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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.

 

Usefulness and relevance

Characteristic Name: Usefulness and relevance
Dimension: Usability and Interpretability
Description: The data is useful and relevant for the task at hand
Granularity: Information object
Implementation Type: Process-based approach
Characteristic Type: Usage

Verification Metric:

The number of tasks failed or under performed due to the lack of usefulness and relevance of data
The number of complaints received due to the lack of usefulness and relevance of data

GuidelinesExamplesDefinitons

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

Guidelines: Scenario:
Define the content of the information object based on the user requirements (as required by the task at hand) and also considering all other compliance requirements so that the information is relevant and legitimate (1) Customer invoice should contain information for the customer to understand his liability and for the delivery person to understand the point of delivery and the tax department to verify the applicable tax amount.
Regularly monitor the changes to the internal operational environment ( business process changes etc) and find out what are the new information requirements emerge due to the changes, and provide for them by amending the information structures (1) Time stamp became an important attribute for GRNs (goods receipts notes) when Lean manufacturing started as all raw materials are expected to receive by six hours before production (GRN-record, and the time stamp -attribute)
Regularly monitor the changes in the external environment find out the new information requirements emerge due to such changes and provide for such data needs (1) Competitors' rates have become important to price the existing products during the recession period since the traditional costing method does not give a competitive price.
Regularly check with knowledge workers to find out how their operations/decisions can be performed better with new data available to them and provide for such data in the information system (1) An hourly working progress report is useful in identifying the bottlenecks in production lines and balance the lines
Monitor and measure the user satisfaction about the information provided (1) User satisfaction survey

Validation Metric:

How mature is the process to maintain usefulness and relevance of data

These are examples of how the characteristic might occur in a database.

The Definitions are examples of the characteristic that appear in the sources provided.

Definition: Source:
1) The Characteristic in which the Information is the right kind of Information that adds value to the task at hand, such as to perform a process or make a decision.

2) Knowledge Workers have all the Facts they need to perform their processes or make their decisions.

ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
1) Can the information process be adapted by the information consumer?

2)Can the information be directly applied? Is it useful?

3) Does the information provision correspond to the user’s needs and habits?

EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.
Relevance of data refers to the extent to which the data meets the needs of users. Information needs may change and is important that reviews take place to ensure data collected is still relevant for decision makers. 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.
Relevance is the degree to which statistics meet current and potential users’ needs. It refers to whether all statistics that are needed are produced and the extent to which concepts used (definitions, classifications etc.) LYON, M. 2008. Assessing Data Quality ,
Monetary and Financial Statistics.
Bank of England. http://www.bankofengland.co.uk/
statistics/Documents/ms/articles/art1mar08.pdf.
The data includes all of the types of information important for its use. 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.
1) Intrinsic: The extent to which the information is new or informative in the context of a particular activity or community.

2) Relational Contextual:The amount of information contained in an information object. At the content level, it is measured as a ratio of the size of the informative content (measured in word terms that are stemmed and stopped) to the overall size of an information object. At the schema number of elements in the object level it is measured as a ratio of the number of unique elements over the total.

3) The extent to which information is applicable in a given activity.

4) The extent to which the model or schema and content of an information object are expressed by conventional, typified terms and forms according to some general-purpose reference source.

STVILIA, B., GASSER, L., TWIDALE, M. B. & SMITH, L. C. 2007. A framework for information quality assessment. Journal of the American Society for Information Science and Technology, 58, 1720-1733.
1) Data are applicable and useful for the task at hand.

2) The quantity or volume of available data is appropriate.

3) Data are of sufficient depth, breath and scope for the task at hand.

WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33.