Solution

A uniform schema ensures consistency in data structure, field names, and data types across merged datasets. This step is essential when combining data from multiple sources, as differing formats can lead to analytical errors. For instance, if two datasets store date fields in different formats (e.g., MM/DD/YYYY and DD-MM-YYYY), creating a uniform schema resolves these discrepancies. Standardization through a schema improves data usability, reduces errors, and facilitates smooth integration. It is a proactive approach to achieving consistency in datasets. Why Other Options Are Incorrect: • A: Deleting records leads to data loss and should be a last resort. • B: Normalization standardizes data within a range but does not address format inconsistencies. • D: Replacing with null values creates gaps and reduces dataset completeness. • E: Ignoring inconsistencies compromises the accuracy and validity of analysis.