Uniqueness
| Characteristic Name: | Uniqueness |
| Dimension: | Consistency |
| Description: | The data is uniquely identifiable |
| Granularity: | Record |
| Implementation Type: | Rule-based approach |
| Characteristic Type: | Declarative |
Verification Metric:
| The number of duplicate records reported per thousand records |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Ensure that every entity(record) is unique by implementing a key in every relation | (1) Key constraint |
| Ensure that same entity is not recorded twice under different unique identifiers | (1) Same customer is entered under different customer ID |
| Ensure that unique key is not-null at any cost | (1) Employee ID which is the key of employee table is not null at any cost |
| In case of using bar codes standardise the bar code generation process to ensure that Bar codes are not reused | (1) UPC |
Validation Metric:
| How mature is the creation and implementation of the DQ rules to maintain uniqueness of data records |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| A school has 120 current students and 380 former students (i.e. 500 in total) however; the Student database shows 520 different student records. This could include Fred Smith and Freddy Smith as separate records, despite there only being one student at the school named Fred Smith. This indicates a uniqueness of 500/520 x 100 = 96.2% | N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013. |
| duplicate vendor records with the same name and different addresses make it difficult to ensure that payment is sent to the correct address. When purchases by one company are associated with duplicate master records, the credit limit for that company can unknowingly be exceeded. This can expose the business to unnecessary credit risks. | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
| on two maps of the same date. Since events have a duration, this idea can be extended to identify events that exhibit temporal overlap. | H. Veregin, “Data Quality Parameters” in P. A. Longley, M. F. Goodchild, D. J. Maguire, and D. W. Rhind (eds) Geographical Information Systems: Volume 1, Principles and Technical Issues. New York: John Wiley and Sons, 1999, pp. 177-89. |
| The patient’s identification details are correct and uniquely identify the patient. | P. J. Watson, “Improving Data Quality: A Guide for Developing Countries”, World Health Organization, 2003. |
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| The entity is unique — there are no duplicate values. | 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. |
| Asserting uniqueness of the entities within a data set implies that no entity exists more than once within the data set and that there is a key that can be used to uniquely access each entity. For example, in a master product table, each product must appear once and be assigned a unique identifier that represents that product across the client applications. | LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation. |
| Each real-world phenomenon is either represented by at most one identifiable data unit or by multiple but consistent identifiable units or by multiple identifiable units whose inconsistencies are resolved within an acceptable time frame. | PRICE, R. J. & SHANKS, G. Empirical refinement of a semiotic information quality framework. System Sciences, 2005. HICSS'05. Proceedings of the 38th Annual Hawaii International Conference on, 2005. IEEE, 216a-216a. |
Data volume
| Characteristic Name: | Data volume |
| Dimension: | Completeness |
| Description: | The volume of data is neither deficient nor overwhelming to perform an intended task |
| Granularity: | Information object |
| Implementation Type: | Process-based approach |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due to not meeting the right volume of data |
| The number of complaints received due to volume related issues |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Define the scope of data in terms of organisational coverage to perform a business activity | (1) At least 70% of the production units should submit data to calculate total production efficiency of the company |
| Define the scope of data in terms of activities relates to any business task | (1) Pages with more than thousand hits per day and above are considered for the analysis |
| Define the scope of data in terms of the population of data which is under concern | (1) At least 10% of the population of white blood cells in the culture should be collected as samples to calculate its growth |
| Define an appropriate amount of records in terms of lower limit and upper limit for any task | (1) At least six responses should be available to evaluate a tutor's skills and competency. |
Validation Metric:
| How mature is the process of defining and maintaining appropriate data volumes of data |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| At the end of the first week of the Autumn term, data analysis was performed on the ‘First Emergency Contact Telephone Number’ data item in the Contact table. There are 300 students in the school and 294 out of a potential 300 records were populated, therefore 294/300 x 100 = 98% completeness has been achieved for this data item in the Contact table. | N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013. |
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| A measure of the availability and comprehensiveness of data compared to the total data universe or population of interest. | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
| Is the scope of information adequate? (not too much nor too little). | EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer. |
| Degree of presence of data in a given collection. | SCANNAPIECO, M. & CATARCI, T. 2002. Data quality under a computer science perspective. Archivi & Computer, 2, 1-15. |
| The quantity or volume of available data is appropriate | WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33. |