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.

Format consistency

Characteristic Name:	Format consistency
Dimension:	Consistency
Description:	Data formats are consistently used
Granularity:	Element
Implementation Type:	Rule-based approach
Characteristic Type:	Declarative

Verification Metric:

The number of inconsistent data formats reported in an attribute 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:
Maintain consistent formats for data values across different data bases and different tables in the same database.	(1) Telephone number : Country code/Area code/number (2) Address : House number, Street, Suburb, Sate, Country
Maintain structural similarity or compatibility of entities and attributes across systems (databases/data sets) and across time.	(1) Customer record has the same structure in all systems which it is being used.
Maintain consistent and compatible encoding /decoding standards across different applications.	(1) ASCII, UTF-8, XML

Validation Metric:

How mature is the creation and implementation of the DQ rules to maintain format consistency

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

Example:	Source:
1) Each class in a UK secondary school is allocated a class identifier; this consists of the 3 initials of the teacher plus a two digit year group number of the class. It is declared as AAA99 (3 Alpha characters and two numeric characters). 2) A new year 9 teacher, Sally Hearn (without a middle name) is appointed therefore there are only two initials. A decision must be made as to how to represent two initials or the rule will fail and the database will reject the class identifier of “SH09”. It is decided that an additional character “Z” will be added to pad the letters to 3: “SZH09”, however this could break the accuracy rule. A better solution would be to amend the database to accept 2 or 3 initials and 1 or 2 numbers. 3) In this scenario, the parent, a US Citizen, applying to a European school completes the Date of Birth (D.O.B) on the application form in the US date format, MM/DD/YYYY rather than the European DD/MM/YYYY format, causing the representation of days and months to be reversed.	N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013.
if a data element is used to store the color of a person’s eyes, a value of TRUCK is invalid. A value of BROWN for my eye color would be valid but inaccurate, in that my real eye color is blue.	J. E. Olson, “Data Quality: The Accuracy Dimension”, Morgan Kaufmann Publishers, 9 January 2003.

Example:

Source:

1) Each class in a UK secondary school is allocated a class identifier; this consists of the 3 initials of the teacher plus a two digit year group number of the class. It is declared as AAA99 (3 Alpha characters and two numeric characters).

2) A new year 9 teacher, Sally Hearn (without a middle name) is appointed therefore there are only two initials. A decision must be made as to how to represent two initials or the rule will fail and the database will reject the class identifier of “SH09”. It is decided that an additional character “Z” will be added to pad the letters to 3: “SZH09”, however this could break the accuracy rule. A better solution would be to amend the database to accept 2 or 3 initials and 1 or 2 numbers.

3) In this scenario, the parent, a US Citizen, applying to a European school completes the Date of Birth (D.O.B) on the application form in the US date format, MM/DD/YYYY rather than the European DD/MM/YYYY format, causing the representation of days and months to be reversed.

N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013.

if a data element is used to store the color of a person’s eyes, a value of TRUCK is invalid. A value of BROWN for my eye color would be valid but inaccurate, in that my real eye color is blue.

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:
A measure of the equivalence of information stored or used in various data stores, applications, and systems, and the processes for making data equivalent	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
The extent to which similar attributes or elements of an information object are consistently represented using the same structure, format, and precision.	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.