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.

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:

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.