Data validation is an essential part of any data handling task whether you’re in the field collecting information, analyzing data, or preparing to present data to stakeholders. If data isn’t accurate from the start, your results definitely won’t be accurate either. That’s why it’s necessary to verify and validate data before it is used.
Validating the accuracy, clarity, and details of data is necessary to mitigate any project defects. Without validating data, you run the risk of basing decisions on data with imperfections that are not accurately representative of the situation at hand.
While verifying data inputs and values is important, it is also necessary to validate the data data model itself. If the data model is not structured or built correctly, you will run into issues when trying to use data files in various applications and software.
Both the structure and content of data files will dictate what exactly you can do with data. Using validation rules to cleanse data before use helps to mitigate “garbage in = garbage out” scenarios. Ensuring the integrity of data helps to ensure the legitimacy of your conclusions.