Wow! What a response rate we’ve had to these first two weeks of Makeover Monday in 2018!

We thought last year was crazy, but this year is taking it to a whole new level. I’m struggling to keep up with all the vizzes coming in and I’m delighted to see such diversity in analysis, design and tools being used. The community is growing rapidly and it is a joy to be part of it.


Getting started with Makeover Monday

If you’re new to Makeover Monday and want to get involved, but don’t quite know where to start, check out our Makeover Monday 101 webinar (recorded) which guides you through the process. You can also just get started and I’d suggest you check out what others have been doing, how they’re submitting their vizzes each week and how the process works. We’re working on creating sharable resources that answer all your questions as we simply cannot address everyone individually at this point. There are just so many people – and that’s a great thing. Until we have those resources we appreciate your patience and welcome the help of our MM power users who have been participating for a while in getting the new folks started.


This week’s challenge

For this week’s makeover we looked at survey data analysing responses to the question of what is most important for people in a romantic partner.

While there were only 6 possible responses and the data was ‘only’ categorised by gender and nationality, this dataset was not easy. Why? Because explaining those results is the tricky part.

People taking the survey ranked the 6 response options in order of preference, resulting in percentages of respondents who ranked each item first, second, third, fourth, fifth or sixth priority.

To put your findings into a simple, plain English (or other language) sentence and to equally reflect such simplicity in your visualisation so that your audience can understand the results at a glance is actually very difficult.


Focus helps you find a story

In this week’s submissions, I noticed that those who either focused on a subset of the data or took a high-level view, seemed to have an easier time breaking down the complexity. Once we start taking every nationality and every possible answer into our visualisation, showing every single data point, it gets heavy and often hard to understand. We need to keep in mind that even though we have spent an hour or two with this data, most people in our audience will be completely new to it and won’t have context or an appreciation of the fields and values available.

Here are a few examples which showcase great simplicity for this potentially complex topic:

Author: Paweł Wróblewski
Tool: Excel

Author: Gina Reynolds
Tool: Rstats

On the point of simplicity, it is good to remember that just because all the data is there, it doesn’t mean you need to include ALL the data in your analysis or visualisation. It just needs to be the right data for your argument.

For example, some people decided to only look at one single nationality. Others focused on the question of looks versus personality and ignored the remaining questions. That’s perfectly fine as long as you frame your data story accordingly and support your claims with data (i.e. don’t make claims about data you didn’t include and visualise because this would confuse your audience and make you look less believable).

Okay, so now onto our lessons. Being the good German I am, I will – unlike Andy – stick to our new rule of 2 lessons.




I’m making this sound more offical by including the word ‘stakeholders’.

On the surface, this lesson helps Andy and me a lot, but deep down it also helps you a lot, so what do I mean?


Research the topic

It is always a good idea to read the article that goes with the viz to understand the background of the topic. In the business world before you start any kind of dashboard project you (hopefully) engage with stakeholders, meet with the people who will be using it, the people who own and/or govern the data and others to get the full picture before you start analysing and visualising the data.

I know that dataviz tools make it fun to get straight in there and I love the enthusiasm this community has for working with data.
We should all still remember though to go through some key steps and while you cannot have Makeover Monday stakeholder meetings, the information we provide for context is your best starting point for solid data analysis and a strong data story.


Don’t rush – read the instructions

The other ‘reading’ I refer to is the reading of the instructions we give. We don’t tell you how to build your viz, what to focus on, etc. But we do tell you some other pieces of key information because this helps us run the project smoothly and with the greatest benefit for everyone involved.

On we post instructions at the top of discussion topics to help you format your own submissions so that an image is included as well as a link to the interactive version. PLEASE READ THESE INSTRUCTIONS.

This week there were countless comments from people telling us they don’t know what to do. If they had taken 30sec to read the instructions, they would have known. It’s not so much that we go slightly crazy when we answer the same ‘admin’ questions multiple times a day, it’s simply about the time we ‘waste’ on those questions that we could otherwise spend giving constructive feedback or helping someone with a genuine and more important question.

The less time we have to spend on admin, the better. We prefer to have more time for Viz Review, for writing this blog and engaging with you online. I know a lot of people are new to this project and we have a lot of patience. We just ask you to come our way and put in a little extra effort to make this easy for everyone. In the meantime we will try to make all the resources available that we possibly can.

Between the Makeover Monday 101 webinar, the slides from which we have posting on our start page, the regular Viz Review webinars, the recap blogs, the instructions on and the various community blogs, you will already find plenty of ways to learn how this project works. There are many people who have been doing Makeover Monday for over a year now and can answer most of your questions, so feel free to reach out to the community as well, just make sure you’ve tried to find the answer first.




As I mentioned above, the findings from this week’s dataset were in no way easy to communicate. The concept of men and women from 20 different nations ranking six different questions on a scale from 1-6 and the results showing the percentage of those men and women who rated each question with a certain importance, is something that takes a while to get your head around. Well, at least for me.

While I suggested earlier that it is important to find focus for your data story to make it easier to understand, I also want to encourage you to express very clearly what your analysis resulted in.


Choose clear titles and descriptions

I understand that not everyone’s first language is English, but even with native speakers I noticed that titles and descriptions didn’t always make it clear what the viz was showing me, what the analysis was about and what some assumptions or subsequent questions were.

What helps is to read your title and description out loud and ask yourself ‘what does this mean?’

Then see if you can quickly and easily answer that question. If not, try to find another way to describe your viz.

Look at your chart(s), tooltips and annotations and ask yourself whether they answer the question you ask in your title or support the claim you make.

For example, if you say that Swedish men care more about looks in a romantic partner than their intelligence, then your viz better make a very strong point to back up your claim.

If your title focus on one of two of the six characteristics in the survey, make sure you either make those two characteristics stand out or remove the others altogether and only show the ones you’re talking about.

Rosario’s viz this week is a great example for a clear title and description. Her words are carefully chosen to communicate the key message which is then reflected in the viz which supports her statements nicely.


Looking at your viz this week, did you do a bit of research and analysis before diving into the visualisation part? And do you think your viz is clear for your audience (of people who most likely haven’t heard about the survey and its results before)? If not, then you could make these two points your learning exercise for next week.

I look forward to seeing clearer viz titles next week :-).



Author: Leonid
Tool: Excel

Author: Steve Wood
Link: Tableau Public

Author: Robert Crocker
Link: D3