Objectivity
| Characteristic Name: | Objectivity |
| Dimension: | Reliability and Credibility |
| Description: | Data are unbiased and impartial |
| Granularity: | Information object |
| Implementation Type: | Process-based approach |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due to biased and partial data |
| The number of complaints received due to biased or partial data |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Identify all the factors that make a particular data/information biased for the intended use and take preventive actions to eliminate them | (1) A written questionnaire is better than a face to face interviews in getting sensitive personal data |
| Design and execute preventive actions for all possible information distortions (malfunctioning or personal biases) which may cause by information /data collectors | Perform a duel coder approach to code qualitative data. |
| Design and execute preventive actions for all possible information distortions (malfunctioning or personal biases) which may cause by information /data transmitters | (1) After a survey is performed, each participant is contacted individually by a party (other than the person who conducted the survey) and randomly verify if the participants real responses have been marked properly. |
Validation Metric:
| How mature is the process to prevent biased and partial data |
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. |
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| The degree to which Information is presented without bias, enabling the Knowledge Worker to understand the meaning and significance without misinterpretation. | ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |
| Is the information free of distortion, bias, or error? | EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer. |
| 1) Data are unbiased and impartial
2) Objectivity is the extent to which data are unbiased (unprejudiced) and impartial. |
WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33. |
Business rules compliance
| Characteristic Name: | Business rules compliance |
| Dimension: | Validity |
| Description: | Data should comply with business rules |
| Granularity: | Element |
| Implementation Type: | Rule-based approach |
| Characteristic Type: | Declarative |
Verification Metric:
| The number of tasks failed or under performed due to lack of data precision |
| The number of complaints received due to lack of data precision |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Identify data related business rules separately (business rules that determines the value of data elements and business rules that get executed depending on the values of data elements) and organise them into a separate executable data rules engine | (1) The system maintains price routines to handle price related data Element :Discount rate A price routine (procedure) can be maintained to calculate the discount rate considering the rules R1:All registered customers get a discount of 6% |
| Implement a stewardship structure for business rules (parallel to stewardship structure for data) and manage the changes to the rules properly | (1) Sales director is responsible for discounts and his approval is needed to change a discount rate. Only the sales manager can change the rules related to discounts. |
| Maintain an error log to identify the problems resulted in the data rules repository where the problematic data records can be identified precisely | (1) Rules engines |
| Continuously monitor the root causes for the errors recorded in the log and take preventive actions by amending the rules, fixing the technical defects in the system etc. | (1) Some trip data is missing for a particular journey in the go card system and as a result an unacceptable journey duration was resulted. New rules were implemented to process such data using a different criteria |
Validation Metric:
| How mature is the creation and implementation of the data related business rules |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| One common example in education is the student school of record. While most students do not change schools during an academic year, many do, especially in urban settings. Thus, the school at which students are tested may not be the school at which they received most of their instruction. Because school-level student achievement measures become increasingly invalid as the number of mobile students increases, many districts will hold schools accountable only for those students who were enrolled for a full academic year. In this case, student achievement measures for a given school lose validity as the percentage of mobile students increases. | J. G. Watson, S. B. Kraemer, and C. A. Thorn, “Data Quality Essentials. Guide to Implementation: Resources for Applied Practice”, August 2009. |
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| Determines the extent to which data is not missing important relationship linkages. For example, the launch date for a new product must be valid and must be the first week of any quarter, since all new products are launched in the first week of each quarter. | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
| 1) Data values conform to the Specified Business Rules.
2) A derived or calculated data value is Produced Correctly according to a specified Calculation Formula or set of Derivation Rules. |
ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |