Data integrity is the dimension that measures the consistency and validity of data across different data sources. In this case, the data analyst wants to create one column that contains the customers' birth dates, but the data is stored in different formats and locations in the sample schema. For example, some customers have their birth dates in the customer table, while others have their birth years in the sales table. To compile the data into one column, the data analyst needs to ensure that the data is consistent and valid across the tables. Therefore, data integrity is the best explanation for the reason for compilation. Reference: Data Quality Dimensions - DATAVERSITY, The 6 Data Quality Dimensions with Examples | Collibra