The guide explains how to maintain consistent colour mapping when a value is missing, ie to resolve issues like this:
Why using a discrete color palette causes issues
Discrete color palettes assign colors according to the sorting and number of values in a field - if the number of values changes, then the assigned colors will change.
Why is this important? If you want your values/variables to have the same color, even if the scale/range in your data changes, you can make some edits to the color palette feature to keep the colors fixed to the variable.
When using a discrete color palette, colors aren't assigned to specific values.
The Solution: assign colors to categorical variables via a complementary continuous field
If your dataset doesn't already have a numerical key assigned to your categorical variable, then you can create one.
In the example dataset, each status value has a corresponding status_id.
We will pull status_id into Color on the visual design tab, and pull status into Label.
Define the Domain (min) and (max), and Number of steps according to the data range of the numerical key column.
Now the colors should stay consistent, even if the distinct number of categorial variables in the field changes.
See the full canvas example here.