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The uncomplicated guide to color palettes in data visualization & Qlik Sense

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A great color scheme can make your data visualizations more intuitive and more accessible to the viewer. It can set the right mood and draw people’s attention to the most important parts of your data story. A bad color choice… well, we’ve all been there.

There isn’t really any “magic bullet” when it comes to choosing the right color scheme for your data visualization, but there are a couple of general rules of thumb that can be leveraged to elevate your data stories. Here’s what we’ve learned while building our own data visualizations.


Sequential, diverging or qualitative data: what are you visualizing?

The first and main challenge when choosing colors for your data visualizations is understanding the data you’re working with and how colors can either enhance or diminish its impact. You got that right, bad color choice can make your data visualization difficult to understand or even warp the message you’re trying to get across (no pressure).

The three main categories that we should distinguish here are sequential data, diverging data and qualitative data. Now let’s look at how they differ.

Sequential color schemes are logically organised from high to low using a gradient effect and need to be represented by sequential lightness steps to form a clear visual message. You’re typically aiming to show progression rather than contrast with low data values represented by light colors and high values represented by dark colors. A gradient-based color scheme is the best choice to visualize progression.

Created using Vizlib Heatmap for Qlik Sense


Diverging color schemes come in very handy when you want to highlight deviations above and below the median data range. A typical diverging color scheme is a combination of two different sequential color schemes based on two different hues with a shared color at the critical midpoint. Each extreme is represented by the dark colors of a different hue, allowing for a stark visual contrast.


Qualitative color schemes are typically used to represent nominal differences or differences in kind. By using light and dark hues, you can easily create a lot of contrast and emphasize the most important data points. Keep in mind that the lightness of the hues used for qualitative categories should be similar but not equal.

A QUICK tip: To visualize qualitative data, use equally bright hues with contrasting colors. This will ensure you don’t skew the interpretation of your data by drawing attention to one data point over others. To visualize sequential quantitative data, use a gradient-based color scheme that’s made up of different shades and tints of one hue. This will help you to highlight the progression from high to low.


Where does color psychology come in here?

Now that we’ve got that out of the way, let’s talk about choosing the right hues. Hues are the unique colors in their purest form, without any shading or tinting — that’s your blue, yellow and red. Unique hues are critical in data visualizations when you need to create contrast and suggest to the viewer that your data points are comparative, not correlated.

Leveraging the right colors in data visualization allows us to use the psychological attractions to enhance the visual and complement the story. That’s because the human brain is wired to use colors to discern the world. Because of their psychological effect on our emotions and perceptions, colors are also a critical element of subliminal messaging.



Here’s how our brain perceives the three primary colors:

Red is associated with passion and energy. It can evoke strong emotions and create a sense of urgency. It is also known to stimulate the appetite and act as physical stimulation, accelerating the heart rate, nerve impulses and blood pressure. In data visualizations, red is the go-to color for creating emphasis and drawing attention to a particular data point.

Yellow taps into the part of the brain that deals with logic and enthusiasm, that’s why it’s often used to stimulate impulse purchases. It is considered to be a cheerful, happy color, but too much of color yellow can cause anxiety.

Blue instils a sense of calm, reliability and security and is associated with maturity. It is also often used to stimulate productivity.

The cultural aspect of color psychology should also be taken into account if you’re communicating to a global audience. Color meanings can differ drastically depending on the reader’s cultural background. For example, red symbolises good fortune and prosperity in China but evokes feelings of danger and caution in the Middle East. In Latin America, red is the color of religion when used with white.

Takeaway. Color selection in data visualization is not merely an aesthetic choice, it plays a crucial role in conveying quantitative information. A wrong color scheme can distort relationships between data values, whereas properly selected colors convey the underlying data accurately and help to tell data stories more effectively. When selecting a color scheme for your data visualization, consider the psychological effects of colors to avoid evoking unwanted emotional and psychological responses from your audience.


The simple rules for choosing the right colors


Have a wide range of hue and brightness.

The whole point of searching for appropriate color palettes is to make the data more accessible and easier to distinguish. And that can’t be achieved without enough variance in brightness and colors. While brightness in monochromatic palettes can make a huge impact on how accessible the data becomes, it may not always be enough. Consider also adding different hues to make it easier for viewers to distinguish data.


Keep it natural.

Let’s not forget that our brain is preprogrammed to favor natural color palettes that we see in nature all the time. Here is a great example from Graphiq; a color progression that transitions from light purple to dark yellow isn’t at all as pleasing to the eye as a transition from light yellow to dark purple. That’s because we’re used to seeing vibrant yellow morphing into deep purple in beautiful sunsets, whereas the former transition feels disharmonious and unnatural. If you were to use such color palette in data visualization, you would lose the reflexive intuitiveness that comes with natural colors and makes it harder for viewers to comprehend the data.


Consider the color-blind people.

Approximately 1 in 12 men and 1 in 200 women are affected by color blindness. While most color-blind people can see things as clearly as everyone else, they’re unable to fully see the red, green or blue light. The most common form of color blindness is known as red/green color blindness, which means the sufferers mix up all colors that have some red or green as part of the whole color. To work around this problem in your data visualizations, avoid using red and green together and if you must use them, leverage light versus dark. Ideally, you should try to go for a color-blind-friendly palette, such as blue & orange or blue & red.


Normal vision vs. Red/green color blindness


Our color palettes in action

Vizlib Colors
Vizlib Colors
Vizlib Colors
Vizlib Colors
Vizlib Colors

Still lost? Check out these tools you can use to always make the right color choice