Uniqueness

Characteristic Name:	Uniqueness
Dimension:	Consistency
Description:	The data is uniquely identifiable
Granularity:	Record
Implementation Type:	Rule-based approach
Characteristic Type:	Declarative

Verification Metric:

The number of duplicate records reported 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:
Ensure that every entity(record) is unique by implementing a key in every relation	(1) Key constraint
Ensure that same entity is not recorded twice under different unique identifiers	(1) Same customer is entered under different customer ID
Ensure that unique key is not-null at any cost	(1) Employee ID which is the key of employee table is not null at any cost
In case of using bar codes standardise the bar code generation process to ensure that Bar codes are not reused	(1) UPC

Validation Metric:

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

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

Example:	Source:
A school has 120 current students and 380 former students (i.e. 500 in total) however; the Student database shows 520 different student records. This could include Fred Smith and Freddy Smith as separate records, despite there only being one student at the school named Fred Smith. This indicates a uniqueness of 500/520 x 100 = 96.2%	N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013.
duplicate vendor records with the same name and different addresses make it difficult to ensure that payment is sent to the correct address. When purchases by one company are associated with duplicate master records, the credit limit for that company can unknowingly be exceeded. This can expose the business to unnecessary credit risks.	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
on two maps of the same date. Since events have a duration, this idea can be extended to identify events that exhibit temporal overlap.	H. Veregin, “Data Quality Parameters” in P. A. Longley, M. F. Goodchild, D. J. Maguire, and D. W. Rhind (eds) Geographical Information Systems: Volume 1, Principles and Technical Issues. New York: John Wiley and Sons, 1999, pp. 177-89.
The patient’s identification details are correct and uniquely identify the patient.	P. J. Watson, “Improving Data Quality: A Guide for Developing Countries”, World Health Organization, 2003.

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

Definition:	Source:
The entity is unique — there are no duplicate values.	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.
Asserting uniqueness of the entities within a data set implies that no entity exists more than once within the data set and that there is a key that can be used to uniquely access each entity. For example, in a master product table, each product must appear once and be assigned a unique identifier that represents that product across the client applications.	LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation.
Each real-world phenomenon is either represented by at most one identifiable data unit or by multiple but consistent identifiable units or by multiple identifiable units whose inconsistencies are resolved within an acceptable time frame.	PRICE, R. J. & SHANKS, G. Empirical refinement of a semiotic information quality framework. System Sciences, 2005. HICSS'05. Proceedings of the 38th Annual Hawaii International Conference on, 2005. IEEE, 216a-216a.

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.