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 awareness
| Characteristic Name: | Data awareness |
| Dimension: | Availability and Accessability |
| Description: | Data users should be aware of all available data and its location |
| Granularity: | Information object |
| Implementation Type: | Process-based approach |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due to lack of data awareness |
| The number of complaints received due to lack of continuity in data awareness |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Educate users on the data landscape of the organisation and how to access the information when needed. | (1) Training programs are conducted to educate on using the systems and retrieve data when necessary. |
| Provide appropriate searching tools, manuals to find the required information. | (1) Data catalogs (2)Data Searching facility provided in application programs |
| Educate users on how data is collected and what are the procedures used in data collection so that they can decide on the appropriateness of the data for use. | (1) Opinion pole data during festival season is not appropriate to analyse drinking habits. |
Validation Metric:
| How mature is the process of creating the awareness of data |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| For example, the best and easiest method to obtain demographic information may be to obtain it from an existing system. Another method may be to assign data collection by the expertise of each team member. For example, the admission staff collects demographic data, the nursing staff collects symptoms, and the HIM staff assigns codes. Team members should be assigned accordingly. | B. Cassidy, et al., “Practice Brief: Data Quality Management Model” in Journal of AHIMA, 1998, 69(6). |
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| Accessibility of data refers to how easily it can be accessed; the awareness of data users of what data is being collected and knowing where it is located. | HIQA 2011. International Review of Data Quality Health Information and Quality Authority (HIQA), Ireland. http://www.hiqa.ie/press-release/2011-04-28-international-review-data-quality. |