Data drift is a change in the distribution of the data received in production compared with the training data. It can degrade a model's performance without any error being raised. It is detected by comparing distributions, for example with statistical tests or PSI.
Example
After a pricing policy change, the customers' average order value goes from 40 to 65 euros: the panier variable has drifted.