Data validation definition
Data validation refers to the process of ensuring that data is accurate, complete, and consistent before it is used for a specific purpose. The goal of data validation is to identify and correct any errors or discrepancies in the data to ensure its quality and reliability.
Data validation techniques:
- Format validation. Verifying that the data is in the correct format, such as date, time, or currency.
- Completeness validation. Ensuring that all required fields are filled out and that there are no missing values.
- Range validation. Verifying that the data falls within an acceptable range of values.
- Cross-field validation. Ensuring that the data in one field is consistent with the data in another related field.
- Business rule validation. Verifying that the data follows specific business rules or requirements.
Data validation process:
- Defining validation criteria that the data will be checked against such as format, completeness, range, consistency, and business logic.
- Gathering data may involve importing data from external sources or entering data manually.
- Applying validation rules manually or via software to check for errors or inconsistencies.
- Identifying and correcting the errors. The errors may be categorized based on their severity and priority and corrected either manually or using software.
- Revalidating the data after the errors have been corrected is necessary to ensure that the data is accurate, complete, and consistent.