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 |
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. |
Continuity of data access
| Characteristic Name: | Continuity of data access |
| Dimension: | Availability and Accessability |
| Description: | The technology infrastructure should not prohibit the speed and continuity of access to the data for the users |
| Granularity: | Information object |
| Implementation Type: | Process-bases approacd |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due to the lack of continuity in data access |
| The number of complaints received due to lack of continuity in data access |
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Convenient and efficient platform should be made available to access data depending on the task at hand | (1) For a sales person, a web based interface run on a smart device is more suitable to quickly access data |
| Speed of the data retrieval should be acceptable for users working pace | (1) For an online customer care executive, speedy retrieval of information is necessary since the customer cannot be kept waiting (2) With the growth of the database reports become slower (Anti example) |
| Continuous and unobstructed connectivity should be ensured for data retrievals | (1) Connection lost while accessing reports (Anti example) |
| Proper concurrency control has been implemented | (1) Controlling access to data by locks |
| Technological changes in the infrastructure/system should be handled in such a way that they should not make data inaccessible | (1) New version of the software does not provide access to " X out orders" since the new version does not allow the function "X out" |
Validation Metric:
| How mature is the process of maintaining an infrastructure for data access |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| 1) For example, recording the age and race in medical records may be appropriate.
However, it may be illegal to collect this information in human resources departments. 2) 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. |
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) Is there a continuous and unobstructed way to get to the information?
2) Can the infrastructure match the user’s working pace? |
EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer. |
| Data is easy and quick to retrieve. | 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) availability of a data source or a system.
2) Accessibility expresses how much data are available or quickly retrievable. 3) The frequency of failures of a system, its fault tolerance. |
SCANNAPIECO, M. & CATARCI, T. 2002. Data quality under a computer science perspective. Archivi & Computer, 2, 1-15. |