Data cleaning is the process of identifying and correcting errors, duplicates, and inconsistent formatting in a dataset before it is labeled or used. It happens before labeling and modeling, since errors introduced at this stage compound through every later step.
Example
Deduplicating business records that appear twice with slightly different spelling, then standardizing every phone number to the same format.