Objectivity
The degree to which the data values are created in an unbiased manner.
- •Data collection and recording processes are free from personal bias or subjective influence.
- •More trustworthy and impartial data for decision-making and performance evaluation.
- •Fairer and more equitable outcomes when data is used for assessments or allocations.
- •Increased credibility of analyses and reports based on unbiased data.
- •Skewed or biased data leading to unfair decisions, inaccurate assessments, or flawed conclusions.
- •Erosion of trust if data is perceived as being manipulated or subjectively influenced.
- •Potential for discriminatory outcomes if biased data is used in AI models or decision-making processes.
- •Difficulty in achieving consensus or buy-in for decisions based on questionable data.
Grade
Logistics: Incident reports for equipment damage capture factual descriptions of events based on evidence, without assigning blame or making subjective judgments.
Performance Reviews: Employee evaluations are based on pre-defined, measurable Key Performance Indicators (KPIs) and documented achievements.
Surveys: Customer feedback is collected using neutral, well-phrased questions, and responses are analyzed without pre-conceived biases.
Logistics: Sales forecasts for shipping volumes are consistently overestimated due to overly optimistic assumptions from the sales team who are incentivized on targets.
Performance Reviews: Employee performance ratings are subjectively influenced by a manager's personal relationship with the employee rather than objective criteria.
Surveys: Leading questions in a customer satisfaction survey are designed to elicit positive responses, skewing the results.