In a customer CRM scenario, how would you handle data that needs to be inverted or transformed from the source format first?

When data arrives from a source system in a format that is not directly usable by the CRM, the first step is to perform an ETL (Extract, Transform, Load) process. Extract the raw data, then transform it by applying mapping rules, data type conversions, and any required inversions or normalizations. For example, if the source provides a combined field like "FirstName LastName" you would split it into separate fields. Inversion might involve reversing a string or changing a date format from MM/DD/YYYY to YYYY-MM-DD. After transformation, load the cleaned data into the CRM’s staging area, validate it against business rules, and finally move it into the production tables. Using tools like Talend, Informatica, or custom scripts in Python/SQL can automate this pipeline. Key considerations include handling nulls, preserving data integrity, and logging transformations for auditability.