Employee Performance Review Dashboard - Systemic Risk & Behavioral Insights

📊 Key Insights

• Compliance-heavy departments (Legal & Finance) had the highest overdue rates, contradicting leadership expectations

• Manager rating style influenced scores more than rating volume, raising fairness and calibration questions

• The performance review backlog reflected behavior patterns, not tooling or workflow issues (accountability mattered more than reminders)

These insights reframed performance reviews from an administrative exercise to a leadership behavior challenge.

🏷️ Overview

This project analyzes systemic delays in employee performance reviews across a dataset of 2,254 employees. Using Python-based data visualization, it highlights overdue risk, tenure-linked performance patterns, and manager behavior profiles. This enables HR and leadership to intervene earlier and support more consistent performance conversations.

❗ Problem

• 61.1% of performance reviews were overdue, affecting recognition and development

• Operations, Legal, and Finance exceeded 65% backlog despite strict compliance culture

• The biggest bottleneck occurred at the approval stage, not during review completion

• Leadership lacked visibility into tenure trends and rating behaviors

• Reporting was manual and not scalable

🧩 Solution

• Built a performance insights dashboard using Python, Matplotlib, and Seaborn

• Segmented departments into low / medium / high overdue risk, enabling targeted support

• Analyzed tenure-based trends to align feedback timing with development needs

• Identified four manager rating archetypes to improve calibration and fairness

• Reduced reporting effort by ~60% and supported monthly instead of quarterly review conversations

The dashboard provided a single source of truth for performance review health and leadership accountability.

🛠️ Methodology

• Cleaned and transformed synthetic HR dataset (2,254 employees) to isolate review status, stage, rating, department, and tenure variables

• Conducted exploratory analysis across:

  • Completion bottlenecks

  • Departmental overdue risk

  • Tenure-performance trends

  • Rating distributions

  • Manager behavior clusters

• Built visualizations using Python (Pandas, NumPy, Seaborn, Matplotlib)

• Combined visual findings with an executive narrative layer to support decision-making and prioritization