Choosing the right font for your data visual

Typography is a fascinating domain. Fonts evoke emotions: there are very sophisticated fonts, playful fonts, attention-grabbing fonts, and elegant handwritten fonts. Using the wrong type of font can have a lot of impact. In data visualization the implications of typography are mainly focused on readability. Labels and annotations can easily become so small they get hard to read. Above all else, we should choose a font which is readable at small sizes.

What influences readability?

The readability of a font at small sizes is mainly determined by three aspects: the x-height, the counter, and the serifs.

The x-height of a font is the height of the lowercase letters compared to the height of the line itself. Fonts with a lower x-height are more difficult to read than fonts with a higher x-height.

The counter is the enclosed space inside a letter, such as in the letters ‘o’, ‘a’ or ‘e’. The larger the counter size, the easier it becomes to read a font at small sizes.

Finally, the serifs. Sans-serif fonts are generally easier to read at small sizes than serif fonts. The different serifs – the small lines and strokes attached to the end of individual letters – make longer texts easier to read, because our brains can more easily distinguish different letters from each other. That’s why almost any book is set in a serif font. But for small text, the serifs get in the way and sans-serif fonts are the way to go.

Where to find the perfect font?

If you’re struggling to find the perfect font, a good website like Google Fonts can help you out. With over 1300 free font families, there’s always something for every situation. You can enter your own text and immediately see how it will look in all of these different fonts. You can filter by category or you can look specifically for very bold fonts, wide fonts, etc. Once you’ve found the perfect font, all you have to do is download it and install it on your computer.

The best fonts for data visualization

If you still find looking for the perfect font a daunting task, try some of the fonts developed specifically for readability:

Assistant | Lato | Noto Sans | Roboto | Source Sans

If you want to know more about visualizing data in the right way, you can check out the other videos in this series. Or I invite you to read my book, Powerful Charts, that will give you actionable insights and practical guidelines to create data visuals that truly engage and inspire your audience.

Read more:

Books on a bookshelf - infographics resources

Data visualization resources: all the links you’ll ever need!

You want to start creating clear and attractive data visuals, but don't know where to start? No worries, here's a complete overview of tools, resources and inspiration you can use as a starting point for your designs.

Read More

thumbnail for video 01 - why is data visualization so powerful

Why is data visualization so powerful?

The amount of data coming our way is growing exponentially. In 2021 alone, it is estimated that humankind generated 74 zettabytes of data – that’s about 10,000 GB per person. How on earth are we going to keep this manageable?

Read More

Amazing facts about the brain - teaser

Infographic: Amazing facts about the brain

Did you know that our brain makes up 2% of our body weight, but consumers about 20% of our energy? Did you know that we have a second brain, located in our gut?

Our information designer Sofia made this insightful infographic, giving you an overview of eight amazing facts about the brain!

Read More

Birthday heatmap

How common is your birthday?

Not all birthdays are created equal... in fact, for most countries in the north temperate zone, more people are born in summer (May - August) than in winter (October - January). This heatmap allows you to check how popular your birth date is. It shows the number of people in Belgium for each specific birthday.

Read More

Visualizing complexity by Superdot: interior

Visualizing Complexity: Dataviz book review

Visualizing Complexity is a great new data visualization book published by information design Superdot. Here's our verdict.

Read More

This chart is trying to trick you

The original chart in this example is trying to suggest a strong correlation between sugar intake and obesity in the US between 1980 and 2000. It does so by carefully choosing the vertical axis ranges and scaling so both lines nicely fall on top of each other.

Read More

We are really into visual communication!

Every now and then we send out a newsletter with latest work, handpicked inspirational infographics, must-read blog posts, upcoming dates for workshops and presentations, and links to useful tools and tips. Leave your email address here and we’ll add you to our mailing list of awesome people!