Analyses anonymised workforce data to surface attrition risk, engagement patterns, absence risk and intervention priorities.
The examples below show where this project can be used, what decision it supports, and how a user can test the scenario in the sandbox.
Where it fits: Combine engagement, performance, tenure, absence and workload indicators to detect teams with higher retention risk.
Decision supported: Prioritise manager follow-up, workload review and development conversations.
How to test it: Apply department filters and review attrition risk distribution.
Where it fits: Monitor headcount, salary bands, performance and absence patterns across offices.
Decision supported: Support staffing, succession planning and budget allocation.
How to test it: Filter by region/department and compare dynamic dashboard visuals.
Where it fits: Identify aggregate stress signals such as high absence, low engagement or high turnover risk in field teams.
Decision supported: Plan support, rotation, supervision and wellbeing interventions.
How to test it: Use only anonymised data and export the clean HR report.
Where it fits: Track engagement, absence and performance dips among high-volume service teams.
Decision supported: Reduce turnover and protect service levels.
How to test it: Use the assistant to summarise top risk groups.
Where it fits: Translate people data into clear interventions without exposing sensitive individual details unnecessarily.
Decision supported: Guide targeted coaching, training and retention programmes.
How to test it: Review the high-priority retention queue and action suggestions.
Where it fits: Separate PII from analytics and report only responsible aggregate insights.
Decision supported: Improve trust and reduce misuse of employee data.
How to test it: Check the PII detection indicators in the sandbox.
The examples above were revised against current industry and research references on how this class of analytics solution is used in practice.