Data Storytelling 101: Using Color Strategically for Impactful Storytelling
This article explores the crucial role of gray color in enhancing data visualization for effective data storytelling. It also provides some basic rules to select the right colors for your data visualizations. The cheat sheets on this topic will be included at the end of the article.
Foreword
Data visualization and data storytelling are two sides of the same coin. Effective data storytelling utilizes visualization to communicate insights from the data. Conversely, creating impactful visuals requires understanding the story you want to tell. Both elements work together to ensure stakeholders grasp the intended message clearly.
While not every data visualization requires data storytelling, it does play a crucial role in specific contexts. Exploratory dashboards, for instance, primarily serve users who want to independently examine the visuals, tables, or matrices. However, when presenting explanatory reports to stakeholders, data storytelling becomes essential to guide them towards data-driven decisions more efficiently.
Beyond understanding the basic functions of charts and graphs, a crucial aspect of data visualization is choosing the right colors. Selecting effective colors enhances the clarity and impact of your visuals, ultimately strengthening the overall data story.
This article dives into the surprising power of gray in data visualization, often considered the most important color in this field. We’ll explore how gray can enhance your visualizations and also provide some basic color selection rules to elevate your visuals even further.
Part 1: How Gray Can Enhance Visualizations in Several Ways
Gray plays a versatile and important role in data visualization for a few key reasons:
- Highlighting key information
By using gray for less important elements like gridlines, backgrounds, or unimportant data points, it allows colored data to stand out and draw the viewer’s eye to the most crucial aspects of the visualization.
- Providing context
Lighter shades of gray can be used to display background information or general trends in a dataset, allowing viewers to understand the bigger picture without being overwhelmed by color.
- Enhancing Clarity
For visualizations with a lot of data or bright colors, incorporating gray can help to create a more balanced and calming overall feel.
Part 2: The Basic Guide to Effective Color Choice in Data Visualization
a. Use Intuitive Colors
When picking colors for your charts, consider how your target audience might interpret them based on their cultural background. Leverage established color associations to enhance understanding. For example, red often signifies danger or stop universally, while green might suggest growth or go.
b. Maintain Color Consistency
Maintaining consistent color usage for the same variable across all your charts is ideal. This applies even if the variable appears in different charts. For instance, always use blue to represent the unemployment rate, regardless of the chart. However, if you introduce multiple colors for different variables in your first chart, avoid using those same colors for unrelated variables in subsequent charts. This repetition can be confusing and hinder comparisons between data points.
c. Avoid Sequential Colors for Categories
While it might seem visually appealing to use shades of the same color (e.g., a gradient of blue) for categories, this can be misleading. Readers often associate darker colors with “more” or “high” values, and lighter colors with “less” or “low” values. This creates an unintended ranking of your categories, which may not be your intention. To avoid this misinterpretation, choose distinct hues for your categories. This allows you to clearly differentiate between them and discuss them in your text by color.
d. Limit Colors for Clarity
Aim for a maximum of seven distinct colors in your charts. While colors help differentiate categories, an excess can overwhelm viewers. A crowded color palette makes it difficult to quickly interpret the data and forces readers to constantly reference the legend.
If your data requires more than seven categories, consider using a different chart type that might be better suited for presenting complex information. Grouping related categories into a smaller number of broader categories can also be an option.
e. Don’t Leave Them Guessing: Decoding Your Colors
Just like explaining the meaning behind the height of a bar or the size of a circle on a map, it’s crucial to explain what your colors represent in a data visualization. This ensures your audience understands your message. Here are three effective ways to create a clear and accessible color legend for your charts.
e. Gradients: Light for Low, Dark for High
To make your charts as easy to understand as possible, consider using lighter colors for lower values and darker colors for higher values. This aligns with most viewers’ natural expectations, allowing them to quickly grasp the data without getting confused.
f. Inclusive Design: Catering to Color Blindness
A crucial aspect of effective data visualization is ensuring accessibility for viewers with color vision deficiencies (CVD). The good news is that using gradients and palettes with varied lightness levels significantly improves the chances of colorblind readers distinguishing your data points. Remember, there are many types of CVD, so consider utilizing online tools or Datawrapper’s automatic colorblind check to verify your chosen colors are universally distinguishable.
For your reference, the cheat sheets are available for download:
Thank you for reading! I hope you found this guide insightful. Feel free to reach out if you have any questions or encounter any issues.
Reference:
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