Several problems can occur if the postal code is not entered or entered improperly. The oversight could make it difficult to leverage the data for information and business intelligence. Practical ExampleĬonsider the example of a retailer that collects data on its stores but fails to create a proper check on the postal code. A uniqueness check ensures that an item is not entered multiple times into a database. A database should likely have unique entries on these fields. Some data like IDs or e-mail addresses are unique by nature. An example is checking if the delivery date is after the shipping date for a parcel. Consistency CheckĪ consistency check is a type of logical check that confirms the data’s been entered in a logically consistent way. A common use case is date columns that are stored in a fixed format like “YYYY-MM-DD” or “DD-MM-YYYY.” A data validation procedure that ensures dates are in the proper format helps maintain consistency across data and through time. Many data types follow a certain predefined format. Any values out of this range are invalid. A latitude value should be between -90 and 90, while a longitude value must be between -180 and 180. For example, latitude and longitude are commonly used in geographic data. Range CheckĪ range check will verify whether input data falls within a predefined range. The same concept can be applied to other items such as country codes and NAICS industry codes. For example, it is easier to verify that a postal code is valid by checking it against a list of valid codes. 2. Code CheckĪ code check ensures that a field is selected from a valid list of values or follows certain formatting rules. If this is the case, then any data containing other characters such as letters or special symbols should be rejected by the system. For example, a field might only accept numeric data. Data Type CheckĪ data type check confirms that the data entered has the correct data type. Common types of data validation checks include: 1. Most data validation procedures will perform one or more of these checks to ensure that the data is correct before storing it in the database. Unstructured data, even if entered correctly, will incur related costs for cleaning, transforming, and storage. The data will be of little use if it is not entered properly and can create bigger downstream reporting issues. Therefore, it is necessary to ensure that the data that enters the system is correct and meets the desired quality standards. In automated systems, data is entered with minimal or no human supervision. It is implemented by building several checks into a system or report to ensure the logical consistency of input and stored data. Data validation refers to the process of ensuring the accuracy and quality of data.
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