Data Validation ensures that data has been cleansed to guarantee its quality. It employs routines, often called “validation rules,” “validation constraints,” or “control routines,” which check the accuracy, meaningfulness, and reliability of the data being entered into the system. The rules can be implemented through the automated structures of a data dictionary or the inclusion of an express validating logic for the computer’s application program and its application.
Data validation has been recognized as an essential part of any data management operation. Data must be verified and validated before using it to avoid inaccurate results. It is a vital part of the workflow as it allows optimal results creation.
Validation of the data's accuracy, transparency, and detail is essential to minimize any project defects. If data validation is not performed, decisions based on data can show imperfections and inaccuracy, not representing the current situation. In addition to verifying data inputs and values, it is required to validate the data model. An unstructured or correctly constructed data model causes problems using various applications and software data files. Validation rules to clean data before use helps mitigate “garbage in = garbage out” scenarios. Ensuring data integrity guarantee the legitimacy of the conclusions.