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 |
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). |
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. |
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 |
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. |