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 maintenance

Characteristic Name:	Data maintenance
Dimension:	Availability and Accessability
Description:	Data should be accessible to perform necessary updates and maintenance operations in it’s entirely
Granularity:	Record
Implementation Type:	Process-based approach
Characteristic Type:	Usage

Verification Metric:

The number of tasks failed or under performed due to lack of data maintenance

The number of complaints received due to lack of continuity in data access

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Technological changes in the infrastructure/system should be handled in such a way that they should not make data inaccessible	(1) Sales order is created once a customer signs a contract. Then it is updated in three instances 1)Delivery date and shipment date is updated once the production plan is created. 2) Actual quantity is updated once the manufacturing is complete 3) Total cost is updated once the freight changes are incurred. A sales order is achieved after one years from delivery.
A maintenance policy for mission critical data should be developed and implemented to handle on going systematic updates (Create, read, update, delete, archive and cleanse)	(1) Customer data : Created when a customer enters into a contract, updated once the customer details change or contact change, archived once the contact end
When multiple versions of the same data is available through different datasets\databases create a master record and make it available across the systems	(1) Master data management
Leverage application and storage technology in such a way that the maintenance policies can be applied on data	(1)Addresses which were not updated during the last 24 months are prompted for validations
Create a responsibility structure/Authorisation structure and a communication structure to manage the process of information generation maintenance and utilisation	(1) It is the responsibility of the work study team to provide SMV (standard minute values) for a garment. (2) Approved SMVs should be sent to the planning department for planning purposes.

Validation Metric:

How mature is the data maintenance process

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

Example:	Source:
minutes of a meeting will be produced in draft form and reviewed by the members of the committee before being approved. Once this process of creation is ﬁnished the record must be ﬁxed and must not be susceptible to change. If a record is changed or manipulated in some way, it no longer provides evidence of the transaction it originally documented. For example, if someone alters the minutes of a meeting after they have been approved, the minutes can no longer be considered an accurate record of the meeting. This is another issue that becomes more important in an electronic context.	K. Smith, “Public Sector Records Management: A Practical Guide”, Ashgate, 2007.

Example:

Source:

minutes of a meeting will be produced in draft form and reviewed by the members of the committee before being approved. Once this process of creation is ﬁnished the record must be ﬁxed and must not be susceptible to change. If a record is changed or manipulated in some way, it no longer provides evidence of the transaction it originally documented. For example, if someone alters the minutes of a meeting after they have been approved, the minutes can no longer be considered an accurate record of the meeting. This is another issue that becomes more important in an electronic context.

K. Smith, “Public Sector Records Management: A Practical Guide”, Ashgate, 2007.

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

Definition:	Source:
A measure of the degree to which data can be accessed and used and the degree to which data can be updated, maintained, and managed.	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
Can all of the information be organized and updated on an on-going basis?	EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.