What you leave out communicates just as much as what you choose to include. In the expansive worlds of Google and Wikipedia, it is easy to forget this simple truth. For example, consider what is captured in the frame of a camera. The photographer can move their lens over ever so slightly when taking a picture of their home and leave out the piles of laundry and clutter. You might see a spotless side of the room, but don’t assume that you are seeing the whole picture. 

This truth applies just as much to data visualisation. But we have gotten into a very bad habit. We recognise the trends towards big data and dashboards and assume that more has to be better. More data . . . more charts . . . more insight? Actually, that isn’t usually the case. More data can sometimes mean more confusion. 

Sometimes what isn’t there can help drive meaningful insights and lift the veil on what your business really needs to understand. But we must proceed carefully because the art of minimalist data visualisation is all in deciding what to include and what to leave out. 

In tackling that issue, let’s start with a map about global life expectancy. The map below shows only those countries that are approaching life expectancy of 80 years or above. By choosing to exclude every part of the world below 78.9 years, you focus only on the highest performers and get a sense for where these high performing areas are spatially. 

But while the data scientist left out many things, they chose to show life expectancy at a regional level within those countries that made the cut nationally. That way you understand that not all of the countries that have these highest performers are uniform. Take the US and notice that much of the South is missing from the map. Also notice parts of Europe that are in the secondary category instead of the first. 

But the big thing that this minimalist data visualisation screams at you is the disappearance of Africa from the picture. You also realise that between Africa, India and China, more than half the world’s population (4 billion) live in an area below this high performing life expectancy standard. 

 Map with sub-national divisions of life expectancy above 80  (Source: Reddit)

What a map like this doesn’t show is that just below these artificial cut-offs for high performing life expectancy countries, there are a huge number of countries that are not far behind. China is in the high 70’s as well as Russia, Brazil and Iran. While that isn’t the point of the data visualisation, it would be easy to assume that other countries must be much lower and that this level of life expectancy is not common. 

You also don’t see the outliers on the bottom end of the spectrum. In Africa there are a variety of countries with life expectancies in the 60’s and 70’s but a few that are extremely low; such as Lesotho, Central African Republic, Chad, Nigeria, Sierra Leone (under 55 years for average life expectancy). 

Now in contrast, the graphic below compares life expectancy at three very different times throughout history and gives detailed data at the country level. While it is much more complex, it does reveal things that the other visualisation leaves in the dark. However, it is overwhelming and hard to take it all in. So, which one communicates better? I’ll let you decide. 

Life expectancy at different times throughout history (Source: Reddit)

Let’s now turn our attention to two visualisations that highlight minimalist data visualisation and the insights that come from leaving many things out. These next two maps come from the Big Data Zone

Key Airport Observations:

  • While China has more airports than India, if you look at their disbursement, you see that India’s population is much more evenly dispersed than China’s. 
  • It becomes clear why it is so expensive to fly to South America or Southern Africa, but it is impressive how accessible Australia is given the few airports it has. 
  • The strategic nature of shipping lanes in Central America, the Middle East and Singapore are highlighted in how many airports utilize these same routes. 

If they could add one thing to this map that would increase the insight, I would say it would be the identification of the top 50 busiest airports in the world. What would you add? 

Key River Observations:

  • Rivers don’t always equal people. Notice the number of rivers in Canada and Russia, but because of the temperature, the population levels are low. Same is true of Brazil and Central Africa but for different reasons. 
  • This map highlights why China cares so much about its western frontiers; a source of much of its water. 
  • The difference between New Zealand and Australia is stark. This has huge implications for their sustainability in the future. 
  • The power of the Nile river in North Eastern Africa becomes clearer when you look at the blank slate to the West. 

If they could add one thing to this map that would increase the insight, I would say it would be making rivers that cross national borders a different color from rivers that are wholly within one country. This would highlight where potential future partnerships and conflicts are likely to arise. 

As you innovate in your business or cause, how will you choose to visualise the data that is important to your impact? Like that photographer choosing what will be inside or outside the frame, will you be able to make the hard calls on what you include? 

Here are a few tips on how to be a data minimalist:

  1. Be fastidious about picking the cutoffs for the data you choose to exclude. Make sure that the ranges you use don’t mislead. 
  2. Look at the simple data presentation and ask yourself what new insights you learn from this unique view of the data. Ask if those insights drive business decisions or get in the way of clarity?
  3. Consider overlaying two sets of minimalist data to see what simplified correlations you might find. 
  4. Run your visualisations by customers, partners, vendors and staff to hear from them what they see. 
  5. Test what you measure in your dashboards against what can be visualised. You may find that there are things you are measuring that are not important or things you want to visualise that you are not measuring.