One form of data preparation is data standardization. It involves putting all of your data into one acceptable format. During data standardization, you will detect, correct, and sometimes remove undesirable data records.
Suppose you have several customer records in your personal data domain with their phone numbers formatted differently (i.e., xxx-xxx-xxx vs. xxxxxxxxxx). If you had to adjust those entries, so they are all formatted the same way, that would be data standardization.
Data standardization is usually done in a DQM tool or during data integration. It's essential because incorrectly formatted data can be unreadable or misinterpreted, rendering it useless and creating extra work for your data scientists (to make it usable).