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.

Accuracy to reality

Characteristic Name:	Accuracy to reality
Dimension:	Accuracy
Description:	Data should truly reflect the real world
Granularity:	Record
Implementation Type:	Process-based approach
Characteristic Type:	Usage

Verification Metric:

The number of tasks failed or under performed due to lack of accuracy to reality

The number of complaints received due to lack of accuracy to reality

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Continuously evaluate if the existing data model is sufficient to represent the real world as required by the organisational need and do the necessary amendments to the data model if needed.	(1) A student who received a concession travel card is not eligible for a concession fare if he terminates his candidature before completion of the course. Hence the data model should have an extra attribute as "current status of candidature"
Perform regular audits on mission critical data to verify that every record has a meaningful existence in the reality which is useful for the organisation	(1) All customers existing in the customer master file actually a customer in the customer space open for the organisation. (non customers are not in the customer file) (2) "Greg Glass" is recorded as a glass work company but in fact they are opticians (3) A person's personal details taken from his educational profile may not be a correct reality for his insurance profile even though the information is
Perform regular audits on mission critical data to verify that every record has a unique existence in the reality	(1) It is difficult to find out that the professor "Andrew" is from Colombia university or from the university of Queensland
Ensure that Information available in the system is accurate in the context of a particular activity or event	(1) The driver details taken from vehicle registration may not be accurate in the case of finding the real person who drive the vehicle when an accident is caused

Validation Metric:

How mature is the process to ensure the accuracy to reality

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

Example:	Source:
if the name of a person is John, the value v = John is correct, while the value v = Jhn is incorrect	C. Batini and M, Scannapieco, “Data Quality: Concepts, Methodologies, and Techniques”, Springer, 2006.
Percent of values that are correct when compared to the actual value. For example, M=Male when the subject is Male.	P. Cykana, A. Paul, and M. Stern, “DoD Guidelines on Data Quality Management” in MIT Conference on Information Quality - IQ, 1996, pp. 154-171.
an EMPLOYEE entity (identified by the Employee-Number 314159) and the attribute Year-of-Birth. If the value of Year-of-Birth for employee 314159 is the year the employee was born, the datum is correct.	C. Fox, A. Levitin, and T. Redman, “The Notion of Data and Its Quality Dimensions” in Journal Information Processing and Management: an International Journal archive, Volume 30 Issue 1, Jan-Feb 1994, 1992, pp. 9-19.
Consider a database that contains names, addresses, phone numbers, and e- mail addresses of physicians in the state of Texas. This database is known to have a number of errors: some records are wrong, some are missing, and some are obsolete. If you compare the database to the true population of physicians, it is expected to be 85% accurate. If this database is to be used for the state of Texas to notify physicians of a new law regarding assisted suicide, it would certainly be considered poor quality. In fact, it would be dangerous to use it for that intended purpose. 24 2.1 Data Quality Definitions 25 If this database were to be used by a new surgical device manufacturer to ﬁnd potential customers, it would be considered high quality. Any such ﬁrm would be delighted to have a potential customer database that is 85% accurate. From it, they could conduct a telemarketing campaign to identify real sales leads with a completely acceptable success rate. The same database: for one use it has poor data quality, and for another it has high data quality.	J. E. Olson, “Data Quality: The Accuracy Dimension”, Morgan Kaufmann Publishers, 9 January 2003.
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:
Determines the extent to which data objects correctly represent the real-world values for which they were designed. For example, the sales orders for the Northeast region must be assigned a Northeast sales representative.	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
The data value correctly reflects the real-world condition.	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.
The data correctly reflects the Characteristics of a Real-World Object or Event being described. Accuracy and Precision represent the highest degree of inherent Information Quality possible.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
Is the information precise enough and close enough to reality?	EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.
1) Each identifiable data unit maps to the correct real-world phenomenon. 2) Non-identifying (i.e. non-key) attribute values in an identifiable data unit match the property values for the represented real-world phenomenon. 3) Each identifiable data unit represents at least one specific real-world phenomenon. 4) Each identifiable data unit represents at most one specific real-world phenomenon.	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.
1) The degree to which an information object correctly represents another information object, process, or phenomenon in the context of a particular activity or culture. 2) Closeness of agreement between a property value and the true value (value that characterizes a characteristic perfectly defined in the conditions that exists when the characteristic is considered. 3) The extent to which the correctness of information is verifiable or provable in the context of a particular activity.	STVILIA, B., GASSER, L., TWIDALE, M. B. & SMITH, L. C. 2007. A framework for information quality assessment. Journal of the American Society for Information Science and Technology, 58, 1720-1733.