What are the most effective ways to visually communicate data in storytelling?

3 Add context and annotations

A data visualization without context and annotations can be misleading or meaningless. To add context and annotations, you need to provide some essential information that can help your audience understand your data and your story. For example, you can add titles, subtitles, labels, legends, axes, scales, units, sources, and dates to your charts to explain what they are showing, how they are measured, where they come from, and when they are relevant. You can also add captions, headlines, summaries, bullet points, or callouts to your charts to emphasize your main findings, insights, or recommendations.

4 Tell a story with data

A data visualization without a story can be boring or forgettable. To tell a story with data, you need to craft a narrative that can engage your audience, deliver your message, and inspire action. For example, you can use a data visualization to show a problem, a solution, or a result; to compare and contrast different scenarios or options; to reveal a pattern, a trend, or an outlier; or to illustrate a cause, an effect, or a connection. You can also use a data visualization to create suspense, surprise, curiosity, or emotion; to ask questions, answer questions, or raise questions; or to challenge assumptions, confirm hypotheses, or suggest actions.

5 Simplify and declutter

A data visualization with too much clutter can be confusing or distracting. To simplify and declutter, you need to remove any unnecessary or redundant elements that can interfere with your data and your story. For example, you can remove any chart elements that do not add value, such as gridlines, borders, backgrounds, or shadows. You can also remove any data points that do not support your message, such as outliers, noise, or anomalies. You can also remove any text that does not clarify, such as jargon, acronyms, or technical terms.

6 Test and refine

A data visualization without testing and refining can be inaccurate or ineffective. To test and refine, you need to check your data and your visualization for any errors, inconsistencies, or biases that can undermine your credibility and your impact. For example, you can check your data for any missing, incorrect, or outdated values; your visualization for any misleading, distorted, or inappropriate charts; and your story for any logical, factual, or ethical flaws. You can also test your data visualization with your intended audience, and collect their feedback, questions, and suggestions to improve your design, your message, and your delivery.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?