Color: Highlight what matters

Choosing the right color scale: categories vs values

Choosing the right colors starts with a simple but critical question: what type of data are you visualizing? This distinction shapes everything that follows – from palette choice to how your audience interprets the chart.

In most cases, Flourish automatically applies the appropriate color type based on your data, but understanding the difference helps you make more intentional decisions.

Qualitative data (categories)

Qualitative data describes categories and answers questions like what type? which group? which segment? Examples include product categories, departments, countries, customer types, or marketing channels.

Because these categories don’t have a natural order, they should be shown using clearly distinct colors (usually no more than five to seven) to avoid confusion and prevent implying hierarchy.

For example, when comparing acquisition by marketing channel (Organic, Paid Search, Social, Email), each channel should have its own clearly different color.

Quantitative data (values)

Quantitative data represents values with order and progression. It answers questions like how much? how high? how low? how fast?

Because these values have a natural range, your color choices should reflect that progression.

Sequential color scales – low to high values

Use a sequential palette (light to dark shades of one color) to show increase or decrease. For example, when mapping population growth, lighter shades represent lower values and darker shades higher ones. The deeper the color, the greater the magnitude.

Diverging color scales – values around a midpoint

Use a diverging palette (two contrasting colors with a midpoint) when your data centers around a meaningful threshold. For example, profit vs. loss or above vs. below target. Each color represents movement in opposite directions from the midpoint.

Visual hierarchy through color

Color isn’t just decorative — it helps structure your visualization. By controlling contrast, you guide your audience through the data in a clear order: what to look at first, what supports it, and what provides context.

A strong visual hierarchy typically uses bold color for key insights, softer tones for supporting data, and neutral colors for background context.

Guide attention with intent

Use color to make your main point immediately visible. The most important data should stand out clearly, while everything else stays visually secondary.

For example, if your brand includes two bold colors, use them for the most important series. Don’t assign colors randomly — even when working with categories, stronger colors naturally draw more attention and can imply importance.

In Flourish, you can control this using color override settings to assign colors deliberately and reinforce your hierarchy.

Choose colors that feel natural for your data and its context.

For example, green often signals gains and red signals losses – but not everywhere. In Japan, it’s often the opposite, with red representing gains and green indicating losses.

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Use contrast, not more color

Adding more colors doesn’t make a chart clearer — it often makes it harder to read. Instead, create contrast.

A single strong color against muted elements is often more effective than a full palette. This keeps the focus on the insight rather than the design.

Color can also help show the difference between two values. For example, when comparing two lines over time, shading the space between them can make the gap easier to spot at a glance. In Flourish, the shade between lines option helps you highlight that difference without adding extra visual clutter.

Building an accessible color palette

Accessible color palettes make your visualizations easier for everyone to read and interpret. The goal is simple: use colors that remain clear, distinct, and readable across different screens and types of vision.

Flourish gives you 10+ default color palettes to choose from, including options designed for strong contrast, readability, and accessible color use. You can use these as a starting point, then customize or create your own palettes to match your brand, campaign, or story.

Ensure enough contrast

Make sure there is clear contrast between colors, text, and backgrounds. Low contrast can make charts difficult to read, especially on small screens or in bright environments. Tools like the APCA contrast calculator can help you test whether your palette is legible against a colored background.

Choose color pairings carefully

Some color combinations are harder to distinguish than others, especially for people with color vision deficiencies. Test your palette with a colorblind simulation tool to check that categories and highlights still appear clearly different.

Maximize clarity with fewer colors

Keep your palette to less than 7 visually separated colors that differ in both hue and lightness. More colors make it harder to maintain sufficient contrast and distinctiveness – if you need more categories, group related items or use gray for less important elements.

Use lightness as well as hue

Don’t rely on hue alone. Colors should also differ in lightness, so they remain distinguishable even when hue differences are less visible. Strong variation in brightness helps viewers separate elements more easily.