Completeness of mandatory attributes

Completeness of mandatory attributes

Compare with other Characteristic

Characteristic Name: Completeness of mandatory attributes
Definition: The attributes which are mandatory for a complete representation of a real world entity must contain values and cannot be null .
Dimension: Completeness
Granularity: Element
Characteristic Type: Declarative
Implementation Form: Rule-based approach

Verification Metric:

The number of null values reported in a mandatory attribute per thousand records

Validation Metric:

To what extent required rules have been identified and implemented to maintain the declarative characteristic in concern.

BackgroundGuidelines

The original definitions given below formed the basis of the consolidated definition of the characteristic.

Definition: Source:
Domain Level: Data element is 1. Always required be populating and not defaulting; or 2. Required based on the condition of another data element. Entity Level: The required domains that comprise an entity exist and are not defaulted in aggregate. B. BYRNE, J. K., D. MCCARTY, G. SAUTER, H. SMITH, P WORCESTER 2008. The information perspective of SOA design Part 6:The value of applying the data quality analysis pattern in SOA. IBM corporation. More from this source
A given data element (fact) has a full value stored for all records that should have a value. ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. More from this source
Determined the extent to which data is not missing. For example, an order is not complete without a price and quantity. G. GATLING, C. B., R. CHAMPLIN, H. STEFANI, G. WEIGEL 2007. Enterprise Information Management with SAP, Boston, Galileo Press Inc. More from this source
Completeness refers to the expectation that certain attributes are expected to have assigned values in a data set. Completeness rules can be assigned to a data set in three levels of constraints: 1. Mandatory attributes that require a value 3. Inapplicable attributes (such as maiden name for a single male), which may not have a value.2. Optional attributes, which may have a value. LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub. More from this source
An expectation of completeness indicates that certain attributes should be assigned values in a data set. Completeness rules can be assigned to a data set in three levels of constraints:1. Mandatory attributes that require a value, 2. Optional attributes, which may have a value based on some set of conditions, and 3. Inapplicable attributes, (such as maiden name for a single male), which may not have a value. LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation. More from this source

The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation

Guidelines: Scenario:
Specify which attributes are required to maintain a meaningful representation of an entity. 1) A sales order should at least have values for order number, Quantity, Price and Total (Sales order is the record)
Specify the states of an entity where the above identified attributes become mandatory values (1)Order number quantity and total should be available as mandatory by the time order is created whereas price will become mandatory when the order is approved. (States :"Order created" "Order approved") (2) Product is retired and now has a product-last-available-date
Specify the dependencies of entities in operational context to identify the mandatory values (1)Invoice number should exist to create a gate pass
Specify default values where possible (1) Default country is Australia for those who fill the application from Australian IP addresses

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