What is the difference between data cleansing and data validation?
- Data cleansing ensures data integrity, while data validation ensures data availability.
- Data cleansing focuses on ensuring data consistency, whereas data validation focuses on data accuracy.
- Data cleansing involves correcting or removing inaccurate or incomplete data, while data validation ensures that data adheres to predefined rules or standards.
- Data cleansing involves removing duplicates, while data validation involves identifying outliers.
Data cleansing refers to the process of detecting and correcting (or removing) inaccurate or incomplete data from a dataset. It involves tasks such as removing duplicates, correcting typographical errors, filling in missing values, and standardizing formats. On the other hand, data validation ensures that data meets specific criteria or conforms to predefined rules or standards. It involves tasks such as checking data types, ranges, formats, and relationships to ensure accuracy and consistency. Both processes are crucial for maintaining high-quality data in databases and analytics systems.
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