Handling missing data is crucial for maintaining data quality. Ignoring or dropping the data is not a recommended approach. Filling in missing values with a default value may introduce bias or inaccuracies. A better approach is to use a data transformation tool like Azure Data Factory to apply custom logic for imputing or estimating the missing values based on other available data, thus preserving the integrity and completeness of the dataset.