knew I did not have time to have a very complicated visualization. Moreover, I am genuinely surprised I could make a viz that quickly considering how long it used to take me. I have also been more careful to make a question out of my data and try my best to answer it.

Some people use the lack of time as their excuse not to make visualization and play around Tableau ( I certainly did), especially since I usually spend hours finding the right story and then perfecting one perfect viz. This really limited what I published online.

Now I believe its more important to just produce a good enough viz and post it.