Suggest at least three specific data validation checks that might help reduce input errors.

Question

What will be an ideal response?

Answer 1

You can mention at least eight types of data validation rules. For example:
• A sequence check is used when the data must be in some predetermined sequence. If the user must enter work orders in numerical sequence, for example, then an out-of-sequence order number indicates an error, or if the user must enter transactions chronologically, then a transaction with an out-of-sequence date indicates an error.
• An existence check is used for mandatory data items. For example, if an employee record requires a Social Security number, an existence check would not allow the user to save the record until he or she enters a suitable value in the Social Security number field.
• A data type check tests to ensure that a data item fits the required data type. For example, a numeric field must have only numbers or numeric symbols, and an alphabetic field can contain only the characters A through Z (or a through z).
• A range check tests data items to verify that they fall between a specified minimum and maximum value. The daily hours worked by an employee, for ex-ample, must fall within the range of 0 to 24. When the validation check involves a minimum or a maximum value, but not both, it is called a limit check. Checking that a payment amount is greater than zero, but not specifying a maxi-mum value, is an example of a limit check.
• A reasonableness check identifies values that are questionable, but not necessarily wrong. For example, input payment values of $.05 and $5,000,000.00 both pass a simple limit check for a payment value greater than zero, and yet both values could be errors. Similarly, a daily hours worked value of 24 passes a 0 to 24 range check; however, the value seems unusual, and the system should verify it using a reasonableness check.
• A validity check is used for data items that must have certain values. For example, if an inventory system has 20 valid item classes, then any input item that does not match one of the valid classes will fail the check. Verifying that a customer number on an order matches a customer number in the customer file is another type of validity check. Because the value entered must refer to another value, that type of check also is called referential integrity, which is explained in Chapter 9, Data Design. Another validity check might verify that a new customer number does not match a number already stored in the customer master file.
• A combination check is performed on two or more fields to ensure that they are consistent or reasonable when considered together. Even though all the fields involved in a combination check might pass their individual validation checks, the combination of the field values might be inconsistent or unreasonable. For example, if an order input for 30 units of a particular item has an input discount rate applicable only for purchases of 100 or more units, then the combination is invalid; either the input order quantity or the input discount rate is incorrect.
• Batch controls are totals used to verify batch input. Batch controls might check data items such as record counts and numeric field totals. For example, before entering a batch of orders, a user might calculate the total number of orders and the sum of all the order quantities. When the batch of orders is entered, the order system also calculates the same two totals. If the system totals do not match the input totals, then a data entry error has occurred. Unlike the other validation checks, batch controls do not identify specific errors. For example, if the sum of all the order quantities does not match the batch control total, you know only that one or more orders in that batch were entered incorrectly or not input. The batch control totals often are called hash totals, because they are not meaningful numbers themselves, but are useful for comparison purposes.