WorkforceIQ — HR Analytics Dashboard

An end-to-end HR analytics project that tracks employee attrition, compensation, performance, diversity, and attendance using SQL for data analysis and Power BI for visualization.

📌 Overview

This project simulates a real-world HR analytics workflow: raw employee data is loaded into a PostgreSQL database, analyzed using SQL, and visualized in an interactive Power BI dashboard. The goal is to answer the kind of business questions an HR or People Analytics team would actually ask — who is leaving, who is underpaid, which departments struggle with attendance, and whether performance and pay are aligned.

🛠️ Tech stack

• PostgreSQL — data modeling and analysis
• Power BI — interactive dashboard and reporting
• VS Code — environment for SQL development and script execution

📊 PowerBI Dashboard

Project workflow

Raw CSV data → PostgreSQL (table creation + import) → SQL analysis queries → Power BI (dashboard)

1. Extract — Raw HR data sourced as CSV files (employees, departments, salaries, performance, attendance)
2. Load — Created a normalized schema in PostgreSQL with 5 tables and foreign key relationships, then imported the CSV files using VS code Terminal
3. Analyze — 12 SQL queries written in PostgreSQL to answer specific HR business questions
4. Visualize — Each SQL query's result loaded directly into Power BI and mapped to a corresponding chart

Dataset

Table	Rows	Description
employees	1,000	Core employee master data (demographics, hire/exit dates, job title, status)
departments	7	Department reference table
salaries	1,000	Base salary and bonus per employee
performance	2,000	Annual performance ratings (2022–2023, scale 1–5)
attendance	90,000	Daily attendance records (Jan–Mar 2024)

Business questions answered (SQL queries)

Each query was designed to answer a specific HR business question, and each one feeds directly into a Power BI visual.

#	Business question	Power BI visual
Q1	What is the total and active headcount?	KPI card
Q2	What is the overall attrition rate?	KPI card
Q3	Which department has the worst attendance?	KPI card
Q4	Which department has the highest attrition?	Clustered Bar chart
Q5	Is the company's headcount growing year over year?	Line chart
Q6	How is performance rating distributed across the workforce?	Donut chart
Q7	Is there a pay gap by department and gender?	Clustered Bar chart
Q8	How long do employees typically stay before leaving?	Pie chart
Q9	Do higher performers earn more on average?	Funnel chart
Q10	What is the gender diversity ratio per department?	Line and Clustered Column chart
Q11	Has average performance improved year over year by department?	Line chart
Q12	Who are the top 10 highest-paid active employees?	Table

All 12 queries are available in /queries/all_queries.sql/.

📊 Power BI integration

Each query's result set was loaded into Power BI as its own table using **Get Data → PostgreSQL database → Advanced options**, pasting the corresponding SQL query directly. No DAX measures or calculated columns were created — all aggregation and business logic happens at the SQL layer, and Power BI is used purely for visualization and interactivity (slicers, filters, drill-through).

Repository structure

WorkforceIQ — HR Analytics Dashboard/
│
├── data/
│   ├── employees.csv
│   ├── departments.csv
│   ├── salaries.csv
│   ├── performance.csv
│   └── attendance.csv
│
├── queries/
│   ├── schema.sql
│   └── all_queries.sql
│
├── doc/
│   ├── schema.erd.json
│   └── workforceiq.pbix
│
├── LICENSE
│
└── README.md

⚙️ How to run this project

1. Clone the repository

git clone https://github.com/bithiNath/WorkforceIQ.git

2. Run schema.sql in PostgreSQL or VScode to create database and tables.
3. Import the CSV files from /data by using VS Code terminal:
   • Open the VS Code integrated terminal and connect to your PostgreSQL database using psql:

     psql -U your_username -d your_database_name

   • Run the following \copy command for each table (change table and file names accordingly):

     \copy table_name FROM 'data/your_file.csv' DELIMITER ',' CSV HEADER;

4. Run the queries in queries/all_queries.sql to verify the data.
5. View Dashboard Screenshots: open doc/workforceiq.csv

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

📬 Contact

• GitHub: @bithiNath
• LinkedIn: Bithi Nath

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Developed by @bithiNath ⚡