What data should performance analytics typically collect?

What data should performance analytics typically collect?

• A. Salary history and personal details.
• B. Attendance alone.
• C. Goals, progress, ratings, feedback sources, calibration results, time-to-improvement, engagement indicators, and business outcomes.
• D. Employee self-evaluation only.

Answer: (C)

Capturing a complete picture of performance requires collecting a broad, multi-source set of data that ties how someone works to the results they drive. This includes goals and progress so you can see what was expected and how close the person is to meeting or exceeding it. Ratings give an overall assessment, but they gain real value when paired with multiple feedback sources (like managers, peers, and direct reports) to balance perspectives. Calibration results are important to ensure consistency across raters and avoid biased or inflated scores. Time-to-improvement tracks how quickly coaching or development actions produce measurable progress, which matters for ongoing growth. Engagement indicators connect how motivated and connected an employee is to their work and the organization, which often influences performance. Finally, business outcomes tie individual performance to tangible results for the company, such as productivity, quality, or customer impact. Data like salary history or personal details aren’t about performance insight and can raise privacy concerns, so they’re not part of performance analytics. Attendance alone only captures a single dimension and misses quality and impact. Employee self-evaluation alone is helpful but biased; the strongest analytics combine several sources to produce a reliable, actionable picture.

Capturing a complete picture of performance requires collecting a broad, multi-source set of data that ties how someone works to the results they drive. This includes goals and progress so you can see what was expected and how close the person is to meeting or exceeding it. Ratings give an overall assessment, but they gain real value when paired with multiple feedback sources (like managers, peers, and direct reports) to balance perspectives. Calibration results are important to ensure consistency across raters and avoid biased or inflated scores. Time-to-improvement tracks how quickly coaching or development actions produce measurable progress, which matters for ongoing growth. Engagement indicators connect how motivated and connected an employee is to their work and the organization, which often influences performance. Finally, business outcomes tie individual performance to tangible results for the company, such as productivity, quality, or customer impact.

Data like salary history or personal details aren’t about performance insight and can raise privacy concerns, so they’re not part of performance analytics. Attendance alone only captures a single dimension and misses quality and impact. Employee self-evaluation alone is helpful but biased; the strongest analytics combine several sources to produce a reliable, actionable picture.