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 freshness
| Characteristic Name: | Data freshness |
| Dimension: | Currency |
| Description: | Data which is subjected to changes over the time should be fresh and up-to-date with respect to its intended use. |
| Granularity: | Element |
| Implementation Type: | Process-based approach |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due to lack of data freshness |
| The number of complaints received due to lack of data freshness |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Identify the natural factors which creates a particular data obsolete | (1) A seasonal change may impact the customer's food preferences. (2) Customers who are students may change their addresses frequently. |
| Considering the above factors plan for data refreshing activities by specify the frequency of refreshing the data elements and adhere to the plan. | (1) Customer contact information should be refreshed annually. |
| Identify the master data that may change over the time but may be used in longitudinal analysis. | (1) Name of customer in 2001 is ABC (PLC) Ltd, after a merger in 2006 its name is XYZ (PLC). This customer is an ongoing customer in the customer master file |
| For such master data maintain longitudinal versions with time a stamp in such a way they can be linked in longitudinal analysis | (1) 2001-2005: ABC (PLC) (2) 2006-20012: XYZ (PLC) |
Validation Metric:
| How mature is the process for ensuring data freshness |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| let us consider two databases, say A and B, that contain the same data. If at time t a user updates data in database A and another user reads the same data from database B at time t' (t < t' ), the latter will read incorrect data. If t and f are included within the time interval between two subsequent data realignments | C. Cappiello, C. Francalanci, and B. Pernici, “Time-Related Factors of Data Quality in Multichannel Information System” in Journal of Management Information Systems, Vol. 20, No. 3, M.E. Sharpe, Inc., 2004, pp.71-91. |
| currency indicates how stale is the account balance presented to the user with respect to the real balance at the bank database. | V. Peralta, “Data Freshness and Data Accuracy: A State of The Art”, Instituto de Computacion, Facultad de Ingenieria, Universidad de la Republica, Uruguay, Tech. Rep. TR0613, 2006. |
| Consider an air traffic control center which receives data from several controller stations. To regulate air traffic, the traffic control center has to cope with uncertain data.Thus, the decision process must balance the delaying receiving more accurate data of airplane positions and the critical period of time in which an“effective” decision must be made to regulate traffic; | B. Pernici, “Advanced Information Systems Engineering” in proc. The 22nd International Conference, CAiSE, Hammamet, Tunisia, June 2010. |
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| A measure of the rate of negative change to the data. | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
| Is the information upto-date and not obsolete? | EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer. |
| Data is accurate if it is up to date – anti example: “Current president of the USA: Bill Clinton”. | KIMBALL, R. & CASERTA, J. 2004. The data warehouse ETL toolkit: practical techniques for extracting. Cleaning, Conforming, and Delivering, Digitized Format, originally published. |
| Currency refers to the degree to which information is current with the world that it models. Currency can measure how up to date information is and whether is it correct despite possible time-related changes. Timeliness refers to the time. | LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub. |
| Currency refers to the degree to which information is current with the world that it models. Currency can measure how “up-to-date” information is, and whether it is correct despite possible time-related changes. Data currency may be measured as a function of the expected frequency rate at which different data elements are expected to be refreshed, as well as verifying that the data is up to date. For example, one might assert that the contact information for each customer must be current, indicating a requirement to maintain the most recent values associated with the individual’s contact data. | LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation. |
| A datum value is up-to-date if it is correct in spite of a possible discrepancy caused by time related change to the correct values; a datum is outdate at time t if it is incorrect at t but was correct at some time preceding t. currency refers to a degree to which a datum in question is up-to-date. | REDMAN, T. C. 1997. Data quality for the information age, Artech House, Inc. |