From Dirty Data to Business Insights: Building the Tembo Hotel Suites Database

Description and use

The project cleaned and normalized a chaotic hotel booking dataset, creating a structured relational database that enabled accurate SQL analytics for revenue and room performance.

Problem

The original hotel booking dataset was messy with inconsistent formatting, missing values, and varied data types, making reliable analysis impossible.

Outcome

The project produced a clean, normalized database that enabled accurate SQL queries to answer key business questions such as monthly revenue trends and room performance metrics.

Workflow

• Create a staging table to import raw data
• Inspect each column for inconsistencies
• Standardize names, phone numbers, dates, monetary values, and categorical fields
• Clean and transform data using SQL UPDATE/INSERT statements
• Design relational schema with separate tables and foreign keys
• Migrate cleaned data from staging to final tables
• Run verification queries to ensure data integrity
• Perform business analysis queries (revenue, room performance, guest locations)

Evidence

I divided the project into five main stages: Database and staging setup, Data cleaning, Database design, Loading clean data, Business analysis.

Source

The original dataset contained hotel booking information such as: Guest details, Phone numbers, Room information, Booking dates, Staff information, Salaries, Payment methods, Booking status, Services used, Guest ratings, Revenue information.

Source