Post by Shivam .

📊 Turning Raw Retail Data into Business Insights using SQL I recently worked on an Online Retail dataset and used SQL to extract meaningful insights from raw transactional data. 🔍 What I did: Cleaned messy data (nulls, cancellations, inconsistencies) Performed aggregations using GROUP BY, DISTINCT Used CTEs for structured analysis Analyzed product performance, customer behavior, and time trends 📈 Key Insights: 💰 Generated over £5.77M in revenue from ~14K orders 📦 Identified products with high sales but heavy cancellations (impacting net performance) 🛍️ Found that a small group of customers contributes disproportionately to revenue 📊 Observed seasonal trends, with peak demand around mid-year and strong holiday impact 💸 Noticed that non-product charges (like POSTAGE) contribute significantly to revenue ⚠️ Challenges: Handling missing customer data Managing cancellations and negative quantities Ensuring accurate aggregation using DISTINCT logic 🧠 Key Learning: This project helped me understand how raw data can reveal powerful business insights when analyzed correctly — and how important data cleaning is before any analysis. 🚀 Next step: Built an interactive Excel dashboard on the same dataset (coming soon!) #SQL #DataAnalytics #DataAnalyst #Projects #LearningInPublic