Understandability

Characteristic Name:	Understandability
Dimension:	Usability and Interpretability
Description:	The data is understandable
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 understandability of data

The number of complaints received due to the lack of understandability of data

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Ensure that appropriate signs/language is used to strenthen the readers understanding about the information object	(1) Poor, good, excellent is more suitable than 1, 2,3 as ratings to compare two factors
Avoid any possibility of ambiguity in understanding data with the inclusion of footnotes, legend etc.	(1) Footnote : Total price includes GST.
Provide supplements to understand the content of non-text and non-numeral information (e.g.. Images)	(1) A location in a plan can be identified by the coordinates
Ensure that data are concisely represented without being overwhelmed	(1) Focussed on one topic
Convenient and user friendly (more natural) formats are used for structured attributes like dates, time, telephone number, tax ID number, product code, and currency amounts	(1) U.S. phone number formats [+1(555)999-1234]
Appropriate fonts and styles are used to improve the clarity of the content	(1) Headings are marked in bold letters, Totals figures are are marked with bold numbers

Validation Metric:

How mature is the process to maintain the understandability of data

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

Example:	Source:
a Social Security number must consist of nine numeric digits. If this is your only deﬁnition, you will ﬁnd that all values that are blank, contain characters other than numeric or contain less than or more than nine digits. However, you can go further in your deﬁnition. The government employs a scheme of assigning numbers that allows you to examine the value in more detail to determine if it is valid or not. Using the larger rule has the potential for ﬁnding more inaccurate values.	J. E. Olson, “Data Quality: The Accuracy Dimension”, Morgan Kaufmann Publishers, 9 January 2003.

Example:

Source:

a Social Security number must consist of nine numeric digits. If this is your only deﬁnition, you will ﬁnd that all values that are blank, contain characters other than numeric or contain less than or more than nine digits. However, you can go further in your deﬁnition. The government employs a scheme of assigning numbers that allows you to examine the value in more detail to determine if it is valid or not. Using the larger rule has the potential for ﬁnding more inaccurate values.

J. E. Olson, “Data Quality: The Accuracy Dimension”, Morgan Kaufmann Publishers, 9 January 2003.

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

Definition:	Source:
The data element is used only for its intended purpose, that is, the degree to which the data characteristics are well understood and correctly utilized.	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.
1) Periodic Reports, such as Financial Statements, Annual Reports, and Policy and Procedure Manuals should have a standard format with a style sheet that presents the information in a consistent and easily read and understood format. 2) The Characteristic in which Information is presented in a way that clearly communicates the truth of the data. Information is presented with clear labels, footnotes, and/or other explanatory notes, with references or links to definitions or documentation the clearly communicates the meaning and any anomalies in the Information.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
Usability of data refers to the extent to which data can be accessed and understood.	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.
A good presentation provides the user with everything required for the correct interpretation of information. When there is any possibility of ambiguity, a key or legend should be included.	LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.
Is the information understandable or comprehensible to the target group?	LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation.
1) The extent to which the content of an object is focused on one topic. 2) The extent of cognitive complexity of an information object measured by some index or indices. 3) 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 compactly represented without being overwhelmed. 2) Data are clear without ambiguity and easily comprehended.	WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33.

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.