Consider a situation where the data model changes after the initial seeding. How should the data seeding approach be modified to accommodate these changes?

When the data model changes, it's essential to modify the data seeding approach accordingly. Automatically detecting model changes and updating seed data can streamline the process, ensuring that the seeded data remains aligned with the updated data model. Manually updating seed data scripts can be error-prone and time-consuming. Version control for seed data can help track changes but may not provide automated updates. Using database migrations to modify seed data ensures consistency and simplifies the process of adapting to model changes.