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.

Business rules compliance

Characteristic Name:	Business rules compliance
Dimension:	Validity
Description:	Data should comply with business rules
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:
Identify data related business rules separately (business rules that determines the value of data elements and business rules that get executed depending on the values of data elements) and organise them into a separate executable data rules engine	(1) The system maintains price routines to handle price related data Element :Discount rate A price routine (procedure) can be maintained to calculate the discount rate considering the rules R1:All registered customers get a discount of 6% R2:All gold customers get a discount of 12% R3:All purchases greater than $500 get a discount of 5%
Implement a stewardship structure for business rules (parallel to stewardship structure for data) and manage the changes to the rules properly	(1) Sales director is responsible for discounts and his approval is needed to change a discount rate. Only the sales manager can change the rules related to discounts.
Maintain an error log to identify the problems resulted in the data rules repository where the problematic data records can be identified precisely	(1) Rules engines
Continuously monitor the root causes for the errors recorded in the log and take preventive actions by amending the rules, fixing the technical defects in the system etc.	(1) Some trip data is missing for a particular journey in the go card system and as a result an unacceptable journey duration was resulted. New rules were implemented to process such data using a different criteria

Validation Metric:

How mature is the creation and implementation of the data related business rules

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

Example:	Source:
One common example in education is the student school of record. While most students do not change schools during an academic year, many do, especially in urban settings. Thus, the school at which students are tested may not be the school at which they received most of their instruction. Because school-level student achievement measures become increasingly invalid as the number of mobile students increases, many districts will hold schools accountable only for those students who were enrolled for a full academic year. In this case, student achievement measures for a given school lose validity as the percentage of mobile students increases.	J. G. Watson, S. B. Kraemer, and C. A. Thorn, “Data Quality Essentials. Guide to Implementation: Resources for Applied Practice”, August 2009.

Example:

Source:

One common example in education is the student school of record. While most students do not change schools during an academic year, many do, especially in urban settings. Thus, the school at which students are tested may not be the school at which they received most of their instruction. Because school-level student achievement measures become increasingly invalid as the number of mobile students increases, many districts will hold schools accountable only for those students who were enrolled for a full academic year. In this case, student achievement measures for a given school lose validity as the percentage of mobile students increases.

J. G. Watson, S. B. Kraemer, and C. A. Thorn, “Data Quality Essentials. Guide to Implementation: Resources for Applied Practice”, August 2009.

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

Definition:	Source:
Determines the extent to which data is not missing important relationship linkages. For example, the launch date for a new product must be valid and must be the first week of any quarter, since all new products are launched in the first week of each quarter.	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
1) Data values conform to the Specified Business Rules. 2) A derived or calculated data value is Produced Correctly according to a specified Calculation Formula or set of Derivation Rules.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.

Definition:

Source:

Determines the extent to which data is not missing important relationship linkages. For example, the launch date for a new product must be valid and must be the first week of any quarter, since all new products are launched in the first week of each quarter.

D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.

1) Data values conform to the Specified Business Rules.

2) A derived or calculated data value is Produced Correctly according to a specified Calculation Formula or set of Derivation Rules.

ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.