Three Types Of Context To Make Your Audience Care About Your Data

The following scene is one of the most pivotal moments in the Game of Thrones series.

As a loyal viewer, this scene represents a turning point for Tyrion. He has reached a breaking point after a lifetime of conflict with his father. His speech is the moment that he sets out on a different path, a path that ultimately leads to (spoiler) the murder his father and (unsurprisingly) a deep schism with his family.

For a new viewer, it is a courtroom confession in costume.

Your experience of entertainment is entirely different based on the context you bring. It makes a world of different to know: Why we are here in this room? Who are these characters? What are their motivations?

Context is the foundation that gives a scene something to build on. Context makes your audience care.

It is the same thing when you design a dashboard, report, or analytical interface (with less beheading and back-stabbing). Lack of context — the set-up that explains the background and motivation for the data — may be one of the primary reasons why dashboards and reports fail to connect to audiences. And it may be the reason you can’t get your colleagues to open that spreadsheet you just sent.

How do you make someone care? You want to anticipate and answer a few inevitable questions:

  1. Why does this data matter to me?

  2. What am I about to see?

  3. What actions can I take based on this data?

Let’s explore these three elements of context with a few examples.

1. Why does this data matter to me?

Context should make it clear why the information is important. At Juice, we always start designing a data story by defining the audience we want to reach. It is best if we can be specific about the kind of person and role that they play in their organization. This person has things they want to accomplish that will make them successful. A good design takes all of that into account.

When it comes time to show the data, there is no reason to be secretive about who should be engaging with the data and why it is designed for them. As an example, take the following introduction to an analytical tool the New York Times’ Buy or Rent Calculator.

2. What am I going to see?

"Tell them what you are going to tell them, tell them, then tell them what you told them."

This famous piece of advice is often ignored by dashboard and report designers. A title isn’t enough; you should explain the scope of the content and, ideally, how the different elements fit together. Is there a structure or framework that undergirds your choice of metrics? Explain this visually before tossing your audience into the deep water.

One way that we’ve found to deliver this context is to provide an automated step-by-step tour of the content. You’ve undoubtedly experienced this approach when to try a new mobile app. The app designers walk you through the workflow and explain features. If done well, you’ve helped new users wrap their head around what they are going to see.

The following example prominently features a descriptive legend showing how to read the glyphs.

You may also want to consider ways how to help the user understand the interactions of your data interface or even show them the types of insights they can glean from the data. Here’s a great example showing survey data about the challenges women face in different countries.

3. What actions can I take on this information? 

Finally, effective context setting explains exactly how the data can guiding your audience to smarter actions. Your report or dashboard should lead to actions, not just show interesting data. It should point to what comes next.

The following example shows data about inequality in travel visas by country. For an individual, the actionable question is: For my country, where can I easily travel to? Data products are inherently personal so you want to highlight this in your context.

Context along a timeline

In summary, we can think about these three essential elements of context along a timeline. You want to explain for your audience:

1) Looking backward, what brought you to viewing this data?

2) Now, what should you see when they engage with the information?

3) Looking forward, what action can come from exploring the data?