People Analytics with Turnover Analysis

📋 Project Summary

Development of a complete People Analytics solution, focused on analyzing turnover, retention, absenteeism, hours worked and salary mass.

🎯 Goals

Analyze the history of hirings, dismissals and internal movements.
Identify turnover patterns by age group, gender, education level and position.
Evaluate the financial impact of absenteeism and absences.
Monitoring the distribution of the wage bill in different groups.
Supporting talent retention strategies based on data.

🛠️ Methodology

Gathering and preparing HR data: hiring, firing, salaries, absenteeism and hours worked.
Data processing (cleaning, normalization and integration between sources).
Building dynamic dashboards in Power BI, with filters by position, age, education and race/color.
Exploratory analysis and visualization of trends in turnover, absenteeism and salary distribution.
Calculation of strategic KPIs: turnover rate, % of bad hires, % of overtime, among others.

📈 Main Results

Clear visualization of turnover rates and their variations over the years.
Identifying the positions and profiles most prone to turnover.
Complete analysis of the wage bill by gender, age group, education level and race.
Evaluation of the main reasons for dismissal and the impact on retention.
Estimativas de custos financeiros associados ao absentismo.

🚧 Difficulties Overcome

Overcoming gaps in historical data and standardizing categorical variables.
Integration of different databases for a 360º view of the workforce.
Optimization of the dashboard model to facilitate analysis by non-technical users.

- Technologies and Tools

Power BI - Interactive dashboards and reports.
Python — Data pre-processing (pandas, numpy).
SQL - Extracting and manipulating data in relational databases.

🔍 Final Reflection

This project shows how data analysis can strengthen people management strategy, promoting fact-based decisions and improving talent retention.
Transforming data into useful information is the first step towards building more efficient, humane and sustainable organizations.