Historical Business Longevity & Category Patterns - SQL Analysis Project

🧭 Context & Challenge:

This project explored a dataset compiled by BusinessFinancing.co.uk, covering the oldest existing businesses by country.

The main challenge was to identify patterns in business longevity across continents, categories, and countries – and to uncover what makes certain industries more resilient over time.

The data was stored in separate tables (businesses, countries, categories, new_businesses), requiring joins and conditional logic to create a unified, analytics-ready view.

🧩 Objectives:

• Find the oldest operating business per continent

• Identify which industries tend to survive the longest

• Analyze global coverage gaps in available data

• Combine datasets to compare historical vs. modern business data

🛠️ Technical Approach (SQL):

• Used JOINs across 3 tables: businesses, countries, and categories

• Built a WITH clause to combine businesses and new_businesses for global coverage analysis

• Grouped data by continent and category to rank by founding year

• Applied MIN() functions to extract earliest business by logic segment

• Filtered for null values and used DISTINCT logic to isolate insights

🔍 Strategic Learnings:

This project highlighted how joined datasets and clean group-by logic in SQL can uncover deep patterns in real-world economic structures – across geography, industry, and time.

It also emphasized the importance of data completeness: only by merging and cleaning across sources could we identify blind spots in coverage and interpret results with confidence.