Appropriate presentation
| Characteristic Name: | Appropriate presentation |
| Dimension: | Usability and Interpretability |
| Description: | The data presentation is aligned with its use |
| Granularity: | Information object |
| Implementation Type: | Process-based approach |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due to the lack of appropriate presentation of data |
| The number of complaints received due to the lack of appropriate presentation of data |
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Ensure that Universally accepted standard formats are used to maintain the compatibility of information across organisations and across time | (1) A patients diagnostic card generated in one hospital is compatible with another hospital. |
| Ensure that information can be aggregated or combined through the use of compatible formats | (1) Product wise monthly sales report can be generated by combining the sales reports of three subsidiaries |
| Ensure that the data presentations are familiar to the users even if the application platform is changed. | (1) A quotation created in one system is sent to another system through an EDI message and displayed in the same presentation format |
| Ensure the media of presentation is appropriate for the target group | (1) A step by step written instruction list in a documents appropriate for a software engineer. (2) A video display is appropriate for a mechanic |
| Ensure that the presentation formats are flexible to accommodate changes easily | (1) An invoice document may require additional space to mansion authorisation evidence |
Validation Metric:
| How mature is the process to maintain appropriate presentation of data |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| my birth date is December 13, 1941. If a personnel database has a BIRTH_DATE data element that expects dates in USA format, a date of 12/13/1941 would be correct. A date of 12/14/1941 would be inaccurate because it is the wrong value. A date of 13/12/1941 would be wrong because it is a European representation instead of a USA representation. | 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: |
|---|---|
| A measure of how information is presented to and collected from those who utilize it. Format and appearance support appropriate use of information. | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
| 1) The Characteristic in which formatted data is presented consistently in a standardized or consistent way across different media, such as in computer screens, reports, or manually prepared reports.
2) The Characteristic of Information being presented in the right technology Media, such as online, hardcopy report, audio, or video. 3) The degree to which Information is presented in a way Intuitive and appropriate for the task at hand. The Presentation Quality of Information will vary by the individual purposes for which it is required. Some users require concise presentation, whereas others require a complete, detailed presentation, and yet others require graphic, color, or other highlighting techniques. |
ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |
| 1) Appropriateness is the dimension we use to categorize how well the format and presentation of the data match the user needs. In our example, there is a difference between a high-level monthly sales report that is supplied to senior management and the daily product manifests that are handed to the shipping department for product packaging.
2) Flexibility in presentation describes the ability of the system to adapt to changes in both the represented information and in user requirements for presentation of information. For example, a system that display different counties; currencies may need to have the screen presentation change to allow for more significant digits for prices to be displayed when there is a steep devaluation in one county’s currency. 3) In an environment that makes use of different kinds of systems and applications, a portable interface is important so that as applications are migrated from one platform to another, the presentation of data is familiar to the users. Also, when dealing with a system designed for international use, the user of international standards as well as universally recognized icons is a sign of system designed with presentation portability in mind. |
LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub. |
| 1) Data is presented in an intelligible manner.
2) Data is presented in a manner appropriate for its use, with respect to format, precision, and units. |
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. |
| Good format, like good views, are flexible so that changes in user need and recording medium can be accommodated. | REDMAN, T. C. 1997. Data quality for the information age, Artech House, Inc. |
| Data are always presented in the same format and are compatible with the previous data. | 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 |
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. |