by Scott Berinato

In a good chart, not all information deserves equal treatment. Designing so that the most important data leaps out at the audience is one of the easiest ways to turn prosaic charts into deeply effective ones. The key principle to remember is to emphasise and isolate.

Think of emphasis as what we do to the important data and isolation as what we do to the other data to help make the important data stand out even more. 

Emphasis comes in many forms – adding weight to a line so it’s thicker than others, using a colour that stands out or simply pointing to a bar with an arrow. 

Isolation usually involves reducing the emphasis on everything else. You could make all other lines or bars in your chart grey, or remove some data completely from view. You can group information so there are fewer unique types of data on the page. 

This simple approach will draw people’s eyes to the right place and help them get to understanding quickly.

Grey is your friend

Once you know which data demands emphasis and isolation, aggressively downplay the other data. 

Often that secondary data is important for context but not worth drawing eyes to. Just as a well-composed photograph contains foreground and background information, so should your chart. The background becomes part of the view, and is necessary for the composition, but it’s not where we focus.

The surest way to set that background is to use grey, other less dominant colours or reduced saturations of colours. Whether it’s dots on a scatter plot, lines on trend chart or even grid lines and tick marks on the axes, setting these to a neutral, unobtrusive grey will help that main idea pop, just as it does with Figure 2, a chart that is supposed to highlight the growing problem of student debt. 

Look at both versions and think about which conveys that idea more directly. One simple alteration completely changes the immediacy of the idea.

 

Create reference points

A well-executed chart that clearly conveys an idea found in the data still may leave an audience wanting because the data is the jumping off point for them, not the final point. They may ask, “but is that normal?” or “where do we stand compared to the competition” or “at what point should I worry?”

You can improve your charts by anticipating these kinds of questions and then providing visual reference points that direct the audience to what the answers. Reference points could be anything from an average rating mark to a line that shows the median family income to an idealised curve that shows what you hope growth looks like. Plotted data about competitors, public data about trends, or bounded boxes that show “normal” or “dangerous” ranges also can help an audience get more insight from your data.

We often miss this because reference points typically aren’t found in our spreadsheets. They need to be calculated (means and medians) or culled from other sources (publicly available data). Sometimes they’re our subjective emphasis, for example a line that shows “this is the point at which we should start worrying”.

Figure 3 is an example in which the chart maker has both emphasized and isolated one group and then added a reference point to bring home the point.

Show less

Of all the pieces of advice I proffer about making good charts, the one that most unnerves folks is when I tell them to show less. Simplify. Reduce the number of things you present to the absolute minimum.

I believe people are reluctant to simplify charts for a few reasons. For one, a busy chart means a busy worker. “Look at all my data and all the work I’m doing,” the chart seems to say, whereas a simple chart looks, well, simple. 

Also, if someone isn’t sure what their main idea is, they may resort to putting in all the ideas in the hope that one emerges for the audience. 

Finally, it turns out that simplicity is hard. The more we know about the data the more we think is important to include.

Having said all that, simplicity is worth the effort. Especially in presentations where you have just a few hundred milliseconds to capture someone’s attention. Present them with a chart in which they can’t see a main idea, or even where to start looking, and you’ll lose them.

Look for redundancy and remove it. Does the subtitle say the same thing as a caption? Does the title repeat the axis labels? Try to say everything you need to only once. 

Group data so there are fewer total marks. As with the scatter plot above, if the West Coast sales team is the one that matters, make all the others the same group, “everyone else”. Often we have many separate categories of data only because our spreadsheets do, when there are easy ways to reduce the number of variables. 

Try to eliminate as many structural marks as possible. Do you need grid lines? Tick marks? How many axis labels are just enough to get your point across. Do you need to label bars with their numeric values?

Be ruthless. I often turn it into a game – how much can I remove and still get people to see my main idea? It’s always surprising just how far you can go, as with Figure 4.

The competitive imperative

Data visualisation was once a nice-to-have skill for some design-minded managers who thought it might set them apart, or who just liked the challenge. Today, I argue, it has become a requisite skill, the only means we have to deal with the influx of data that runs our businesses.

If your business doesn’t invest in improving its dataviz skills, be sure that competitors are spotting risks and opportunities you don’t see. 

Don’t fumble through presentations, spending more time explaining what your charts mean than you do talking about the ideas in them. 

Improving data presentation skills is an investment worth making. And you’re here, so you’ve already started.