The most straightforward (and arguably the most essential) rules used in data validation are rules that ensure data integrity. Types of Data Validation Validation Rules for Consistency Ensuring the integrity of data helps to ensure the legitimacy of your conclusions. Using validation rules to cleanse data before use helps to mitigate “garbage in = garbage out” scenarios. 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.īoth the structure and content of data files will dictate what exactly you can do with data. While verifying data inputs and values is important, it is also necessary to validate the data model itself. Without validating data, you run the risk of basing decisions on data with imperfections that are not accurately representative of the situation at hand.
Validating the accuracy, clarity, and details of data is necessary to mitigate any project defects. With data integration platforms that can incorporate and automate validation processes, validation can be treated as an essential ingredient to your workflow rather than an additional step. These days data validation can be a much quicker process than you might’ve thought. It may seem as if data validation is a step that slows down your pace of work, however, it is essential because it will help you create the best results possible.
While data validation is a critical step in any data workflow, it’s often skipped over.
That’s why it’s necessary to verify and validate data before it is used. If data isn’t accurate from the start, your results definitely won’t be accurate either. 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.