Viewer considerations are key: What type of information do your viewers need to see? What information do they already have? What information are they expecting? How many points in time should be included? Will you present aggregated or disaggregated findings? How long do you anticipate that they’ll spend looking at your graph?

And here’s another thought-starter question to consider: Do your viewers want to see the data presented as-is, or do they want you to cut to the chase and interpret the data?

Sometimes we need to serve as unbiased collectors and disseminators of information. This is especially true for my workshop participants in research-y roles who publish their data in places like peer-reviewed journal articles or formal research reports with lengthy appendices.

Other times, we need to get a message across in our graphs — and fast! This is especially true for my workshop participants who are consultants or those who work in communications-y roles. Their viewers are busy, busy, busy. Their viewers are hoping that someone else — you! — will dig through mountains of data and uncover the handful of nuggets worth paying attention to.

It’s not that one visualization style is better or worse than the other. They’re apples and oranges. I want you to figure out when your viewers are expecting to see each style and then learn how to switch back and forth.


Four storytelling strategies I use in my graphs

The as-is approach is the easy one. You create a graph. You clean up the default settings a little, especially those cruddy parts like borders or too-thick grid lines. You select colors from the viewers’ color palette. I’ve been doing a lot of design projects with USAID contractors lately; this blog post has USAID’s exact shades of blue and red.

The storytelling approach can seem like the harder one. But! It’s not impossible! This is just a newer style for most of us.

I want to make it easier for you. Here are four design strategies you can use to tell a story in your graph:

  • Descriptive titles
  • Descriptive subtitles
  • Annotations
  • Saturation

Use one technique or all four, it’s up to you. Let’s check out a few examples. The graphs on the left present the data as-is while the graphs on the right interpret the data. 


Example 1: A bar chart

This first bar chart uses a descriptive title and saturation to show how chocolate is the preferred ice cream flavor.


Example 2: A slope graph

The descriptive title and saturation emphasize how Project A is performing particularly well.


Example 3: A line graph

descriptive title, a descriptive subtitle, and an annotation explain how the agency is funding more studies to measure the effectiveness of their programming, which is due to their new policy. Annotations are call-out boxes that give viewers more background information about a specific data point or two, like why we’re seeing a sudden increase or decrease.


Example 4: A donut chart

The as-is version on the left gives equal emphasis to both subgroups of students because the red and blue are both relatively dark colors. This is USAID’s exact shade of red (with data that is obviously not from USAID). The red is tricky because we’re accustomed to stoplight color-coding in which green means “good” and red means “caution!” or “bad!” In addition to the red and blue being equally saturated, we also have to be careful with the cultural connotations of using red here.

The interpreted-version on the right uses saturation to highlight the percentage of students who qualify for free and reduced meals.


Example 5: A dot plot

Finally, this dot plot uses a descriptive subtitle and saturation to draw viewers’ eyes towards the teachers’ survey responses.


Let me know: Which approach do you follow most often? And who are your viewers? Their preferences drive every decision about how you’ll format your graph, after all. Are your viewers expecting you to present the data as-is, or do they prefer that you offer interpretations through titles, subtitles, annotations, or saturation?