Financial KPI Dashboard

Tools: Excel (Advanced) · Plotly.js · GitHub Pages
Dataset: UK Retail Sales Data (ONS) / Kaggle Financial Dataset
Live Dashboard: 🔗 View Live Interactive Dashboard

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Problem Statement

Finance teams need real-time visibility into revenue performance, cost control, and margin trends. This project builds an end-to-end financial KPI dashboard — from raw data to boardroom-ready visualisations.

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What Was Built

Interactive Plotly.js Dashboard (index.html)

• Revenue vs Budget vs Forecast — monthly trend lines for Jan–Apr 2024
• Revenue Mix by Category — share of total revenue across product segments
• Regional Revenue Breakdown — stacked bar chart by region and month
• Profit Margin by Segment — gross margin % and EBITDA margin % per category
• Cost Waterfall (P&L Bridge) — Revenue → COGS → Gross Profit → OpEx → EBITDA
• Budget Variance RAG Table — Actual vs Budget with Red / Amber / Green status
• Revenue Heatmap — Region × Month intensity matrix
• Operating Expenses by Region — grouped bar chart (£ thousands)

Data Preparation (Excel)

• Revenue forecasting using moving averages
• Budget vs Actual variance analysis with conditional formatting (RAG)
• P&L summary by month and segment
• Pivot tables for drill-down by region, product category, and time period

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Key KPIs Tracked

KPI	Value	Status
Total Revenue (Actual)	£4.92M	🟢 On Track
Revenue Budget	£4.97M	—
Gross Margin %	41.9%	🟡 Watch
EBITDA Margin %	17.6%	🟢 On Track
Cost-to-Revenue Ratio	82.4%	🟢 On Track
Revenue MoM Growth	+5.5%	🟢 On Track
Budget Variance	−1.0%	🟢 On Track

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Repository Structure

financial-kpi-dashboard/
├── index.html               # Interactive Plotly.js dashboard (deployed via GitHub Pages)
├── retail_sales_sample.csv   # Source dataset — UK retail sales Jan–Apr 2024
├── requirements.txt          # Python dependencies
├── LICENSE                   # MIT License
└── README.md                 # This file

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How to Run Locally

1. Clone the repository:

   git clone https://github.com/mayankjoshiii/financial-kpi-dashboard.git

2. Open index.html in any modern browser — no server required.
3. To modify the data pipeline, install Python dependencies:

   pip install -r requirements.txt

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Live Dashboard

👉 View the Interactive Dashboard

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Author

Mayank Joshi — Business Analyst & Data Analyst
MSc Business Analytics (Distinction) · Swansea University
LinkedIn · GitHub