Availability and Accessability |
Currency |
Reliability and Credibility |
Usability and Interpretability |
Objectivity
Compare with other Characteristic
Characteristic Name: | Objectivity |
Definition: | Data are unbiased and impartial |
Dimension: | Reliability and Credibility |
Granularity: | Information object |
Characteristic Type: | Usage |
Implementation Form: | Process-based approach |
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 |
Validation Metric:
To what extent required capabilities and skills have been implemented to improve the data usage of a task |
BackgroundGuidelines
The original definitions given below formed the basis of the consolidated definition of the characteristic.
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. More from this source |
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. More from this source |
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. More from this source |
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. |
Availability and Accessability |
Currency |
Reliability and Credibility |
Usability and Interpretability |
Leave a Reply
Be the First to Comment!
You must be logged in to post a comment.