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.

A powerful chart has a clear message. It should be short and meaningful, and obvious in the blink of an eye. If there’s only one thing our audience remembers at the end of the day, this should be it.

Answering the ‘so what’ question

A visual without a clear key message might show the data, but it doesn’t show what’s interesting, surprising or noteworthy about the data. It leaves our audience guessing, they have to do all of the thinking work. Ideally, we want to create a visual that helps them to quickly see what’s important. A visual that not only answers the ‘what’ question, but more importantly also the ‘so what’ question.

Here’s one of my favourite examples to illustrate the importance of visual storytelling. This chart shows the evolution of meat consumption in the US since the 1960s. By itself, this chart is pretty clear. It shows us the data, in a way that is easy to understand. It answers the ‘what’ question. The design is satisfactory, with pleasing colours and good readability. But can it be improved?

Here is an attempt at a rework. Even though we’re looking at exactly the same data – we wouldn’t want to lie to our audience! – the message in this new visual is loud and clear. It’s right there in the title: Americans are eating more chicken than ever before!

The power of data visualization at work

Two simple changes turned the original ‘what’ visual into this super-clear ‘so what’ visual.

• First of all, clever colour choices: the line that interests us – the one for chicken consumption – gets a bright orange colour, the other two become grey. They’re still there, but pushed a bit to the background.
• Secondly, clever text: the original title was very factual – ‘US per capita consumption of poultry and livestock’. All the thinking work is left for the reader. But what if we simply tell them the interesting part? The new title ‘Americans are eating more chicken than ever before’ is still 100% true, but now it tells us why this visual is actually quite surprising.

These two small but impactful changes turned the original visual, which simply shows the data, into a great visual that actually tells a story. For me, that’s the real power of data visualization at work.

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.

Once we’ve decided to create a data visual or infographic, there are a lot of questions we should ask ourselves to determine the most suitable format. These considerations could include size, readability, possible interactive functionality, and the level of detail we need.

Consider your audience

Our audience is crucial in this: in the end, it’s all about them. Even when we have the most important message in the world, if our audience doesn’t understand it or they’re not interested, it’s all for nothing.

So, take a moment to consider what your audience is trying to achieve. Which decision are they trying to make? What data or information do they need in order to make this decision? And how much time do they have to do so?

The landscape of powerful charts

Depending on our answers to these questions, the charts we create can vary wildly. If time is of the essence, we should build charts which can be interpreted in the blink of an eye. These could be static charts for reports, animated online charts, or interactive charts in mobile applications.

On the other end of the spectrum we have a bit more time for visual storytelling. This includes infographics for print or social media, or maybe breathtaking interactive visual essays. We can take our audience by the hand and guide them, step by step, through a visual story.

In between these two extremes we can create reports or dashboards that can guide our audience through a collection of clearly presented key insights.

Don’t start your computer (yet)

Once we have figured out our place in this landscape of powerful charts, the production of our visual can start. However, I urge you not to open your computer at this point of the workflow. It’s a common mistake: we want to start creating as quickly as possible, so we open our favourite tool right away. But then it hits us – the blank screen is staring us in the face, and we start doubting. Where should we put the first line? What was it again we were trying to achieve?

We’re not there yet. It’s time to first define a clear 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.

Data visualization is very powerful, but it can also be hard. That’s because a great data visual combines three different aspects simultaneously.

The three properties of a great data visual

• A great data visual is clear: it communicates a strong message and is easy to understand without too much additional explanation.
• A great data visual is correct: it presents the data in an accurate and appropriate way, and is unambiguous.
• A great data visual is beautiful: it is inviting to look at, and uses colour, typography and other design elements in the right way to support its message.

That also implies that we, as data visualizers, need to consider three different aspects when creating a data visual:

• the communication aspect, in order to make our visual clear,
• the analytical aspect, in order to make it correct, and
• the design aspect, in order to make it beautiful.

If one of these aspects is missing, we end up with a suboptimal chart. A chart can be beautiful and correct but confusing to navigate, causing the message to be lost. Or it can be very beautiful and clear, but fall apart because the underlying data or the representation of it is flawed. A lot of charts we encounter are clear and correct, but simply boring or uninviting to look at, because they were not designed to look good.

Three different skills

So, in order to create a powerful chart we must apply our communication, our analytical ánd our design skills. Most people feel comfortable with one or two of these skill sets, but not with all of them. Many people in analytical jobs, such as researchers, engineers or consultants, struggle with the design aspects of a visual. People with a role in communication, such as journalists or marketeers, often feel uncomfortable to dive into data analytics and the theoretical principles behind charts. And professional designers can make their visuals look beautiful, but don’t always succeed in crafting a crystal-clear message.

If you recognize yourself in one of these worries, fear not! I am convinced that anyone can create great data visuals on the intersection of clarity, correctness and beauty.

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.