How to Influence With Data Visualization

Is it possible to influence with data visualization? What impact does your choice of a chart make? How can we start directing thoughts and conversations with data visualization? Let’s look at examples of how to influence with data visualization.

As I previously wrote in my blog post on visualizing to persuade and inspire action, representing data in a visual format is about seeing and envisioning. The simple elements, like bars, lines and circles, should guide the eye. There should be nothing there to impede the viewer, or to allow them to be misdirected by their own biases. The story should flow and hold together. There should be nothing unnecessary.

Influence with Data Visualization - Measuring Goal 16

Selecting the right type of visualization can be difficult.

I'm sure you've seen at least one of the variations of this graph. It is often given to beginners in data visualization as a guide to visualization-type selection.

Image source:

It was designed by Andrew Abela.

There is a lot of talk about which charts should be used, and which ones should be avoided. I'm not going to get into this here. In my opinion, all types of charts serve a purpose, and when carefully used, can communicate well.

You should try to see beyond, and deeper than, the visualization type. All of them are simply ways to represent facts, but facts aren't as important as what they add up to, and where they lead.

The same data can be represented in so many different ways.

These two are stories about nature conservation. One was created by Angie Chen and the other by Florin Iorganda. They were created for an organization called Protected Planet, which is part of the UN Environment Programme as a part of Data for a Cause.

Influence with Data Visualization by Angie Chen
Influence with Data Visualization by Florin I

These visualizations use the same data set. But they tell the story in different ways.

Let's focus on the line charts, for example.

Influence with Data Visualization - line chart

Angie uses the running total to give the positive message that overall the situation is improving. Florin uses totals for each year, putting the emphasis on the fact that the number of conservation areas hasn’t grown very much in recent years in the UK.

So, the same choice of chart, the same data, but a different story and different message.

And this is the choice that we, as data visualization designers, have to make. Depending on the angle you take, the story will have a different flow. There is no one right way to show things. But the choices you make ultimately will have an impact on the way you influence with data visualization.

This visualization was developed for Vision of Humanity by the Institute for Economics and Peace. It measures Sustainable Development Goal 16 set by the UN. Goal 16 is about promoting peaceful and inclusive societies through sustainable development.

Let's focus on the map visualization. It shows discrimination levels in Europe. Discrimination is measured as a proportion of the population reporting having personally felt discriminated against or harassed in the previous 12 months. The red bubbles show more discrimination, while grey bubbles show less discrimination.

There are other ways we could tell this story. We could use a map visualization and use colors for the countries instead of for the bubbles.

Influence with Data Visualization - Map vs Bubble Chart

Green colors tell a positive story. Most countries in fact are alright in this regard. But in this case, we needed to focus the conversation on the areas where the problem exists, such as Croatia, which have higher levels of discrimination (the large dark red bubble).

But Croatia isn't a large country, so the map visualization doesn't communicate the story effectively in the way we need it to. The bubble chart tells the story better. It allows for capturing the attention of the viewer right away. And most importantly, it helps steer the conversation in the right direction.

And so design decisions allow us to influence with data visualization. There is no one right way of telling a story. We have to look at this from difficult angles, and make our choices accordingly.