Workforce Analytics and Bias Detection Using 360-Degree Performance Review Data

This project is based on a real-world case study of MC Foods Inc. (MCF), a large food company headquartered in the U.S., which uses a 360-degree employee evaluation process to assess performance across seven core values. The objective of the project was to analyze 2019 evaluation data, assess the integrity of performance and promotion decisions, and examine the potential for implicit bias across gender, ethnicity, and business units. The project reflects deep application of the analytics mindset — asking the right questions, transforming data, applying statistical and visual analysis, and effectively communicating insights.

Data Overview:

675,000+ records from MCF’s 360-degree evaluations

Each employee rated across 7 core values: Availability, Determination, Discipline, Humility, Ownership, Simplicity, Sincerity

Ratings were collected from four sources: self, manager, cross group, and direct reports

Includes demographics (age, gender, ethnicity), tenure, business unit, and location

Key Objectives:

Assess whether 360-degree ratings provide valid inputs for promotions and the Nine-Box Matrix

Detect potential rating biases across gender, ethnicity, or rater categories

Evaluate organizational performance by location, business unit, and manager influence

Deliver strategic recommendations for improving performance evaluation fairness and accuracy

Tools & Techniques Used:

Power BI or Tableau for interactive visual dashboards

Python (pandas, matplotlib, seaborn) or R (dplyr, ggplot2) for statistical modeling

SPSS or Excel for data preprocessing, validation, and summary stats

ETL Process: Cleaned, filtered, and validated over 675K records; handled missing values and inconsistent formats

Key Analytical Components:

Organizational Performance Assessment

Visualized average non-self ratings by business unit and location

Mapped performance scores to identify underperforming regions

Compared manager vs. employee self-evaluations across core values

Bias Detection and Fairness Analysis

Analyzed ratings by gender and ethnic group, comparing self, manager, and peer evaluations

Measured differences in manager ratings based on demographic similarity/difference between employee and manager

Highlighted potential bias patterns in specific locations and business units

Nine-Box Ranking Simulation

Ranked employees based on average non-self ratings

Segmented the workforce into top, middle, and bottom performers

Examined the fairness of forced rankings and their reliance on subjective inputs

Value-Specific Insights

Assessed which of the 7 core values showed the greatest rating variability across departments and raters

Highlighted values that are most subjective or difficult to assess fairly

Custom Recommendations

Suggested enhancements to the 360 process including additional inputs (e.g., KPIs, peer-reviewed contributions)

Recommended combining qualitative feedback with quantitative scoring

Proposed blind review pilots to reduce demographic-based bias

Outcomes and Impact:

Delivered a 4-page visual executive report with performance and DEI dashboards

Identified departments and locations with potential evaluator bias

Proposed data-driven improvements to MCF’s promotion and evaluation framework

Showcased ability to apply advanced analytics to organizational behavior and HR processes

This project reflects a robust application of people analytics, combining statistical reasoning, data visualization, and DEI auditing to support equitable talent management. It demonstrates your capability to handle large organizational datasets, uncover hidden patterns, and provide strategic insights for executive leadership.