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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

GuidelinesExamplesDefinitons

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.

 

Redundancy

Characteristic Name: Redundancy
Dimension: Consistency
Description: The data is recorded in exactly one place
Granularity: Record
Implementation Type: Rule-based approach
Characteristic Type: Declarative

Verification Metric:

The volume of redundant data as a percentage to total data

GuidelinesExamplesDefinitons

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

Guidelines: Scenario:
Maintain the database schema eliminating the causes for redundancies of entities and attributes (1) All customers are in customer table
Ensure that there are no redundant records across distributed databases (1) Organisation has different customer bases maintained in different databases. But one customer is available only in one database
Ensure that same entity is not originally captured more than once in the systems (1) Medical Insurance system refers employee bank details from the payroll.
Ensure that there are no temporary table backups are available in the database (1) Created a backup for employees as employee_temp for a specific purpose and it is still in the database

Validation Metric:

How mature is the creation and implementation of the DQ rules to eliminate the occurrence of redundant data

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

Example: Source:
A school has 120 current students and 380 former students (i.e. 500 in total) however; the Student database shows 520 different student records. This could include Fred Smith and Freddy Smith as separate records, despite there only being one student at the school named Fred Smith. This indicates a uniqueness of 500/520 x 100 = 96.2% 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 unwanted duplication existing within or across systems for a particular field, record, or data set. D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
There is only one record in a given data store that represents a Single Real-World Object or Event. ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
Determines the extent to which the columns are not repeated. G. GATLING, C. B., R. CHAMPLIN, H. STEFANI, G. WEIGEL 2007. Enterprise Information Management with SAP, Boston, Galileo Press Inc.