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 reference source

Characteristic Name:	Accuracy to reference source
Dimension:	Accuracy
Description:	Data should agree with an identified source
Granularity:	Element
Implementation Type:	Process-based approach
Characteristic Type:	Usage

Verification Metric:

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

The number of complaints received due to lack of accuracy to reference sources

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Establish the source for a data attribute and maintain facilities to access the correct source.	(1) Actual Cost of raw material is taken from Supplier invoices and not from quotation. (2) Interest rates are taken from daily central bank statistics which is available in the finance system online.
Establish the data capturing points in the business process without leading to any ambiguity and enforce process level validation mechanisms to ensure the process is being followed.	(1) Personal drug utilisation data is captured at POS units at pharmacies and ALL pharmacies in the country are connected to a central system (All pharmacy data is considered). (2) In a barcode scanning system in a production system, finished products cannot be scanned into quality checked products (Finished, Quality checked are the two data capturing points here)
Implement effective techniques and efficient technological solutions (devices) in collecting data which minimise data errors and omissions in data capturing.	(1) Barcode scanning is used to enter sales of products. (2) Invoices are scanned into the system and price is automatically recognised. (3) Standard forms are used to collect patient data.
If data is collected and transferred batch wise, establish the frequencies of data transfers/uploads considering the nature of the data and business needs.	(1) All drug utilisation data collected in the pharmacies are transferred to the central system at the end of every month. (2) Production efficiency data is transferred to monitoring systems every 30 minutes
Implement an effective and efficient data transferring technology which do not cause distortions or omissions to data	(1) Data migration tools
Define and implement appropriate input validation rules to notify the data collector/operator about the erroneous values being entered, avoid erroneous values being entered into database or erroneous values are flagged for clear identification	(1) Telephone number does not accept non numeric characters
Implement flexible data capturing interfaces to accommodate important but out of the way data.	(1) A field exists to record special comments in a goods receipts note (GRN)
Implement and enforce standardised data capturing procedures/ best practices through the system in collecting data.	(1) Standard wait times are used in taking blood samples of a patient. (e.g.: one hour after meal)
Establish mitigation mechanisms to handle measurement errors and ensure that acceptable error tolerance levels are established	(1) calibrate the equipments on a routine basis
Identify barriers for data collection or barriers for data providers and take appropriate actions to remove them	(1) Maintain a log file of response failures of a web based survey and then eliminate the root causes.
Identify the practices which encourage data providers	(1) Reward survey participants
Conduct regular training programs for data capturing/entering staff and educate them on possible data capturing problems and how to overcome data entry errors depending on the context	(1) Do not restart the Scanner when it is hung up while scanning (2) Repeat a telephone number in a different pattern to validate it from the source e.g. : 045 220 371 9 , in validating repeat it as 04 52 20 37 19

Validation Metric:

How mature is the process for ensuring accuracy for reference sources

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

Example:	Source:
In this scenario, the parent, a US Citizen, applying to a European school completes the Date of Birth (D.O.B) on the application form in the US date format, MM/DD/YYYY rather than the European DD/MM/YYYY format, causing the representation of days and months to be reversed.	N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013.
, let us consider two databases, say A and B, that contain the same data. If at time t a user updates data in database A and another user reads the same data from database B at time t' (t < t' ), the latter will read incorrect data. If t and f are included within the time interval between two subsequent data realignments	C. Cappiello, C. Francalanci, and B. Pernici, “Time-Related Factors of Data Quality in Multichannel Information System” in Journal of Management Information Systems, Vol. 20, No. 3, M.E. Sharpe, Inc., 2004, pp.71-91.
Consider an air traﬃc control center which receives data from several controller stations. To regulate air traﬃc, the traﬃc control center has to cope with uncertain data.Thus, the decision process must balance the delaying receiving more accurate data of airplane positions and the critical period of time in which an“eﬀective” decision must be made to regulate traﬃc;	B. Pernici, “Advanced Information Systems Engineering” in proc. The 22nd International Conference, CAiSE, Hammamet, Tunisia, June 2010.

Example:

Source:

In this scenario, the parent, a US Citizen, applying to a European school completes the Date of Birth (D.O.B) on the application form in the US date format, MM/DD/YYYY rather than the European DD/MM/YYYY format, causing the representation of days and months to be reversed.

N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013.

, let us consider two

databases, say A and B, that contain the same data. If at time t a user updates data in database A and another user reads the same data from database B at time t' (t < t' ), the latter will read incorrect data. If t and f are included within the time interval between two subsequent data realignments

C. Cappiello, C. Francalanci, and B. Pernici, “Time-Related Factors of Data Quality in Multichannel Information System” in Journal of Management Information Systems, Vol. 20, No. 3, M.E. Sharpe, Inc., 2004, pp.71-91.

Consider an air traﬃc control center which receives data from several controller stations. To regulate air traﬃc, the traﬃc control center has to cope with uncertain data.Thus, the decision process must balance the delaying receiving more accurate data of airplane positions and the critical period of time in which an“eﬀective” decision must be made to regulate traﬃc;

B. Pernici, “Advanced Information Systems Engineering” in proc. The 22nd International Conference, CAiSE, Hammamet, Tunisia, June 2010.

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

Definition:	Source:
A measure of the correctness of the content of the data (which requires an authoritative source of reference to be identified and accessible).	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
The data agrees with an original, corroborative source record of data, such as a notarized birth certificate, document, or unaltered electronic data received from a party outside the control of the organization that is demonstrated to be a reliable source.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
1) Accuracy of data refers to how closely the data correctly captures what it was designed to capture. Verification of accuracy involves comparing the collected data to an external reference source that is known to be valid. Capturing data as close as possible to the point of activity contributes to accuracy. The need for accuracy must be balanced with the importance of the decisions that will be made based on the data and the cost and effort associated with data collection. If data accuracy is compromised in any way then this information should be made known to the data users. 2) Reliability of data refers to the extent to which data is collected consistently over time and by different organisations either manually or electronically.	HIQA 2011. International Review of Data Quality Health Information and Quality Authority (HIQA), Ireland. http://www.hiqa.ie/press-release/2011-04-28-international-review-data-quality.
Data accuracy refers to the degree with which data values agree with an identified source of correct information. There are different sources of correct information: database of record, a similar, corroborative set of data values from another table, dynamically computed values, the result of a manual workflow, or irate customers.	LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.
Data accuracy refers to the degree with which data correctly represents the “real-life” objects they are intended to model. In many cases, accuracy is measured by how the values agree with an identified source of correct information (such as reference data). There are different sources of correct information: a database of record, a similar corroborative set of data values from another table, dynamically computed values, or perhaps the result of a manual process.	LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation.
Accuracy of datum refers the nearness of the value v to some value v’ in the attribute domain, which is considered as the (or maybe only a) correct one for the entity e and the attribute a. In some cases, v’ is referred to as the standard. If the datum’s value v coincides value v’, the datum is said to be correct.	REDMAN, T. C. 1997. Data quality for the information age, Artech House, Inc.
Degree of correctness of a value when comparing with a reference one	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.
The extent to which data are correct reliable and certified free of error.	WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33.