What are the 3 types of data validation?

Anton Knight
Anton KnightAnswered

Data validation is the method of ensuring high-quality, accurate data. Guaranteeing the plausibility of data input and storage is accomplished by performing numerous checks into the program or report.

  • In automated systems, data entry validation is performed with little or no human oversight.

It is vital that the data entered into the system is accurate and satisfies the quality criteria requested. If the data is not recorded correctly, it will be of little value and may cause larger problems with reporting in the future. Even when input properly, unstructured data incurs expenses for cleansing, converting, and storing.

Types of Validation for Data

Before saving data in a database, the majority of data validation strategies will execute one or more of these tests to guarantee that the data is accurate. Here are the common ones:

  • Data Type Checking. This verifies that the entered data has the appropriate data type. For instance, a field may only take numeric values. If this is the case, the system should reject any data including additional characters such as capitals or special symbols.
  • Code Check. This verifies that a field’s value is picked from a legitimate set of options or that it adheres to certain formatting requirements. For instance, it is easy to verify the validity of a postal code by comparing it to a list of valid codes. The same principle may be extended to other things.
  • Range Check. This determines whether or not supplied data falls within a specified range. Values outside of this range are invalid.
  • Format Review. Many data types adhere to a set format. Date columns with a set storage format are a popular use-case (a data validation technique that ensures dates are in the correct format contributes to data and temporal consistency).
  • Verify Consistency. A consistency check is a logical check that verifies if the data has been input consistently. Checking if a package’s arrival date is later than its shipment date is a good example of this.

Uniqueness Check. Identification numbers and email addresses, for example, are data that can never be duplicated. These fields should typically contain unique items in a database. A uniqueness check guarantees that an item is not put into a database numerous times.

Testing. CI/CD. Monitoring.

Because ML systems are more fragile than you think. All based on our open-source core.

Our GithubInstall Open SourceBook a Demo

Subscribe to Our Newsletter

Do you want to stay informed? Keep up-to-date with industry news, the latest trends in MLOps, and observability of ML systems.