🧭 Context & Challenge:

As part of my practical SQL certification exam, I worked with raw product data from FoodYum – a fictional grocery retailer navigating inflation-driven consumer behavior.

The dataset had significant data quality issues and gaps that prevented business users from analyzing product availability, pricing strategies, and customer accessibility.

🧩 Objectives:

• Clean and structure the dataset for reliable analysis

• Identify missing/incomplete product records and fix them using business rules

• Enable KPI tracking of product distribution, category performance, and price ranges

• Support decision-making around pricing, customer segmentation, and inventory optimization

🛠️ Technical Approach:

• SQL-based Data Cleaning: Detected and handled nulls, removed outliers, fixed datatype mismatches

• Business Logic Imputation: Median-based imputation for numeric fields, categorical fallback for types

• Data Enrichment: Added derived columns for analysis (e.g., price/weight ratio, category coverage)

• Exploratory SQL Queries: Filtered, grouped and ranked product performance across dimensions

• Churn Risk Flagging: Used filters to highlight product types at risk of customer drop-off

🔍 Strategic Learnings:

This project demonstrated the importance of structured SQL logic in preparing datasets for business use.

It also confirmed that data cleaning is not just technical – it’s a strategic step that determines what the business can see, understand and act on.

Even without visual tools, SQL alone delivered powerful insight into churn risk, price positioning and SKU health.