Data validation involves several steps, each designed to ensure the integrity and accuracy of the data. These steps include:
Data Cleaning: This involves identifying and correcting errors in the data, such as missing values, duplicates, and outliers. Consistency Checks: Ensuring that the data is consistent across different datasets and within the same dataset. Data Verification: This involves cross-referencing the data with other sources to ensure its accuracy. Validation Rules: Implementing rules and algorithms to automatically check for errors and inconsistencies in the data.
