Snow_Retail_Analytics_DWBI_Suite

Developed a Retail Analytics project utilizing a Snowflake Schema design to convert raw retail data into actionable insights. The project integrates key dimensions such as customers, products, orders, time, and store information. It involves end-to-end processes including data cleaning, transformation, modeling, and visualization to support strategic business decisions.

The data model was built using a combination of sample datasets generated through Python libraries and tables sourced from Oracle Database, with well-defined relationships established between entities for effective analysis.

🧱 Data Model Overview

This project uses a normalized dimensional model where some dimension tables reference additional dimension tables for better organization, flexibility, and data integrity.

At the center of the model is the FACT_ORDERS table, which captures transactional sales data and is connected to multiple dimension tables.

⚙ Languages and Tools involved:

Language: Python, SQL

Tools: Oracle, Jupyter_Notebook, Excel, Snowflake_DB ❄, Power_BI

📐 Entity Relationship Diagram

Below is the ER diagram that illustrates the logical data model for the retail analytics project:

🔹Sales Retail Analytics Dashboard

Below is the BI Dashboard that illustrates the logical data visualisation of the Retail data analytics project:

Dashboard Link: Snow Retail DWBI

📊 Tables and Their Roles

🔹 FACT_ORDERS

The core fact table that holds transactional sales data.

Order_ID, Customer_ID, Store_ID, Order_Date_ID, Product_ID, Quantity_Ordered, Order_Amount, Discount_Amount, Shipping_Cost, Total_Amount

🔹 DIM_CUSTOMER

Holds detailed customer information.

Customer_ID, First_Name, Last_Name, Gender, DOB, Email, Phone_Number, Address, City, State, Zip_Code, Country,Loyalty_Program_ID

🔹 DIM_DATE

Provides temporal context for orders.

Date_ID, Date, Day_Of_Week, Month, Quarter, Year, IsWeekend

🔹 DIM_PRODUCT

Contains product-related metadata.

Product_ID, Product_Name, Category, Brand, Unit_Price

🔹 DIM_STORE

Stores store-specific details where the sales occur.

Store_ID, Store_Name, Store_Type, Store_Opening_Date, Address, City, State, Country, Region, Manager_Name

🔹 DIM_LOYALTY_INFO

Details of customer loyalty program participation.

Key Fields:

• Loyalty_Program_ID (Primary Key)
• Program_Name, Program_Tier, Points_Accrued

🔗 Relationships

• FACT_ORDERS references:

  • DIM_CUSTOMER via Customer_ID
  • DIM_DATE via Order_Date_ID
  • DIM_PRODUCT via Product_ID
  • DIM_STORE via Store_ID

• DIM_CUSTOMER references:

  • DIM_LOYALTY_INFO via Loyalty_Program_ID

This design ensures efficient slicing and dicing of sales data across different business perspectives like customer demographics, product hierarchy, store performance, and loyalty programs.