Executive Summary

• Problem & Objective: Atlas Labs, a fast-growing technology company, was facing challenges in understanding the drivers of employee satisfaction and attrition. The HR department needs a data-driven view of workforce composition, performance trends, and turnover patterns to guide retention strategies and improve employee engagement.
• Data & Approach: The dataset contains demographic, educational, and performance information for over 1,400 employees. The analysis focused on identifying trends in satisfaction and attrition through an interactive Power BI dashboard. The model is structured as a snowflake schema, balancing analytical depth with efficient data organization.
• Key Findings: Higher attrition rates were concentrated among employees with lower satisfaction scores and entry-level positions. Education Level and tenure were significant predictors of retention, highlighting the importance of career development initiatives. The Performance Tracker also showed that high performers tend to report higher satisfaction levels, emphasizing the link between recognition and engagement.
• Limitations & Next Steps: The dataset lacked qualitative data such as employee feedback or reasons for leaving. Future analyses could incorporate survey data and predictive modeling to anticipate attrition risks more accurately.

Objective

To analyze employee demographics, performance, and satisfaction data to uncover patterns driving retention and turnover, and to build an HR dashboard that enables data-driven workforce management.

Tools Used

• Power Query: Data transformation and cleaning.
• Power BI: Data modeling, DAX calculations, and interactive dashboard creation.

Process

1. Data Gathering

Data was provided as multiple CSV files representing employees, satisfaction levels, education, and performance ratings.

2. Transformation

Data was cleaned and structured in Power Query. A snowflake schema was established with FactPerformanceRating as the central fact table, connected to dimensions such as DimDate, DimSatisfiedLevel, DimRatingLevel, DimEmployee, and DimEducationLevel.

3. Analysis

Calculated metrics included attrition rate, total active/inactive employees, last/next review, average salary. DAX was used to compute key ratios and conditional measures for segmentation (demographics).

Once the data model and DAX measures were finalized, the visualization stage focused on designing an intuitiv, for-page dashboard for HR decision-making.

4. Visualization

The dashboard is structured into four pages: