Business Performance Analytics Dashboard

An end-to-end analytics project for a technology consulting company that tracks client acquisition, project delivery, employee utilization, and revenue performance using Python, SQL, and BI reporting.

Problem statement

Technology consulting firms need visibility across sales, delivery, staffing, and finance. This project builds a relational analytics model and KPI dashboard to support business decision-making.

Business questions

• Which lead sources convert best?
• Which service lines generate the most revenue and profit?
• Which project types face the most delays?
• How efficiently are employees utilized?
• What is the client retention rate by industry?

Repository structure

business-performance-analytics/
├── data/
├── notebooks/
├── images/
├── scripts/
├── dashboard/
├── requirements.txt
└── README.md

Data model

This project uses 6 relational tables:

• leads — sales pipeline data
• clients — converted clients
• projects — delivery and revenue data
• employees — consultant information
• time_logs — billable and non-billable work logs
• finance — invoice, cost, and profit data

Data source

The dataset is synthetically generated using Python to simulate a realistic consulting-company workflow from lead generation to invoice payment. The data is reproducible and structured for SQL, Python, and Power BI analysis.

Tools used

• Python: pandas, numpy, matplotlib, seaborn
• SQL: relational schema and KPI queries
• Jupyter Notebook: analysis workflow
• Power BI / Streamlit: dashboarding

Key KPIs

• Lead conversion rate
• On-time project delivery rate
• Employee utilization rate
• Profit margin
• Client retention rate

Key insights

• Some lead sources convert significantly better than others.
• Project delays are concentrated in selected service lines.
• Employee utilization varies across teams.
• Client retention is a major growth opportunity.

How to run

git clone <repo-url>
cd business-performance-analytics
pip install -r requirements.txt
jupyter notebook

Resume highlights

• Built a business performance analytics dashboard for a simulated technology consulting company.
• Designed a 6-table relational dataset and analyzed sales, delivery, workforce, and finance metrics.
• Computed KPIs including conversion, utilization, project delivery, margin, and retention.
• Generated business recommendations using Python, SQL, and dashboarding tools.