Validating data

Without adequate data validation, bad indexes or data can be saved in the target repository. Export processes might fail because of a bad data type.

The purpose of validation is to determine whether captured data conforms to specified business rules. For example:
  • Does an expense lie within permitted limits?
  • Are dates valid and within a permitted range?
  • Is the total cost calculated correctly?
  • Does the vendor information match the information that is stored in a database of approved vendors?
  • Does a field value match one of a set of permitted values?
To check whether an expense lies within permitted limits, you might create a rule that does the following validation:
  • Ensures that the expense field contains numbers in a valid currency format
  • Determines whether the value is less than or equal to the maximum permitted limit
  • Handles exceptions for invalid currency formats and numbers that are above the permitted limit

Generally, validate every field on the form. Every data and image repository has restrictions on data it can accept for fields that you want to export to. Maximum lengths and data types are common restrictions, but there might be others. In the validation ruleset, you must make sure that you check each field to ensure that it contains the correct data and can be exported properly.

Validations are checked before a data entry operator sees the data. When a page or document is viewed by a data entry operator, all data that fails validation is flagged for the operator to review. The same validations are typically run after the data entry operator submits the page. Validating again ensures that the export to the repository is accurate and less likely fail because of an improper length or data type.

Two other factors must be considered for each field:
  • Whether you allow a data entry operator to successfully submit the form if the conditions you specify are not met
  • Whether the field can be blank