Color Choices in Data Visualizations: Importance and How To
By Indeed Editorial Team
Published March 11, 2022
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
Data visualizations are a useful way to present data points and insights. Using visual aids, charts and diagrams allow viewers to interpret information, make connections and remember keynotes that visualized data reports represent. Along with the physical structure of data in a visualization, the color choices and contrasts enhance its effectiveness. Understanding the benefits of color choice can help you in developing informational and engaging data visualizations. In this article, we discuss the importance of color in data visualization and how to choose colors.
Why is color important in data visualization?
Color is important in data visualization because it allows you to highlight certain pieces of information and promote information recall. Using different colors can separate and define different data points within a visualization so that viewers can easily distinguish significant differences or similarities in values. Choosing colors for a data visualization involves considering color theories and common color associations to reinforce the memorability of presented data. Colors can influence the way viewers perceive data and persuade them to feel positively or negatively based on the purpose of the visualization report.
How to choose colors for data visualizations
Consider these steps when choosing colors for data visualizations:
1. Analyze collected data insights
Before choosing the colors for your data visualization, analyze the collected data insights to determine the necessity of using a variety of colors. For data with two main values or categories, it may not be necessary to create a color palette to represent your data. If you have three or more values, choosing colors can be more beneficial.
2. Evaluate other visualizations
Review other data visualizations presented within the company to identify color palettes, themes and specific color associations. These other visualizations allow you to assess established color correlations with certain values or categories relevant to the new data visualization. Keep these color trends consistent by maintaining previously assigned colors when creating your visualization.
3. Limit color variations
Having too many colors can make the data visualization more complex and harder to understand, so it's important to stay within six or fewer different colors so that the color values remain distinct from one another. Choosing colors that are right next to each other on the color wheel can make it hard to distinguish. When looking at a color wheel with 12 colors, using six of those colors allows you to choose colors that aren't directly next to each other.
4. Group together similar data
If you have more than six values or categories to present in your data visualization, consider grouping similar values together to use one color for them. When grouping these values together, you can also use different shades of the color to demonstrate its slight variation from the main color value. The dominant value can maintain the original color, while relevant data points can vary with lighter or darker shades of the color.
5. Consider the type of color palette
With your data visualization application software or tool, you can find different pre-made or recommended color palettes that you can use. These palettes often consider color theories and shade variations to allow you to choose a palette and adapt it to suit your specific visualization needs. Depending on the type of data and report you want to present, there are different palette categories you can implement, such as:
Diverging: A diverging color palette is useful when representing three main values, with one category as the median or average, while the other two diverge from it. This uses a neutral color in the middle of the palette for the median and two opposite colors with varying shades to demonstrate their divergent values.
Qualitative: A qualitative color palette can best represent values of distinct categories using different colors. These palettes work for line graphs, bar graphs and pie charts.
Sequential: A sequential color palette uses one color with multiple shade variations that can vary from an almost black shade to an almost white tone. This type of color palette may suit values that represent a single category of data.
6. Assign colors to values
When creating a data visualization using colors to represent values, create labels or a dedicated legend that defines what color represents what value or category. For visualizations like graphs or plots that have an x-axis and y-axis, it's important to remember to label what those axes represent and to note the value increments for each line. Maintain company terminology throughout the labeling process and consider using company-established colors or a color theme that incorporates company colors.
7. Highlight important data points
Consider using color to highlight data points in your visualization. You can use muted or unsaturated colors to minimize the visual impact of other values while using saturated colors to create a distinct separation that highlights a few areas. Depending on the purposes of your visualization, you may choose to avoid using color on any value other than the desired points you want to highlight.
8. Determine the background-color
The background of your visualization can affect the perception of colors when there's a lack of contrast between the background and data points. Aim for a neutral background such as black or white. If you're using a wide range of color shades or tones that may look similar to black or white, choose a color with a different undertone to add contrast.
9. Use online resources and tools
You can find online color palette resources through a browser search to identify a tool that can help you achieve your desired visualization results. These tools can allow you to input specific colors you want and it produces a color palette that accommodates those colors while remaining distinguishable. Other tools may suggest different visualization types that may best suit the data you want to represent.
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