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.

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.