Telling Stories with Data: Dos and Don'ts for Beginners and Experts

Nowadays, we all seem to be swimming in endless data and information. This can present exciting opportunities to understand our work and its impact more deeply. It also means that if we want to help others understand, we need to be able to share data and information in ways that truly cut through the noise.

Often we spend so much time collecting and interpreting data that we leave little time and attention to thoughtfully reporting and sharing it. But if we don’t ensure that it gets attention it deserves, how can we expect to inspire the change we hope for?

Visuals provide the key. They help us learn a lot very quickly, are easily shareable, and leave lasting impressions. They also can be simple to create if you know what’s most important and are willing to learn as you go.

Here are some dos and don'ts to keep in mind as you create game-changing data visualizations:

Do be sure the data you are using is complete and reliable! Always list sources, ideally with links so readers can dive deeper if they want.

Don’t let your anxieties about design software stop you from creating. There are many free or low-cost user-friendly tools online with training materials and even service. A few recommendations are Tableau, Kumu, and Venngage.

Do take the time to think deeply about your audience. So your visual will be most appropriate and useful, consider who can benefit from understanding the data, what action you’d want them to take, what they need to know to do so, and, most importantly, what they care about. Then walk them through the data by identifying what they think the problem is and what solution you propose based on the data you are showing. For example, if you want to share data to persuade parents to vaccinate their children, keep in mind they are likely most concerned about keeping their children safe, so you may want to use data that demonstrates the safety of getting immunizations and the dangers of not.

Don’t make it hard for them to have an “aha” moment. Use clear quantities (3 new clinics) and ratios (3 of 10) instead of percents or percentage changes (30%). Help your reader understand why your data is so significant by comparing it with data from similar situations, other places, longer timelines, etc. For example, instead of comparing 762 homicides in Chicago with 334 in New York, compare 24 versus 3.8 murders per 100,000 residents. Even better, compare those with 50 per 100,000 in New Orleans.


Do use visuals that show key comparisons.

  • If you are comparing locations, use a geographic map.
  • If you are comparing ties within networks, use a network map.
  • If you are showing change over time, use a line graph.
  • If you are showing ratios, use a pictograph.
  • If you are showing other amounts, use a bar graph.
  • If you are showing proportions, use a stacked bar graph.

Be wary of using visuals that make it difficult for the reader to compare, like bubble maps, donut charts, or treemaps.

Don’t bog the viewer down with clutter. Don’t use 3D graphs, pie graphs with more than a few slices, or icons that don’t add any meaning. When in doubt, remember that you want the data to be conveyed in what William Playfair (the inventor of the bar, pie, and line graphs) called “one simple impression.”

Do use images of familiar objects, if they help your reader connect to the data. For example, you might use bathtubs to describe amounts of water or football fields to describe long distances.

Don’t forget to think carefully about color. Dark text on a light background is the most legible. Avoid red/green and yellow/blue color combinations as those will be hard for those who are colorblind to read. Stick with only one or two colors (ideally ones that will still contrast with one another if printed in black and white) and use them to emphasize important points, lines or bars in your graph.

Do use narrative to clarify your message. Titles should either directly say what the graph means or ask the question that the graph answers. Be sure to label features, axes, etc. very clearly. Annotate significant data points to make your graphs more meaningful. It’s also great practice to couple quantitative data (numbers and graphs) with qualitative data (testimonials or case studies) to make it more personal and relatable. Be sure to use active voice and replace jargon with more common words. Only use acronyms if they are well known (such as CPR or MRI) or will further educate your reader (they may benefit from knowing what TANF is, for instance). Check out the CDC’s resources for using plain language.

Don’t worry about doing it perfectly. Embrace that creating and sharing meaningful visuals takes practice, just like collecting meaningful data does. Ask others, ideally those you ultimately want to your share data with, to give you feedback on whether the data is understandable in the way you are presenting it. When you get stuck, reach out for support from colleagues, peers, or online groups. When you’re ready to take your skills to the next level, you can dive into a book like Storytelling with Data by Cole Nussbaumer Knaflic, Resonate by Nancy Duarte, or The Truthful Art by Alberto Cairo.

Do think of data communication as a ongoing conversation. Provide opportunities for your audience to engage further and learn more. Be open to learning from them, especially about what they are struggling to understand, so you can provide better solutions as you continue to practice.

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This article was originally published by The National Rural Health Resource Center.