Are we done with the indulgence related datasets yet? First chocolate, then wine…

For week 14, we challenged the Makeover Monday community to create data stories from a data set about global wine production which contained the production volumes for a number of leading wine producing countries for a number of years.

I noticed a huge amount of submissions this week and my highlight were the many new participants, submitting their very first Makeover Monday viz. Andy and I both get excited by how much this project continues to resonate with the wider dataviz community and how everyone embraces the weekly challenges, working to create better data stories and visualizations from any given dataset we serve up.

This week’s lessons come from some patterns I noticed across a number of submissions. For those new to Makeover Monday, please know that we provide these lessons not as criticism, but to give you a chance to learn about specific aspects of design and analysis skills. If one person in the community finds a particular dataset challenging and takes a wrong approach to aggregating the data, it is likely that someone else will make the same mistake. Through these lessons we want to cover the topics by giving everyone the same advice and those who didn’t know will be able to pick it up and take it onboard for their future work.




In this week’s dataset there was a world total figure as well as individual country totals. The country totals didn’t add up to the world total, however, because the ‘rest of the world’ beside the countries listed, wasn’t a record in the data.

The first steps of your analysis should always include familiarizing yourself with the data, the fields available, the types of values, etc. This must include checking whether data is incomplete as far as you can tell. For example, there might be five years of data at the month, level but the most recent year is incomplete. Therefore you cannot compare these years at the highest (year) level (e.g., comparing sales for 2017 versus 2018, which has only three complete months right now).

This week, some people created visualizations which showed totals of 100% (e.g., in bar or area charts) by adding up the countries listed. Calling this ‘world wine production’ is incorrect because other countries which weren’t listed produce wine as well and the total of the listed countries doesn’t add up to the world total.


Either adjust the way you visualize and communicate your results by explicitly focusing on the leading wine producing countries or simply add one row of data into the dataset for every year containing the difference between the Sum(World Total production volume) – Sum(listed countries’ production volume). Call it ‘rest of the world’ or ‘other countries’ or something like that and voilà, you can now add everything together and have the total world production.




Whenever there is geographical data, such as country names, someone will invariably create a map. It’s one of the few things that are certain in life. Maps are a great tool to provide context and a neat way to add interactivity to your visualization. Maps are, however, not always useful and when they’re not fulfilling a specific purpose, they can end up being confusing or even a waste of valuable screen real estate that can instead be filled with insights, explanations or possibly even be left empty.

We have previously written about the topic of maps, for example during week 20week 23, week 42week 45, week 46 and have discussed it in various Viz Reviews, so have a look at those blogs for commentary on the question of ‘do you need a map?’ What I will suggest here, though, is that when using a map, make sure it is actually useful.

In this dataset the main wine producing countries were in Europe. On a world map Europe is pretty small, countries like Switzerland, Italy and Georgia become pretty much invisible on a large scale. A map may therefore not be very helpful for your audience and if they are supposed to use the map to navigate around the viz, they won’t have a lot of fun because they will be unable to click easily on the smallest countries.

Does your map add value to your viz? What is it telling your audience? If you shade countries in a filled map based on their production volumes, again this will be difficult to see for small countries. Don’t be afraid to represent country data using bar charts, line charts, slope charts or, if possible, hex maps. You don’t HAVE TO use a map.




Author: Nick Brown
Link: Power BI

Author: Paweł Wróblewski
Tool: Excel