Spreadsheet tools such as Microsoft Excel or Numbers might not be the first thing on your mind when considering data visualization tools, but they can be pretty solid choices to build data visuals. Don’t let anyone convince you that using Excel to create data visuals is unprofessional. In my practice as an information designer I have created many charts in both Excel and PowerPoint, in particular for clients who wanted some degree of flexibility in modifying the visuals themselves.

Spreadsheet tools are more powerful than you might think

Spreadsheet applications allow us to create almost any type of chart in the family of bar charts, line charts and pie charts – including stacked bar charts, area charts, and so on. But even more ‘exotic’ alternatives such as treemaps, sunburst diagrams, candlestick charts, radar charts and waterfall charts are available.

The strength of spreadsheet tools lies more in building the charts than in polishing their design. We could use Excel to create the basic shapes that will make up the core of the data visual, and then export them to another, more design-oriented tool such as PowerPoint or Adobe Illustrator to further modify the colours, annotations and layout – or to combine multiple graphs into a single chart. Nevertheless, most of these things are possible in spreadsheet tools as well, although they might require a bit more tinkering and clicking around.

Move away from the defaults

The challenge in these situations is to move away from the default options. It takes some time and patience, and maybe some visual trickery, but it pays off!

This is how a default visual might look like: it has no visual hierarchy, boring default colors, the legend takes up a lot of space, and the numbers are hard to read.

In the reworked visual below we changed the orientation of the bar chart from vertical to horizontal, we chose a better colour scheme, and we used direct labelling to remove the legend and gridlines. The end result contains exactly the same data as the default chart, but presented in a much clearer and more structured way.

Both of these charts were created from scratch using nothing but Microsoft Excel. Creating powerful charts is not about the tools you use, it’s all about applying the right principles!

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.

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.

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.

Colour is one of the most crucial tools we have to turn a normal chart into a powerful chart with a clear message, a chart which tells a story rather than simply presenting the information.

This Venn diagram shows the number of harmful pesticides, presented as dots, banned or phased out in four major economic regions (Brazil, the United States, Europe and China).

Notice how colour is used in three different ways here.

Background colours

Grey is used as a background colour– elements in grey are present and visible, but they will never be at the foreground of the visual, or get in the way of the key message. Used in a clever way, grey can help our viewer to distinguish between what’s most and what’s less important, and bring structure to the layout of the visual.

In fact, most of the time it’s a good idea to create the first version of your data visual purely in tints of grey. This will help you to quickly see if your design will work, and how your data visual will be perceived.

Thematic colours

Blue is used as a thematic colour – because we associate the European Union with the colour blue (because of its flag and logo), it is a logical choice to use this colour to indicate everything related to Europe. Blue is not only used for the dots and the Venn diagram, but also the number 169 in the label, and the words ‘Europe’ and ‘European Union’ in the title and label. This helps to tie all the different parts of the image – diagram, title and annotations – together in a logical, visual way.

Accent colours

Finally, red is used as an accent colour – it almost automatically draws our attention to the central part of the visual: the 10 harmful pesticides which are banned in all four major economic regions simultaneously. Make sure to use your accent colours sparingly. As they are so good at drawing attention, using too much of them will quickly overwhelm your audience – they won’t know where to look first! Highlight only the elements which are crucial to explain your key message.

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.

A crucial step in building a powerful chart is choosing the right type of chart. A lot of charts don’t work because they simply use the wrong type of chart. To avoid this trap, we must ask ourselves a basic question: what’s the ultimate goal of our data visual? What do we want to show with our data?

Categories of graphical representation

As it turns out, there’s only a limited set of goals we might have for our chart. These goals, sometimes called ‘categories of graphical representation’, always boil down to the same seven categories.

Removing noise from a data visual is not only about taking things away such as gridlines, axes or legends. That’s just one part of it, which we could call removing physical noise. Improving the signal-to-noise ratio is often also about adding little things that help our audience better understand the visual. We are helping them by removing mental noise, or mental barriers.

Towards a noise-free data visual

This visual is a concrete example from sports science. It’s a typical chart you could encounter in a scientific article or report. It does, however, contain a lot of noise.

‘Less is more’. It’s a crucial principle in most of our communication, and in data visualization in particular. Because of my background as a physicist, I prefer to talk about the ‘signal-to-noise ratio’. The message – our signal – should be amplified as much as possible, giving it all of the attention. Everything that can distract from our message – the noise – should be removed.

Noise: the enemy of communication

Maximizing the signal-to-noise is important for every form of communication. A company that applies this principle very well is Apple. In their presentation of the first iPhone, back in 2007, the signal-to-noise ratio is very high.

On the slide is just a picture of the iPhone with a single word: GPS. That’s what this part of the presentation is about: here’s our new phone, and it has GPS!

There’s nothing else on the slide – no slide numbers, dates or logos. Not even the Apple logo! Both the slide and the room have a dark background. Steve Jobs is not behind his desk, because it would be a barrier between him and the audience. He’s wearing neutral clothes, because anything too fancy might distract from the message. Every possible source of noise has been eliminated.

Noise in data visualization

Apple still does this today, and they do it in their data visualization as well. This very clean chart was a part of their 2020 presentation of the M1 chip. Again, it has a noise-free dark background. Everything unnecessary is removed. There are no gridlines or axis ticks. There is not even a legend!

Remove the unnecessary

Here’s another practical example of signal-to-noise ratio applied to data visuals. From this chart we can remove the coloured background, the gridlines, the legend, and even the horizontal and vertical axes. Removing these elements doesn’t harm the data or the message of the visual. In fact, it makes the data even more accessible. That means they were just noise! We end up with a much clearer visual, with a much higher signal-to-noise ratio.