## 🧩 Challenge 01 — About

**From Spreadsheets to Structure**

A regional grocery chain has outgrown its spreadsheet chaos.
Each department tracks products, stores, and vendors differently — IDs drift, columns change weekly, and no one can answer the same question twice.

Your mission is to turn this “Excel spaghetti” into structure — a single, normalized relational database that connects customers, products, stores, and transactions.
The goal: create a trusted foundation ready for analytics, AI, and predictive modeling.

---

### 🧭 Series Context

This challenge is the **first stage** in the *Grocery Retail – Driven by Data* series — a four-part journey from **structure → trust → signal → strategy**.
Here, you establish the architectural backbone that every later stage relies on: data quality checks, time-series forecasting, and promotion ROI modeling.

---

### 🎯 What You’ll Build

Participants will:

* Reverse-engineer a **conceptual data model** from messy spreadsheets.
* Normalize to **3NF**, balancing relational rigor with analytical usability.
* Implement the schema in **PostgreSQL** with keys, constraints, and indexes.
* Build reproducible **ETL pipelines** that clean, type-cast, and load data.
* Demonstrate **analytic readiness** through summary queries or a baseline model.

---

### 💡 Why It Matters

Every advanced analytic or machine-learning workflow begins with trusted structure.
A well-designed schema doesn’t just store data — it defines how the business understands itself.
By completing **Challenge 01**, you’ll transform operational chaos into clarity — laying the groundwork for reproducible, insight-driven decision-making across the entire series.