‘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.

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?

Visualization: our most powerful tool

Visualization is one of the key solutions to cope with the endless stream of data, content and information – together with other strategies such as filtering and organization. Visualization might very well be the most powerful tool we have to turn complex information into manageable insights. But why is that?

There are three important reasons why data visualization is a very strong way to present information:

1. its information density is extremely high,
2. it attracts the attention of your audience, and
3. visual information is easier to process and memorize.

Reason 1: information density

Researchers at MIT have shown that we can detect the meaning of a picture in as little as 13 milliseconds – that’s extremely fast.

We could spend hours looking at this dataset for example, created by visual journalism professor Alberto Cairo, without learning anything. But as soon as we turn the data into a scatter plot, it’s obvious that we’re looking at a dinosaur. In the blink of an eye!

Reason 2: attractiveness

Visual information is also attractive. Not in the sense that it is beautiful to look at (although that’s often also our goal), but literally: it attracts the attention of your audience. In a book or newspaper, people will often look for the pictures first, before they start reading all of the text.

Reason 3: easier to process

Finally, charts and infographics are easier to process than written text. The dual-coding theory, developed by Allan Paivio, states that our brains process information both in a visual, as well as a verbal way. If we only get verbal stimuli, only a part of our brain is working. That’s why during a long phone call we automatically start doodling – the visual brain is bored and looking for things to do. By providing our audience with a combination of text and images, the entire brain is stimulated, leading to better focus, better understanding, and better memorization.

Harnessing the power of data visualization

So in summary, data visualization is powerful because it combines a high information density, attractiveness, and easier processing and memorization.

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.