How do you avoid color overload and confusion in your dashboard?

Don't forget that color has very strong cultural implications - and the interpretations of color will vary between cultures. So if you have a diverse audience, be sure to understand how those colors might be interpreted by your entire audience. Don't assume that your understanding of color meanings is universal.

If you are using your companies marketing guidelines to choose colors, keep in mind that the message the company is making with those colors may not be the same as the message you want to convey in your dashboard. You can still follow those guidelines and incorporate the colors into your report without using them as indicators in your charts.

Yes, and having company wide standards/guidelines for data visualizations is a foundation for consistent and efficient data/visualizations interpretation across company, empowered by nice and uniform reports and dashboards. Also it would simplify developers' efforts of choosing colors.

A trusty tool I turn to is ColorBrewer: https://colorbrewer2.org/. Be sure to turn on the filter for color blind-friendly palettes if color is the only means of distinguishing between data (e.g., some people can't distinguish between red and green).

https://colorpicker.me Is also a useful tool for selecting color palettes--complementary, triadic, tetradic, etc. If you use Seaborn they have a built-in function for that as well- sns.color_palette("Spectral") See: https://seaborn.pydata.org/generated/seaborn.color_palette.html#seaborn.color_palette

If you can, use just one color. Maybe everything is gray, except the ONE item that really must stand out. Don't start with the assumption that you can use "up to 5-7". Instead, start with the assumption that you're going to use as few colors as you can. Challenge yourself. Can your visual even work in black and white? Can you use one color? Fewer is better to maximize your impact and communicate one or two key points clearly and effectively.

Also be aware that while humans are pretty good at detecting contrast differences, we're not that good at differentiating between subtle color differences. And we're also not very good at quantifying color values (is that shade of blue 2X as dark as the other one?) So while color is good for categorizing things and for denoting quantities - in both cases, there are limits to our precise understanding of those differences. So use with caution (you can learn more about this by reading books by Colin Ware and Tamara Munzner).

A simple way to test colors is asking different people what they think is happening in the report. This can provide insight into if the colors are providing the message you intended or if the colors lead to inaccurate assumptions.

Unfortunately, my last contract job fought me on trying to avoid colorblindness confusion; for example, they wanted red versus green, which I understand symbolically, but these are confused by people with one type of colorblindness. I was keeping in mind that they were telling me that the visualizations would be made available to end users (company customers), and years of being an educator and content creator has kind of hardwired this consideration within the way I present to others. I found that the best thing to do was let them know why I had chosen my original color scheme, and then change it to the colors as instructed.

Don't forget that at the end of the day, a visualization is only as good as the clarity of the data contained within it. If the ultimate story, insight, or correlation of the data is unclear, color and aesthetics often muddy the messaging even further. Save the color themes and aesthetics for the end, once you have vetted the integrity of the data itself (and even the chart type employed). Finally, it never hurts to get input from others you trust on your visualization's overall design and conveyed message.

“Form follows function”. Color should be used to add focus and attention, NOT aesthetics. A dashboard or visualization that is built with a purpose, exists to drive action. There is a balance in how to achieve this. Your focus should be to build an effective data viz first, run it by the primary users of that viz, and then refine it and add color contextually. Adding color to categorize and strike a contrast between segments is an effective use of color so long as it clearly, effectively, and efficiently communicates the point. An ineffective use of color is to create something beautiful that has little utility, but looks “cool”. Some of the most effective data viz I’ve seen are simple column charts or pareto charts with little color.