Redundancy

Characteristic Name:	Redundancy
Dimension:	Consistency
Description:	The data is recorded in exactly one place
Granularity:	Record
Implementation Type:	Rule-based approach
Characteristic Type:	Declarative

Verification Metric:

The volume of redundant data as a percentage to total data

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Maintain the database schema eliminating the causes for redundancies of entities and attributes	(1) All customers are in customer table
Ensure that there are no redundant records across distributed databases	(1) Organisation has different customer bases maintained in different databases. But one customer is available only in one database
Ensure that same entity is not originally captured more than once in the systems	(1) Medical Insurance system refers employee bank details from the payroll.
Ensure that there are no temporary table backups are available in the database	(1) Created a backup for employees as employee_temp for a specific purpose and it is still in the database

Validation Metric:

How mature is the creation and implementation of the DQ rules to eliminate the occurrence of redundant data

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.

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

Definition:	Source:
A measure of unwanted duplication existing within or across systems for a particular field, record, or data set.	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
There is only one record in a given data store that represents a Single Real-World Object or Event.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
Determines the extent to which the columns are not repeated.	G. GATLING, C. B., R. CHAMPLIN, H. STEFANI, G. WEIGEL 2007. Enterprise Information Management with SAP, Boston, Galileo Press Inc.