Difference Between Data Cleaning and Data Validation

When it comes to data analysis, it's crucial to understand the distinctions between data cleaning and data validation:

Data Cleaning:

Data cleaning involves identifying and correcting errors or inconsistencies in the data. This process aims to improve data quality by removing duplicate entries, correcting inaccuracies, and handling missing values. Data cleaning ensures that the data is accurate, complete, and reliable for analysis.

Data Validation:

Data validation, on the other hand, focuses on verifying the accuracy and quality of the data. It involves checking the data against predefined rules or validation criteria to ensure it meets specific standards or requirements. Data validation helps in identifying any discrepancies or anomalies in the data that may affect the analysis results.

In summary, while data cleaning is about fixing errors and enhancing data quality, data validation is about confirming the correctness and validity of the data according to predefined criteria.