3 color principles

In all data visualizations you want to have the user quickly identify the most important information. You might need to draw their attention to an outlier, to a metric that is under-performing or over-performing. This can be done through color, though not only, by applying these principles:

1. Use commonly understood colors

We get bombarded by colors every day. We see a multitude of colors with different tints, shades, and tones. Unknowingly, we associate these colors with different moods, feelings, messages – and advertisers know this, of course. For example, we might associate green with freshness, new, luck, safety, and so on. The reason why I’m mentioning this is to be aware of these meanings and associations and apply them when choosing colors for your data visualizations. Let’s take another example, which is more universal: the traffic light colors. Red represents danger, yellow represents attention required or caution, and green means safety, or “go ahead”. It is very common to use primary colors (blue, red, yellow) and/or secondary colors (green, orange, purple) in reports and dashboards, to draw awareness.

In the bar graph below (Figure 1) you can see that through the usage of color green, we can quickly identify those manufactures with sales over $18k, and in red, those under $9k.

color attention fig1
Figure 1

On the other hand, in Figure 2, when we graph the profit from each manufacturer, the same colors are now deceiving us. They don’t convey a message of positive vs negative anymore, but they just take into account the large absolute values, ignoring the fact that they are positive or negative. Our brain can get a bit confused and tries to understand why a negative value is portrayed by a “positive” color, such as green. In this example, green was not properly used and you end up spending too much time on trying to understand the message this graph is sending.

color attention fig2
Figure 2

If you want to draw attention to those manufacturers with the highest profit and the lowest negative profit, in relation to sales, here’s a better way of doing so. Use red to denote a loss and green a profit. You can quickly identify in the bar graph below that Cubify has a plenty of sales, but also is losing money, whereas Ativa has a bit over the average number of sales, but the highest profit. This simple use of color draws the attention where it’s needed.

color attention fig3
Figure 3

My 2 take away messages:

  1. Regardless of the visualization, the user should always have the same color-meaning association. In this case green = positive and red = negative. So be consistent
  2. Be mindful that depending on the culture, the same color can convey different messages. So adjust the colors based on your audience.

2. Reference the color wheel

colour wheelThe color wheel is a designer’s friend. I’m not going to go into details of design principles, but will offer an advice I often see people don’t take:

If you want to draw a user’s attention, don’t only use colors adjacent to each other on the color wheel as they tend to seem too similar to a lot of end users.

In the example below, you might not think there is much of a difference between each color. In fact, you might assume there are 3-4 colors which repeat themselves, but actually each state is represented by a different shades of blue and blue violets. Not very helpful. If one is looking for North Carolina and is familiar to the color assigned to it, it cannot be easily identified with the pallet below.

color attention fig4
Figure 4

On the other hand, if you knew North Carolina was associated with pink, it can be easily identified in the graph below because all these colors are not adjacent to each other. Ok, orange and yellow are, but they don’t have the same hue level.

color attention fig5
Figure 5


Do you need some data visualization ideas? These are the 5 graphs you should always use


3. Reduce the saturation level

In nature, full-saturation colors often indicate threats or mating opportunities (think feathers, flower petals, blood, etc.), and we’re evolutionary wired to pay attention to them quicker. In Figure 6 (see below), even though we’re using the same green and red principles to denote those who exceeded their sales quota vs those who are below a 50% threshold, our eyes jump all over the place as the colors are too strident.

color attention fig6
Figure 6

Now, compare the above graph with Figure 7. We can visually digest this information a bit better.

color attention fig7
Figure 7

A best practice is to tone down those less important metrics so your eyes can focus on just two or three of them. In the example below, I’ve desaturated the color completely from the 50%-100% range.

color attention fig8
Figure 8

Colors can be a powerful asset, if used properly. Even so, they are not right for all audiences and all medium. Always take into account any visualization disabilities one might have (ex: there are different types of color blindness) and the medium your visualization will be represented on (ex: different screens, different papers, different printers, etc.)

{"email":"Email address invalid","url":"Website address invalid","required":"Required field missing"}

About the author 

George Firican

George Firican is the Director of Data Governance and Business Intelligence at the University of British Columbia, which is ranked among the top 20 public universities in the world. His passion for data led him towards award-winning program implementations in the data governance, data quality, and business intelligence fields. Due to his desire for continuous improvement and knowledge sharing, he founded LightsOnData, a website which offers free templates, definitions, best practices, articles and other useful resources to help with data governance and data management questions and challenges. He also has over twelve years of project management and business/technical analysis experience in the higher education, fundraising, software and web development, and e-commerce industries.

You may also like:

Main considerations for testing BI reports