Objectivity

Characteristic Name:	Objectivity
Dimension:	Reliability and Credibility
Description:	Data are unbiased and impartial
Granularity:	Information object
Implementation Type:	Process-based approach
Characteristic Type:	Usage

Verification Metric:

The number of tasks failed or under performed due to biased and partial data

The number of complaints received due to biased or partial data

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Identify all the factors that make a particular data/information biased for the intended use and take preventive actions to eliminate them	(1) A written questionnaire is better than a face to face interviews in getting sensitive personal data
Design and execute preventive actions for all possible information distortions (malfunctioning or personal biases) which may cause by information /data collectors	Perform a duel coder approach to code qualitative data.
Design and execute preventive actions for all possible information distortions (malfunctioning or personal biases) which may cause by information /data transmitters	(1) After a survey is performed, each participant is contacted individually by a party (other than the person who conducted the survey) and randomly verify if the participants real responses have been marked properly.

Validation Metric:

How mature is the process to prevent biased and partial data

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

Example:	Source:
Consider an inventory database that contains part numbers, warehouse locations, quantity on hand, and other information. However, it does not contain source information (where the parts came from). If a part is supplied by multiple suppliers, once the parts are received and put on the shelf there is no indication of which supplier the parts came from. The information in the database is always accurate and current. For normal inventory transactions and deci- sion making, the database is certainly of high quality. If a supplier reports that one of their shipments contained defective parts, this database is of no help in identifying whether they have any of those parts or not. The database is of poor quality because it does not contain a relevant element of information. Without that information, the database is poor data quality for the intended use.	J. E. Olson, “Data Quality: The Accuracy Dimension”, Morgan Kaufmann Publishers, 9 January 2003.

Example:

Source:

Consider an inventory database that contains part numbers, warehouse locations, quantity on hand, and other information. However, it does not contain source information (where the parts came from). If a part is supplied by multiple suppliers, once the parts are received and put on the shelf there is no indication of which supplier the parts came from. The information in the database is always accurate and current. For normal inventory transactions and deci- sion making, the database is certainly of high quality. If a supplier reports that one of their shipments contained defective parts, this database is of no help in identifying whether they have any of those parts or not. The database is of poor quality because it does not contain a relevant element of information. Without that information, the database is poor data quality for the intended use.

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 degree to which Information is presented without bias, enabling the Knowledge Worker to understand the meaning and significance without misinterpretation.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
Is the information free of distortion, bias, or error?	EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.
1) Data are unbiased and impartial 2) Objectivity is the extent to which data are unbiased (unprejudiced) and impartial.	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:

The degree to which Information is presented without bias, enabling the Knowledge Worker to understand the meaning and significance without misinterpretation.

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

Is the information free of distortion, bias, or error?

EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.

1) Data are unbiased and impartial

2) Objectivity is the extent to which data are unbiased (unprejudiced) and impartial.

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

Data volume

Characteristic Name:	Data volume
Dimension:	Completeness
Description:	The volume of data is neither deficient nor overwhelming to perform an intended task
Granularity:	Information object
Implementation Type:	Process-based approach
Characteristic Type:	Usage

Verification Metric:

The number of tasks failed or under performed due to not meeting the right volume of data

The number of complaints received due to volume related issues

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Define the scope of data in terms of organisational coverage to perform a business activity	(1) At least 70% of the production units should submit data to calculate total production efficiency of the company
Define the scope of data in terms of activities relates to any business task	(1) Pages with more than thousand hits per day and above are considered for the analysis
Define the scope of data in terms of the population of data which is under concern	(1) At least 10% of the population of white blood cells in the culture should be collected as samples to calculate its growth
Define an appropriate amount of records in terms of lower limit and upper limit for any task	(1) At least six responses should be available to evaluate a tutor's skills and competency.

Validation Metric:

How mature is the process of defining and maintaining appropriate data volumes of data

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

Example:	Source:
At the end of the first week of the Autumn term, data analysis was performed on the ‘First Emergency Contact Telephone Number’ data item in the Contact table. There are 300 students in the school and 294 out of a potential 300 records were populated, therefore 294/300 x 100 = 98% completeness has been achieved for this data item in the Contact table.	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 the availability and comprehensiveness of data compared to the total data universe or population of interest.	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
Is the scope of information adequate? (not too much nor too little).	EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.
Degree of presence of data in a given collection.	SCANNAPIECO, M. & CATARCI, T. 2002. Data quality under a computer science perspective. Archivi & Computer, 2, 1-15.
The quantity or volume of available data is appropriate	WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33.