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What is the difference between data integrity and data validity?

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Pure integrity of data refers to the property which determines that data, once stored, has not been altered in an unauthorised way -- either by a person, or by the malfunctioning of hardware.

An example is used to illustrate the point. Suppose your name is stored on some computer somewhere as "Harry Hacker", and either by the hand of a hacker, or a disgruntled employee, your name is changed to "Harry Hawker"; data integrity has been compromised.

To answer the second portion of the question, we merely have to consider if "Harry Hacker" would really be a legitimate name for someone to have. Data validity thus has to do with determining if the data itself is valid -- context here plays a critical role. For example, one would not expect a phone number to contain the characters #, @, or %. It can be said with reasonable safety that a such a phone number is invalid (thus no data validity).

In some instances it is possible for data validity and integrity to fail on the same piece of data. For example, because of a hard disk crash, a person's phone number is changed in such a way that it now contains the character '%'. Thus the integrity has been compromised, and the validity as well.

Important data stored in the database include the catalog pricing shipping tables tax tables order database and customer information. Testing must verify the correctness of the stored data. Therefore testing should be performed on a regular basis because data changes over time. Examples of data integrity tests:

* Test the creation modification and deletion of data in tables as specified in the functionality.

* Test to make sure that sets of radio buttons represent a fixed set of values. Check what happens when a blank value is retrieved from the database.

* Test that when a particular set of data is saved to the database each value gets saved fully. In other words the truncation of strings and rounding of numeric value does not occur.

* Test whether default values are saved in the database if the user input is not specified.

* Test the compatibility with old data. In addition old hardware versions of the operating system and interfaces with other software need to be tested. Data Validity Errors caused due to incorrect data entry called data validity errors are probably the most common data-related errors. These errors are also the most difficult to detect in the system. These errors are typically caused when a large volume of data is entered in a short time frame. For example $67 can be entered as $76 by mistake. The data entered is therefore invalid. You can reduce data validity errors. Use the data validation rules in the data fields. For example the date field in a database uses the MM/DD/YYYY format. A developer can incorporate a data validation rule such that MM does not exceed 12 DD does not exceed 31.In many cases simple field validation rules are unable to detect data validity errors. Here queries can be used to validate data fields. For example a query can be written to compare the sum of the numbers in the database data field with the original sum of numbers from the source. A difference between the figures indicates an error in at least one data element.
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