Availability and Accessability |
Currency |
Reliability and Credibility |
Usability and Interpretability |
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: |
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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: |
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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 |
Availability and Accessability |
Currency |
Reliability and Credibility |
Usability and Interpretability |
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