| 1 |
Identify the nature of your data |
Understanding the data set and the types of variables it contains is important to determine the number of colors and how color should be used |
| 2 |
Select a color space |
Having the right color space ensures that the colors one sees on a monitor or in print will match that of the initial image |
| 3 |
Create a color palette based on the selected color space |
With knowledge of the data, specific rules permit the selection of colors from the chosen color space |
| 4 |
Apply the color palette to your data set for visualization |
Colorizing a data vis adds meaning and it is important to use a color mapping that supports the story behind the data |
| 5 |
Check for color context in your data vis after the color palette is applied |
Surrounding colors may lead to seeing different data points as the same, although their colors are effectively different |
| 6 |
Evaluate interactions of colors in your data visualization |
Understanding which color interactions can be avoided or which ones should be considered helps to design better data visualizations |
| 7 |
Be aware of color conventions and definitions in your particular discipline |
Depending on the audience, some colors may have unintended connotations and meaning; prompting the consideration of other colors |
| 8 |
Assess color deficiencies |
Involving and reaching a much larger audience is an important aspect of visual communication |
| 9 |
Consider web content accessibility and print realities |
Knowing where the final image will be displayed enables the audience to see it as intended |
| 10 |
Get it right in black and white |
Using color sparingly is relevant when color is not needed. Black and white or gray scale visualizations can also be considered |
