Funders create charts and graphs—visualize their data—for different audiences and different purposes. Sometimes funders serve as unbiased disseminators of data as-is; other times they seek to tell a story—and fast. It’s not that one method of data visualization—as is vs. storytelling—is better or worse. But it is up to you to figure out when your viewers expect each style and then switch back and forth.
The as-is approach to data visualization is the easy one. You create a graph. You clean up the default settings a little. You select a color palette that matches your brand. (Default color palettes scream, I have no idea what I’m doing!) The storytelling approach can seem like the harder one. But it’s not impossible. It’s just a newer style for most of us.
Who are your viewers? What information do they need? Do they want to see the data presented as-is, or do they want you to interpret the data?
Storytelling Strategies to Consider for Your Graphs
Let’s take a look at some examples. The graphs on the left present the data as-is while the graphs on the right interpret the data. They illustrate how to use the following design strategies to tell a story with your data:
- Descriptive titles
- Descriptive subtitles
- Annotations, which are call-out boxes that give viewers more background information about a specific data point or two
- Color saturation
Example 1: A bar chart
The chart on the right uses a descriptive title and color saturation to show how chocolate is the preferred ice cream flavor.
Example 2: A slope graph
The descriptive title and color saturation (right) emphasize how Project A is performing particularly well.
Example 3: A dot plot
Finally, this dot plot uses a descriptive subtitle and color saturation (right) to draw viewers’ eyes toward the teachers’ survey responses.
Resolutions for the Coming Year
Wherever you are in your savviness with data visualization, and whether presenting your data as-is or telling a story, you can improve your outcomes with a commitment to any of the following mantras.
Repeat after me. In the coming year, I resolve to:
- Commit to easier-to-read layouts. I’m on a personal mission to banish cluttered, impossible-to-grasp-the-patterns-at-a-glance charts from your deliverables. Check out my complimentary tools, including a chart chooser to help you select an effective way to present your data and a data visualization
- Try a new format, just for fun, then hide it away for a few weeks before sharing it. Tree maps, social network maps, and more! Shiny! New! Give new chart types an honest try during your lunch break. But please don’t use them just because they’re cool. Use them when they’re the clearest chart for your viewers.
- Question every software program’s default settings. The first few times we use a software program, we’re tempted to trust the program. We might think, “I don’t really understand how this 3D chart is effectively communicating my message, but the software company must be full of experts, so I’ll just go with it.” It’s no secret that you’ll need to adjust almost every one of Excel’s default settings to produce something clear and comprehensible.
- Cease the search for a foolproof software tool. One day soon, we’ll stop arguing about which software program is best, and we’ll agree that our brains are the best visualization tools of all time. One day, in a galaxy far, far away, the software salespeople will also stop pretending that their tool is best.
- Stop looking for the perfect visualization book that will answer all my questions. The field is young. A good number of visualization professionals have written books, and dozens of partnerships for new books are in the works. In the meantime, turn your attention to blogs and tweets.Highly recommended Twitter accounts: @NYTgraphics, @Postgraphics, @WSJgraphics. Highly recommended blogs to get more from common software tools like Excel:
- Compliment that person who designed an amazing visualization. Like what you see? Tell the designer. Write them an email, comment on their blog post, send them a tweet. Your kind messages inspire new blog posts, conference presentations, and more, which keeps the field healthy and thriving. Don’t like what you see? Shhh. Best to keep your thoughts to yourself. It’s bad karma and nobody wants to be friends with Debbie Downer.
For more tips to help you create successful visualizations, download the Data Visualization Checklist I created with Stephanie Evergreen.
Ann K. Emery is a sought-after speaker who equips organizations around the globe to visualize their data more effectively. Within the past year, she led more than 60 trainings for more than 2,800 participants. Her design consultancy also overhauls graphs, publications, and slideshows with the goal of making technical information easier to understand for non-technical audiences. Ann chairs the American Evaluation Association’s data visualization interest group, serves as an advisory member of the American Evaluation Association’s Potent Presentations Initiative, and is the past Secretary for the Washington Evaluators.
Data visualization is a generic term used which describes any attempt to help understanding of data by providing visual representation.
Have to say, as a designer, this post really helped me to see that I should be emphasising to clients that they should sweat the fine details like their data labels to help transform simple graphs into story-telling opportunities. Really helpful – makes me want to discover the rest to your site.