Data Validation

Data validation is the process of checking the accuracy, consistency, and reliability of data before it's used or processed. It's a type of data cleansing that involves building checks into a system or report to ensure the quality of data.

Data validation is the practice of checking the integrity, accuracy and structure of data before it is used for or by one or more business operations. The results of a data validation operation can provide useful, actionable data that can then be used for data analytics or business intelligence applications, or for training machine learning models. Often, data is validated to ensure its integrity for financial accounting or regulatory compliance.

Data validation also ensures the consistency, accuracy and completeness of data, particularly if data is being moved, or migrated, between locations or if data from different sources is being merged. As data moves from one location to another, different needs for the data arise based on how the data is being used.

Data validation ensures the data is correct for specific contexts. The right type of data validation makes the data useful for an organization or for a specific application operation. Ensuring the validity and meaningfulness of the data set facilitates useful analytics for a wide variety of applications. It also prevents issues related to data inconsistencies or corruption. For example, if data is not in the right format to be consumed by a specific system, then the data can't be used easily -- or at all -- since the system might not be able to read it.

Data validation is also often related to data quality. The validation process can be used to measure data quality, which ensures a given data set is supplied with information sources that are not only of the highest quality, but also authoritative and accurate. Higher-quality, validated data removes the need for data cleansing and its associated costs, which can be very high if done later in a data-driven or data-dependent process.

Finally, data is also validated as part of many business application workflows. Examples include spell checks and rule setting for the creation of strong system, account, application and website passwords. In these types of workflows, the use of automated data validation systems eliminates the need for human intervention, thus speeding up the workflow, improving output consistency and preventing errors.