Data punctuality

Characteristic Name:	Data punctuality
Dimension:	Availability and Accessability
Description:	Data should be available at the time of its intended use
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 data punctuality

The number of complaints received due to lack of data punctuality

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Standardise the timelines for the availability of information for a particular task	(1) Investment product pricing data is often provided by third-party vendors. As the success of the business depends on accessibility to that pricing data, service levels specifying how quickly the data must be provided are defined and compliance with those timeliness constraints.
Create efficient processes for information delivery by removing the bottlenecks in information flow	(1) Billing details of a patient is gathered two hours before discharging the patient

Validation Metric:

How mature is the process of ensuring data punctuality

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

Example:	Source:
1) For example, the best and easiest method to obtain demographic information may be to obtain it from an existing system. Another method may be to assign data collection by the expertise of each team member. For example, the admission staff collects demographic data, the nursing staff collects symptoms, and the HIM staff assigns codes. Team members should be assigned accordingly. 2) For example, patient census is needed daily to provide sufficient day-to-day operations staffing, such as nursing and food service. How- ever, annual or monthly patient census data are needed for the facilityís strategic planning.	B. Cassidy, et al., “Practice Brief: Data Quality Management Model” in Journal of AHIMA, 1998, 69(6).

Example:

Source:

1) For example, the best and easiest method to obtain demographic information may be to obtain it from an existing system. Another method may be to assign data collection by the expertise of each team member. For example, the admission staff collects demographic data, the nursing staff collects symptoms, and the HIM staff assigns codes. Team members should be assigned accordingly.

2) For example, patient census is needed daily to provide sufficient day-to-day operations staffing, such as nursing and food service. How- ever, annual or monthly patient census data are needed for the facilityís strategic planning.

B. Cassidy, et al., “Practice Brief: Data Quality Management Model” in Journal of AHIMA, 1998, 69(6).

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

Definition:	Source:
1) The characteristic of getting or having the Information when needed by a process or Knowledge Worker. 2) The Characteristic of the Information being accessible when it is needed.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
Is the information processed and delivered rapidly without delays?	EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.
Timeliness refers to the time expectation for accessibility and availability of information. Timeliness can be measured as the time between when information is expected and when it is readily available for use. For example, in the financial industry, investment product pricing data is often provided by third-party vendors. As the success of the business depends on accessibility to that pricing data, service levels specifying how quickly the data must be provided can be defined and compliance with those timeliness constraints can be measured.	LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation.
Timeliness reflects the length of time between availability and the event or phenomenon described. Punctuality refers to the time lag between the release date of data and the target date when it should have been delivered.	LYON, M. 2008. Assessing Data Quality , Monetary and Financial Statistics. Bank of England. http://www.bankofengland.co.uk/ statistics/Documents/ms/articles/art1mar08.pdf.

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.