Business Rules are commonly implemented in software, or by using Document Templates for Data Entry. Some common simple Business Rule types are:
- Definitional Conformance: Confirm that the same understanding of data definitions is implemented and used properly in processes across the organization. Confirmation includes algorithmic agreement on calculated fields, including any time, or local constraints, and roll-up and status interdependence rules.
- Value Presence and Record Completeness: Rules defining the conditions under which missing values are acceptable or unacceptable.
- Format Compliance: One or more patterns specify values assigned to a data element, such as standards for formatting telephone numbers.
- Value Domain Membership: Specify that a data element’s assigned value is included in those enumerated in a defined data value domain, such as 2-Character United States Postal Codes for a STATE field.
- Range Conformance: A data element assigned value must be within a defined numeric, lexicographic, or time range, such as greater than 0 and less than 100 for a numeric range.
- Mapping Conformance: Indicating that the value assigned to a data element must correspond to one selected from a value domain that maps to other equivalent corresponding value domain(s). The STATE data domain again provides a good example, since State values may be represented using different value domains (USPS Postal codes, FIPS 2-digit codes, full names), and these types of rules validate that ‘AL’ and ‘01’ both map to ‘Alabama.’
- Consistency Rules: Conditional assertions that refer to maintaining a relationship between two (or more) attributes based on the actual values of those attributes. For example, address validation where postal codes correspond to particular States or Provinces.
- Accuracy Verification: Compare a data value against a corresponding value in a system of record or other verified source (e.g., marketing data purchased from a vendor) to verify that the values match.
- Uniqueness Verification: Rules that specify which entities must have a unique representation and whether one and only one record exists for each represented real world object.
- Timeliness Validation: Rules that indicate the characteristics associated with expectations for accessibility and availability of data.
Other types of rules may involve aggregating functions applied to sets of data instances. Examples of aggregation checks include:
- Validate Reasonableness of the number of records in a file: This requires keeping statistics over time to generate trends.
- Validate reasonableness of an average amount calculated from a set of transactions: This requires establishing thresholds for comparison, and may be based on statistics over time.
- Validate the expected variance in the count of transactions over a specified time-frame: This requires keeping statistics over time and using them to establish thresholds.
