Source quality

Characteristic Name:	Source quality
Dimension:	Reliability and Credibility
Description:	Data used is from trusted and credible sources
Granularity:	Information object
Implementation Type:	Process-based approach
Characteristic Type:	Usage

Verification Metric:

The number of tasks failed or under performed due to lack of source quality

The number of complaints received due to lack of source quality

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Asses the reputation of data sources	(1) Central Bank is the best source to get daily exchange rates
Evaluate the remedies for non-compliance of data	(1) Any remedies given by the source organisation to mitigate the losses in case if the information is of low quality
Rely on shared information sources created\recommended\used by the organisations operating in the industry	(1) In performing portfolios analysis most organisations use the risk factors produced by a central body of the economy (Central bank)

Validation Metric:

How mature is the process to maintain quality of data sources

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 source of information (1) guarantees the quality of information it provides with remedies for non-compliance; (2) documents its certification in its Information Quality Management capabilities to capture, maintain, and deliver Quality Information; (3) provides objective and verifiable measures of the Quality of Information it provides in agreed-upon Quality Characteristics; and (4) guarantees that the Information has been protected from unauthorized access or modification.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
The notion of abstracting information into a data domain implies that there are enough users of the same set of data that it makes sense to manage their own versions. The dimension of enterprise agreement of usage measures the degree to which different organizations conform to the usage of the enterprise data domain of record instead of relying on their own data set.	LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.
Reputation is the extent to which data are trusted or highly regarded in terms of their source or content.	SCANNAPIECO, M. & CATARCI, T. 2002. Data quality under a computer science perspective. Archivi & Computer, 2, 1-15.
The degree of reputation of an information object in a given community or culture.	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.
Data are trusted or highly regarded in terms of their source and content.	WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33.

Data volume

Characteristic Name:	Data volume
Dimension:	Completeness
Description:	The volume of data is neither deficient nor overwhelming to perform an intended task
Granularity:	Information object
Implementation Type:	Process-based approach
Characteristic Type:	Usage

Verification Metric:

The number of tasks failed or under performed due to not meeting the right volume of data

The number of complaints received due to volume related issues

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Define the scope of data in terms of organisational coverage to perform a business activity	(1) At least 70% of the production units should submit data to calculate total production efficiency of the company
Define the scope of data in terms of activities relates to any business task	(1) Pages with more than thousand hits per day and above are considered for the analysis
Define the scope of data in terms of the population of data which is under concern	(1) At least 10% of the population of white blood cells in the culture should be collected as samples to calculate its growth
Define an appropriate amount of records in terms of lower limit and upper limit for any task	(1) At least six responses should be available to evaluate a tutor's skills and competency.

Validation Metric:

How mature is the process of defining and maintaining appropriate data volumes of data

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

Example:	Source:
At the end of the first week of the Autumn term, data analysis was performed on the ‘First Emergency Contact Telephone Number’ data item in the Contact table. There are 300 students in the school and 294 out of a potential 300 records were populated, therefore 294/300 x 100 = 98% completeness has been achieved for this data item in the Contact table.	N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013.

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

Definition:	Source:
A measure of the availability and comprehensiveness of data compared to the total data universe or population of interest.	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
Is the scope of information adequate? (not too much nor too little).	EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.
Degree of presence of data in a given collection.	SCANNAPIECO, M. & CATARCI, T. 2002. Data quality under a computer science perspective. Archivi & Computer, 2, 1-15.
The quantity or volume of available data is appropriate	WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33.