Data Analytics Power BI Report

Overview
Key Features
Project Structure
Usage
Screenshots

Overview

This repository contains the files and documentation for my Power BI Data Analysis project, showcasing my skills in data visualization and analysis. The project focuses on transforming data into actionable insights for better decision-making by using Microsoft Power BI to design a comprehensive Quarterly report.

Key Features

This project involves:

Extracting and transforming data from various origins
Designing a robust data model rooted in a star-based schema
Constructing a multi-page report

The report presents:

A high-level business summary tailored for C-suite executives
Insights into the company's highest value customers segmented by sales region
A detailed analysis of top-performing products categorised by type against their sales targets
A visually appealing map visual that spotlights the performance metrics of their retail outlets across different territories

Project Structure

├── Q&A/ 
│   ├── questions.txt
|   ├── question_1.sql
|   ├── question_1.csv
|   └── question_2.sql ...
├── navigation_bar_images/
│   ├── UI Icon #1.png
│   ├── UI Icon #1 Hover.png 
|   └── UI Icon #2.png ...
├── Power BI Project.pbix
├── LICENSE.txt
└── README.md

Questions

Q1. How many staff are there in all of the UK stores?

SELECT SUM(staff_numbers)
FROM dim_store
WHERE country = 'UK';

13273

Q2. Which month in 2022 has had the highest revenue?

SELECT d.month_name
FROM dim_date AS d
JOIN orders AS o ON d.date = o.order_date
JOIN dim_product AS p ON o.product_code = p.product_code
WHERE d.year = 2022
GROUP BY d.month_name
ORDER BY SUM(p.sale_price * o.product_quantity) DESC
LIMIT 1;

August

Q3. Which German store type had the highest revenue for 2022?

SELECT s.store_type 
FROM dim_store AS s
JOIN orders AS o ON s.store_code = o.store_code
JOIN dim_date AS d ON o.order_date = d.date
JOIN dim_product AS p ON o.product_code = p.product_code
WHERE d.year = 2022 AND
    s.country = 'Germany'
GROUP BY s.store_type
ORDER BY SUM(p.sale_price * o.product_quantity) DESC
LIMIT 1;

Local

Q4. Create a view where the rows are the store types and the columns are the total revenue, percentage of total revenue and the count of orders

CREATE VIEW summary AS
    SELECT s.store_type, 
        ROUND(CAST(SUM(p.sale_price * o.product_quantity) AS NUMERIC), 2) AS total_revenue, 
        ROUND(CAST(SUM(p.sale_price * o.product_quantity) / SUM(SUM(p.sale_price * o.product_quantity)) OVER() * 100 AS NUMERIC), 2) AS percentage_of_revenue, 
        COUNT(o) AS order_count
    FROM dim_store AS s
    JOIN orders AS o ON s.store_code = o.store_code
    JOIN dim_product AS p ON o.product_code = p.product_code
    GROUP BY s.store_type;

store_type	total_revenue	percentage_of_revenue	order_count
Local	4200344.27	65.02	78409
Mall Kiosk	705645.90	10.92	13142
Outlet	508029.41	7.86	9207
Super Store	1045045.19	16.18	19276
Web Portal	1067.77	0.02	43

Q5. Which product category generated the most profit for the "Wiltshire, UK" region in 2021?

SELECT p.category
FROM dim_product AS p
JOIN orders AS o ON p.product_code = o.product_code
JOIN dim_store AS s ON o.store_code = s.store_code
JOIN dim_date AS d ON o.order_date = d.date
WHERE s.full_region = 'Wiltshire, UK' AND
    d.year = 2021
GROUP BY p.category
ORDER BY SUM((p.sale_price - p.cost_price) * o.product_quantity) DESC
LIMIT 1;