Precision

Characteristic Name:	Precision
Dimension:	Accuracy
Description:	Attribute values should be accurate as per linguistics and granularity
Granularity:	Element
Implementation Type:	Rule-based approach
Characteristic Type:	Declarative

Verification Metric:

The number of tasks failed or under performed due to lack of data precision

The number of complaints received due to lack of data precision

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Ensure the data values are correct to the right level of detail or granularity	(1) Price to the penny or weight to the nearest tenth of a gram. (2) precision of the values of an attribute according to some general-purpose IS-A ontology such as WordNet
Ensure that data is legitimate or valid according to some stable reference source like dictionary/thesaurus/code.	(1) Spellings and syntax of a description is correct as per the dictionary/thesaurus/Code (e.g. NYSIIS Code) (2) Address is consistent with global address book
Ensure that the user interfaces provide the precision required by the task	(1) if the domain is infinite (the rational numbers, for example), then no string format of finite length can represent all possible values.
Ensure the data values are lexically, syntactically and semantically correct	(1) “Germany is an African country” (semantically wrong); Book.title: ‘De la Mancha Don Quixote’ (syntactically wrong); UK’s Prime Minister: ‘Toni Blair’ (lexically wrong)

Validation Metric:

How mature is the creation and implementation of the DQ rules to maintain data precesion

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

Example:	Source:
if v = Jack,even if v = John, v is considered syntactically correct, as Jack is an admissible value in the domain of persons’ names	C. Batini and M, Scannapieco, “Data Quality: Concepts, Methodologies, and Techniques”, Springer, 2006.

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

Definition:	Source:
Data values are correct to the right level of detail or granularity, such as price to the penny or weight to the nearest tenth of a gram.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
Data is correct if it conveys a lexically, syntactically and semantically correct statement – e.g.,the following pieces of information are not correct:“Germany is an African country” (semantically wrong);Book.title: ‘De la Mancha Don Quixote’ (syntactically wrong); UK’s Prime Minister: ‘Toni Blair’ (lexically wrong).	KIMBALL, R. & CASERTA, J. 2004. The data warehouse ETL toolkit: practical techniques for extracting. Cleaning, Conforming, and Delivering, Digitized Format, originally published.
The set S should be sufficiently precise to distinguish among elements in the domain that must be distinguished by users. This dimension makes clear why icons and colors are of limited use when domains are large. But problems can and do arise for the other formats as well, because many formats are not one-to-one functions. For example, if the domain is infinite (the rational numbers, for example), then no string format of finite length can represent all possible values. The trick is to provide the precision to meet user needs.	LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.
Is the information to the point, void of unnecessary elements?	LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation.
The degree of precision of the presentation of an attribute’s value should reasonably match the degree of precision of the value being displayed. The user should be able to see any value the attributer may take and also be able to distinguish different values.	REDMAN, T. C. 1997. Data quality for the information age, Artech House, Inc.
The granularity or precision of the model or content values of an information object according to some general-purpose IS-A ontology such as WordNet.	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.

Data punctuality

Characteristic Name:	Data punctuality
Dimension:	Availability and Accessability
Description:	Data should be available at the time of its intended use
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 punctuality

The number of complaints received due to lack of data punctuality

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Standardise the timelines for the availability of information for a particular task	(1) Investment product pricing data is often provided by third-party vendors. As the success of the business depends on accessibility to that pricing data, service levels specifying how quickly the data must be provided are defined and compliance with those timeliness constraints.
Create efficient processes for information delivery by removing the bottlenecks in information flow	(1) Billing details of a patient is gathered two hours before discharging the patient

Validation Metric:

How mature is the process of ensuring data punctuality

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

Example:	Source:
1) 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. 2) For example, patient census is needed daily to provide sufficient day-to-day operations staffing, such as nursing and food service. How- ever, annual or monthly patient census data are needed for the facilityís strategic planning.	B. Cassidy, et al., “Practice Brief: Data Quality Management Model” in Journal of AHIMA, 1998, 69(6).

Example:

Source:

1) 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.

2) For example, patient census is needed daily to provide sufficient day-to-day operations staffing, such as nursing and food service. How- ever, annual or monthly patient census data are needed for the facilityís strategic planning.

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:
1) The characteristic of getting or having the Information when needed by a process or Knowledge Worker. 2) The Characteristic of the Information being accessible when it is needed.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
Is the information processed and delivered rapidly without delays?	EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.
Timeliness refers to the time expectation for accessibility and availability of information. Timeliness can be measured as the time between when information is expected and when it is readily available for use. For example, in the financial industry, investment product pricing data is often provided by third-party vendors. As the success of the business depends on accessibility to that pricing data, service levels specifying how quickly the data must be provided can be defined and compliance with those timeliness constraints can be measured.	LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation.
Timeliness reflects the length of time between availability and the event or phenomenon described. Punctuality refers to the time lag between the release date of data and the target date when it should have been delivered.	LYON, M. 2008. Assessing Data Quality , Monetary and Financial Statistics. Bank of England. http://www.bankofengland.co.uk/ statistics/Documents/ms/articles/art1mar08.pdf.