Our explanatory approach to visualizing data

If you make a tooltip or rollover, assume no one will ever see it. 
Archie Tse, New York Times graphics editor

When visualizing data, it's tempting to show all the data you have, with tabs, dropdown menus, etc. This article explains why Datawrapper doesn't let you do this. 

The difference between exploratory and explanatory data visualization

Imagine browsing Reddit, searching for your keys in your flat, going through a clothing store, or navigating through a computer game. These things are "exploratory": You yourself need to decide where you'll go next. Nobody guides you. You need to actively and freely explore your environment. And you need to interact with it, otherwise, you get nothing in return: You click buttons in Reddit or the game, open drawers in your flat, or try out hats in the store.

Browsing a shop (or Reddit) is an exploratory experience. Photo by Ashim D’Silva on Unsplash

Now imagine watching a movie, reading a web article, or listening to a talk at a conference. These things are "explanatory". An author – the movie director, the article author, or the speaker – decides what you'll see next. You don't need to interact a lot with the movie, article, or speaker to get something out of it. Instead, you trust the author to lead you through their content in a way that's most entertaining, enlightening, or efficient.

Reading a book is an explanatory experience. Photo by Sincerely Media on Unsplash.

Data visualization can be exploratory or explanatory. 

Exploratory visualizations work well for users that have a certain goal – e.g. finding out the situation for their country. But often, a user doesn't know yet what's important. They trust the author to show them what's important. That's what explanatory visualization is for.

Datawrapper is a tool to create explanatory visualizations

Datawrapper was made for these explanatory, author-driven experiences. We help you visualize data with which you can explain something to readers in a direct way. Often, that will be a statement you want to make, like "Unemployment is going down". Datawrapper works great for that. Here's an example of a clear statement made with a Datawrapper chart:

So we don't let you hide crucial information. If some information is important for a chart, map, or story, it shouldn't be hidden in a layer that readers first have to reveal by clicking a button. Because:

If the content is hidden, readers won't see it

Assume that your readers won't find your content if the first need to interact to see it (e.g. hover, click a button, use a filter). Yes, you know the data by heart and all the interesting (hidden) bits in it. But a reader skimming your article on a mobile device will likely miss them. 

Tracking has shown that few readers interact with data visualizations if the interaction isn't crucial to continue in the article.

If you make a tooltip or rollover, assume no one will ever see it. 
Archie Tse, New York Times graphics editor
Large portions of your readership may miss huge parts of the dataset entirely, just because they're either oblivious to the possibility, or choose not to interact. 
Using Datawrapper nudges you to present your data in a straightforward way. Readers won't need to click twice to get to the really interesting parts. Instead, everything important is visible right away, at first glance at the chart. This way, w e hope that using Datawrapper means that more people will see the statements you want to make with your data.

Alternatives to tabs and drop-downs

Because of the reasons stated, most of our available chart types don't offer drop-down menus or above charts or maps, and the reason why we don't implement tabs:

Elements of data tools that you won't be able to create in Datawrapper. 

But there's often a way to show your data with Datawrapper, anyway. If you're curious, visit our article  Why we don't offer drop-down menus & tabs, and what to use instead

And if you have any questions, let us know at support@datawrapper.de. We're looking forward to hearing from you!

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