The single idea that will instantly improve your data visualizations

Signal-to-noise: reduce the noise and pump up the signal.

P resenting data in an easy-to-read format is very useful. You might want to share some findings in a talk or build a dashboard for your software application. For this purpose, you have access to a variety of software tools and solutions. In this article, I’d like to share some tips for building data visualizations that are effective and beautiful at the same time. You can apply these principle in any software.

The core idea: signal to noise ratio

The very basic and most imperative principle of designing data visualizations is this achieving a high signal-to-noise ratio. What does this mean? Signal is the raw data that you want your listeners or readers to see and understand. Noise refers to all graphic things that don’t give additional information. You want to put as much data as possible in the visualization. The “noisy” elements should be taken away.

Imagine you are using a pen to draw your data viz, instead of software. With every line that you draw, ask yourself: “Is this line useful for making the data clearer?” Expressed differently: if you break down the chart into its elements (lines, shapes, type), which of these parts contribute to the data and which don’t?

One obvious example is the use of 3D. Excel still offers the option to build 3D bar charts, although it’s none of the default options anymore. What is the problem with 3D? If we look to our basic principle, it’s clear that 3D introduces additional elements which don’t add new information.

Less noise

I’ll walk you through an example with some made-up numbers. Everything was designed with the default templates that come with Excel 2016.

This template can be found in Excel 2016

You might have encountered business presentations with this kind of graphic before. It’s not per se a bad visualization, but it has a bad signal-to-noise ratio. Going back to the pen analogy, there are quite a few elements that I would need to draw which give no information.

Lots of unnecessary elements

There are the 3D elements: filled shapes that give no information. In fact, they confuse the reader because they introduce an ambiguous height of the bar.

The problem with 3D charts

The same goes for the sides: any useful information? Just shading. Does shading support data? It does not. It’s noise.

Then there is the elaborate grid in the background. Does it support the data? Not really, but you could argue that it’s necessary to read the values. That’s true, but it’s not the best solution. Your readers are lazy: they don’t want to look at the top of the chart, then follow the line to the left, then leaving the line to look for the numbers, then searching the line again. It’s a ton of work for something as simple as a number!

Let’s see what happens when we implement those things. We’ll kick the 3D effect and the grid.

Alright, that’s clearer. There is less confusing information, less noise. But there is still a lot of work for our readers, especially in finding out the exact values. Fortunately, we can add those quite easily.

Now, we have the labels directly attached to the bars. We can remove the gridlines, there is no use for them anymore.

This is rather minimalistic and already a very good result. We can make some changes to the legibility of the numbers and bars. The bars can be wider and the numbers formatted differently.

Fewer elements will increase readability

After we had decreased the noise by removing unnecessary elements, we can look at increasing the signal.

The first part was an exercise in minimalism: how much can I take away without breaking the chart? The next part will be about adding more signal.

More signal

As mentioned above, the signal is the part that communicates the data. There are some occasions where you might just leave the chart as it is. All data is equally important, all bars and labels are equal. If that’s the message you want to communicate, that’s alright.

In many cases, you do want to set an emphasis on your data. Why is this important? What exactly is important about it?

In our example we want to set an emphasis on the current year. We can add visual hierarchy by decreasing the importance of the other data and increasing the importance of the data we want to emphasize.

2018 will be a great year for our company.

It’s clear what the reader should look at: the only bar that has a color and bolded text. This bar is different. By increasing the visual importance, we’ve added more signal. The reader knows where to look.

To finish this off, we’ll add more information about the chart in the title.

Get some information in the title

This is clean, minimalistic and it has a great signal-to-noise ratio. Everything has purpose, nothing is superfluous.