Business Rules in Data Quality
Business Rules are commonly implemented in software or by using Document Templates for Data Entry. Some common simple Business Rule types are:
Type of Business Rules in Data Quality
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 domains. 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 is 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 Rocords, Transcations and Time Frame
- 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.
