Likewise, you would not want to allow negative numbers for that field. If a person is listed in your database as being 12 feet tall (about 3 meters), then you can probably assume the data is incorrect. If you are recording a person’s height, you might want to prohibit values that fall outside the expected range. (FYI, Precisely offers address validation solutions)įor instance, in some cases you might need to set limits around possible numeric values for a given field, albeit with a bit less precision than in the previous example. Data validation would perform a check against existing values in a database to ensure that they fall within valid parameters.įor a list of addresses that includes countries outside the U.S., the state/province/territory field would need to be validated against a significantly longer list of possible values, but the basic premise is the same the values entered must fit within a list or range of acceptable values. If you were to enter “ZP” or “A7” in the state field, you would in essence be invalidating the entire address, because no such state or territory exists. territories, such as Guam (“GU”) and the Northern Mariana Islands (“MP”). There are also two-character abbreviations for U.S. As you know, those abbreviations denote specific states. Certain values such as NH, ND, AK, and TX conform to the list of state abbreviations as defined by the U.S. In the United States, for example, every street address should include a distinct field for the state. In a nutshell, data validation is the process of determining whether a particular piece of information falls within the acceptable range of values for a given field. Knowing the distinction can help you to better understand the bigger picture of data quality. When you delve into the intricacies of data quality, however, these two important pieces of the puzzle are distinctly different.
In layman’s terms, data verification and data validation may sound like they are the same thing.
Location Intelligence Product DownloadsĬheck whether data falls within the acceptable range of valuesĬheck data to ensure it’s accurate and consistentĬhecking whether user-entered ZIP code can be foundĬhecking that all ZIP codes in dataset are in ZIP+4 format.For example, a Numeric value validation rule.