You all probably know by now that the #MakeoverMonday community is pretty switched on, creative, passionate and simply a bunch of incredible people that we truly enjoy working with.

Not only do you all surprise us week in, week out, with great visualizations, creative representations of data and new designs. You also help us better understand what Makeover Monday means to you, how you learn, how we can help you and you make it possible for us to see what is needed for everyone to improve their skills and grow their capabilities.

Just now (thanks for ad hoc Twitter checking in between travelling a lot lately) I discovered the following thread. I want to thank Zach for starting the conversation as well as everyone else for contributing their answers. It’s so encouraging to see and read how Makeover Monday has helped you…

click on the tweet to see the conversation…

This week’s Viz Review was a solo act and I really appreciate people’s effort to iterate quickly and show us the result of their reviews. I want to call out a couple of examples and highlight the before and after to show how a few simple changes can make a big difference.

I suggested to Anna to have the default sort order be the total costs in descending order, rather than having the sort order based on the selected item

The updated sort order makes for a more intuitive ‘first view’ dashboard when someone sees this for the first time. Changing the sort order can now be part of the viewer’s exploration.

Suggested changes for Katie:

  • Change the order of cities in the subtitle to align with the viz (orange first, blue second)
  • Simplify the description

Result: A more logical flow from top to bottom. Well done, Katie!

For Cindy I proposed to update the background color of her charts and I questioned the use of the map. I suggested to make the BANs really large and to add white borders in the bar chart to make it easier to distinguish the start and end of each section.

The revised version from Cindy is simpler, has a more coherent design and highlights the big numbers easily.

What are some of the lessons learned from this week? One stood out very clearly for me and relates to our analyst skillset.



The dataset this week contained data for the cost of a party night out versus a date night out. Many people opted to compare the two, which is a very valid approach. Not everyone, however, made sure to compare like with like.

The party night out costs comprised of the cost of two long drinks, a Big Mac, entry to a club and a taxi trip. These costs were averages from each city. The date night out contained a dinner and drinks for two and a cinema entry. Comparing the two costs straight up is not right, because the party night just focuses on one person (unless you’re cheap enough to insist on sharing a Big Mac with your friends).

So comparing the costs of a party night (for one person) with a date night (for two people) isn’t right. What we need to do in this case is:

  • Double the long drinks costs (or include a note that it’s assumed each person only gets one drink)
  • Add a Big Mac
  • Double the club entry
  • in my opinion, sharing the taxi cost across two people is acceptable
  • make a judgement call on the cinema costs in the date night category. While the UBS page states that the date night costs are for two people, I am not convinced that you can purchase cinema tickets for two for US$16. In my opinion, these should be doubled too, but given the ambiguity on the UBS page, I’d accept both options.


In short: to compare like for like, make sure the costs for both categories are for either one person or for two people, so they are truly comparable.




Colors are important for data visualization and can be used to evoke emotions, create associations, to set the tone and create a certain mood in a viz. We have written an extensive chapter about color in our MakeoverMonday book and had the honor to have it reviewed by none other than Tableau’s own Research Team lead Maureen Stone, a renowned computer scientist and digital color expert (ever wondered why 3 color palettes in Tableau Desktop contain ‘Stone’ in the name?…)

In our typical business use cases for data visualization, color is typically used to highlight trends, data points, categories, dimensions and to bring consistency across different views to help our audience connect with the data and understand insights more easily.

Color can also be used in very creative and – unfortunately – confusing ways. Now, not every data visualization built for Makeover Monday needs to have the purpose of being the most effective display of data for the most efficient understanding of a dataset. Creativity and experimentation is certainly encouraged and we want that to always be the case.

What I did notice this week though, were a few simple vizzes (e.g. bar charts or slope charts) that became unneccessarily complicated through the excessive use of color.

Use color to draw attention and to highlight specific data points or insights. If everything is colorful, nothing stands out.


A great example comes from Chris Kalwa who uses two shades of green. Dark green for the selected city in his viz and light green for the bars of every other city…

Let’s now get to this week’s favorites. It was really hard to decide but here they are…



Author: Tucker Gordon
Link: Github