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.

Information value

Characteristic Name:	Information value
Dimension:	Usability and Interpretability
Description:	Quality information should provide a business value to the organization
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 business value delivered by the information

The number of complaints received due to the lack of business value delivered by the information

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Continuously asses the relevance and the usefulness of existing data to the organisational goals (Strategic level).	(1)What is the cost of poor quality customer data to the organisation in concern? (2) What revenue can be generated from data?
Continuously asses the usefulness of information based on the tasks at hand (Operational level)	(1) Can we predict our future market share from the existing market information?
Monitor and Measure if the intended goal of the data presentation/Interpretation is achieved	(1) Employee efficiency data is displayed in a dash board to motivate employees. The effectiveness of this display can be measured by examining the efficiency gain of each employee. (2) Has the given sales forecast for the last three years been reasonably accurate compared to actuals.

Validation Metric:

How mature is the process to maintain the business value of information

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

Example:	Source:
Consider a database containing orders from customers. A practice for handling complaints and returns is to create an “adjustment” order for backing out the original order and then writing a new order for the corrected information if applicable. This procedure assigns new order numbers to the adjustment and replacement orders. For the accounting department, this is a high-quality database. All of the numbers come out in the wash. For a business analyst trying to determine trends in growth of orders by region, this is a poor-quality database. If the business analyst assumes that each order number represents a distinct order, his analysis will be all wrong. Someone needs to explain the practice and the methods necessary to unravel the data to get to the real numbers (if that is even possible after the fact).	J. E. Olson, “Data Quality: The Accuracy Dimension”, Morgan Kaufmann Publishers, 9 January 2003.

Example:

Source:

Consider a database containing orders from customers. A practice for handling complaints and returns is to create an “adjustment” order for backing out the original order and then writing a new order for the corrected information if applicable. This procedure assigns new order numbers to the adjustment and replacement orders. For the accounting department, this is a high-quality database. All of the numbers come out in the wash. For a business analyst trying to determine trends in growth of orders by region, this is a poor-quality database. If the business analyst assumes that each order number represents a distinct order, his analysis will be all wrong. Someone needs to explain the practice and the methods necessary to unravel the data to get to the real numbers (if that is even possible after the fact).

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:
1) A measure of the degree to which data will produce the desired business transaction or outcome. 2) A measure of the perception of and confidence in the quality of the data; the importance, value, and relevance of the data to business needs.	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
As a data quality-oriented organization matures, the agreement of usage will move from a small set of “early adopters” to gradually encompass more and more of the enterprise, Ubiquity measures the degree to which different departments in an organization use shared reference data.	LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.
Data are beneficial and provide advantages for their use.	WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33.

Definition:

Source:

1) A measure of the degree to which data will produce the desired business transaction or outcome.

2) A measure of the perception of and confidence in the quality of the data; the importance, value, and relevance of the data to business needs.

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

As a data quality-oriented organization matures, the agreement of usage will move from a small set of “early adopters” to gradually encompass more and more of the enterprise, Ubiquity measures the degree to which different departments in an organization use shared reference data.

LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.

Data are beneficial and provide advantages for their use.

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