Validation Rules

22 days ago
2 min read

Validation rules in this sense are the constraints and criteria that define acceptable values, formats, or relations of data inputs or outputs of a software system. Validation rules help to ensure the integrity, consistency, and correctness of data within the system by rejecting or flagging the data that is not conformity with the set requirements.

Validation rules can be applied in various levels, namely:

  1. Field-level validation: Specifying the kind of acceptable data, format, range, or pattern of individual data fields, such as requiring a data field to have a valid email address or positive numeric value.
  2. Record-level validation: Verification of required fields, uniqueness constraints, or cross-field dependencies of a single record or entity, such as the need to assure a customer record has a valid name, address, and phone number.
  3. Business-level validation: Implementation of complex business rules or workflows that span across multiple records, entities, or systems, such as verification that a purchase order total matches the sum of the amounts of its line items or that an employee has sufficient vacation balance before the approval of a time-off request.

These aspects should be considered when defining validation rules:

  1. Data types: Specify the expected data types for each field, such as string, integer, float, boolean, or date, including how type mismatches or conversions are handled.
  2. Formats and patterns: Regular expressions, masks, or predefined templates define the required formats or patterns for data fields, such as "XXX-XX-XXXX" for a Social Security number or "MM/DD/YYYY" for a date.
  3. Ranges and Limits: Define acceptable ranges or limits for numeric or date fields - min and max values, future or past dates, or intervals.
  4. Required and optional fields: The fields that are mandatory and optional should be indicated, and the presence or absence of values enforced accordingly.
  5. Unique constraints: Specify the uniqueness of a field or a combination of fields. That is, the unique constraint will enforce that a username, an email, a product code, among others, cannot be duplicated in the system.
  6. Cross field dependencies: The relations or dependencies between diverse fields or entities, for example, the end date should be after the start date, or the shipping address should be equal to the billing address.
  7. Error handling and messaging: Define proper error codes, messages, and actions for each validation rule to give clear and informative feedback to users or downstream systems.

The following best practices can be adopted to effectively implement validation rules:

  1. Centralize and reuse: Define and manage validation rules in one place or library to ensure that the treatment and use thereof throughout a system is consistent.
  2. Parameterize and Configure: Enable validation rules to be parameterized or configured based on diverse contexts, user roles, or business requirements to allow flexibility and adaptability.
  3. Test and validate the validation rules by the most rigorous manner of using a comprehensive set of test cases, including edge cases and boundary conditions, to ensure that the rules are accurate, complete, and performant.
  4. Document and communicate: The validation rules should be clear and complete in terms of aims, criteria, and error conditions; communicate this information to the concerned parties- developers, testers, and users.
  5. Monitor and Audit: Mechanisms should be put in place for the monitoring and auditing that can be done to track the usage, performance, and effectiveness of the validation rules over time. The identification of the issues and their bottleneck in the areas for opportunity improvement is achieved.

The software system will enforce data correctness, avoid inconsistent data and errors, and support steady and reliable functioning of business processes with well-defined validation rules. Validation rules represent an important aspect of data governance and need to be covered from requirements gathering through software development and maintenance.

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