Introduction
Data becomes valuable when it helps people make better decisions.
In this project, I analyzed booking data for a fictional transport company called Safari Connect. The goal was to clean the raw data, build meaningful business metrics, and design an interactive Power BI dashboard that executives can use to monitor business performance.
This project allowed me to strengthen my SQL, Power BI, and data visualization skills while creating a portfolio-ready business intelligence solution.
Project Objectives
The project aimed to answer questions such as:
- How much revenue has the business generated?
- Which routes generate the highest revenue?
- What is the average passenger rating?
- Which days receive the most bookings?
- Which drivers perform best?
- What is the cancellation rate?
- Which seat class is most popular?
Tools Used
- SQL
- Power BI
- GitHub
Data Cleaning
Before creating the dashboard, I cleaned and prepared the dataset using SQL.
Some of the cleaning tasks included:
- Standardizing data
- Removing unnecessary spaces
- Handling missing values
- Correcting inconsistent entries
- Preparing the data for analysis
Dashboard Features
The dashboard includes:
- Total Revenue
- Total Bookings
- Average Passenger Rating
- Monthly Revenue Trend
- Revenue by Route
- Bookings by Departure Time
- Passenger Distribution by City
- Seat Class Preference
- Driver Performance
- Cancellation Rate
I also created a separate dashboard page containing interactive filters for:
- Departure Date
- Booking Status
- Route Code
- Passenger Gender
- Seat Class
- Vehicle Type
Key Business Insights
Some interesting findings include:
- Revenue exceeded KES 226K during the analysis period.
- The average passenger rating remained above 3.5/5.
- Certain routes generated significantly higher revenue than others.
- Economy Class was the most preferred seat category.
- Bookings were concentrated on specific days and departure times.
- Driver performance varied across the business.
Dashboard Preview
Challenges
Like most data projects, this one involved several challenges.
Some fields required data type corrections before they could be analyzed in Power BI. I also redesigned the dashboard several times to improve readability, color consistency, and the user experience.
These challenges helped me become more confident using both SQL and Power BI.
GitHub Repository
You can view the complete project, SQL scripts, Power BI dashboard, and documentation on GitHub:
https://github.com/Angellicah/safari-connect-business-analysis
Conclusion
This project strengthened my understanding of the complete data analytics workflow—from data cleaning with SQL to creating an interactive dashboard in Power BI.
I'm excited to continue building more analytics projects and expanding my portfolio.
If you have feedback or suggestions, I'd love to hear them.
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