What is data validation?
Data validation is the process that maintains the accuracy, integrity, and consistency of data, checking it against predefined rules or criteria, identifying and correcting errors or inconsistencies to maintain data quality and reliability. This helps prevent erroneous information from being stored and used in decision-making processes, especially when undertaking a data migration project.
Types of data validation
Data type check
- Ensures the data entered is of the correct type. For instance, only numbers must appear in a numeric field.
Code check
- Validates data against a list of accepted values or formats, like postal codes, country codes, or industry codes.
Range check
- Verifies data falls within set limits, such as percentage allotment which has to be between 0 and 100.
Format check
- Confirms that data adheres to a specific format, like dates appearing in ‘DD-MM-YYYY’ format, for ensuring consistency.
Consistency check
- Helps maintain logical consistency, such as ensuring a delivery date is after the shipping date.
Uniqueness check
- Prevents duplicate entries for fields that require unique values, like email addresses or IDs.
Significance of data validation: backbone of data assurance and quality management
Data validation is an essential process in data assurance, ensuring that information entering systems is accurate, consistent, and reliable. It serves as the first line of defense in maintaining data quality by applying checks based on predefined rules, which in turn reduces the risk of errors impacting decision-making.
Mirrors real-world data
In test data management, data validation ensures that test datasets mirror real-world data conditions, allowing teams to identify potential issues before deployment. This approach strengthens data governance by enforcing rules for accuracy, consistency, and compliance across an organization’s data ecosystem.
Prevents inconsistent data
Data engineering teams implement validation checks in data pipelines to prevent corrupted or inconsistent data from entering core systems. Data validation helps prevent these errors from being passed through the system, which can save time and resources downstream.
In DataOps, validation is a core component of automating and monitoring data workflows. Validation techniques such as range checks, format checks, and uniqueness checks are commonly employed to maintain data quality as it flows through various stages in the pipeline.
Wrapping up
Data validation is key to maintaining data accuracy, integrity, and reliability at every stage. By applying checks for types, formats, ranges, and uniqueness, it prevents errors that could impact decisions and efficiency. When integrated with data assurance, governance, and DataOps disciplines, it reinforces a comprehensive approach to data quality, supporting reliable analytics for accurate decision-making.
