Welcome to combining data from different sources! Real business insights often require bringing together information from multiple datasets to get the complete picture.
Think customer data plus purchase history plus product details - much richer than any single dataset alone.
Engagement Message
What's one business question that might need data from multiple sources?
Here's why data combination matters: your customer table might have names and ages, but your sales table has purchase amounts and dates.
To analyze "Do older customers spend more?", you need to connect these separate pieces of information.
Engagement Message
Why is it useful to analyze customer age alongside purchase amount?
The key to combining data is finding common identifiers - pieces of information that appear in both datasets. Usually this is something like Customer ID, Product ID, or Order ID.
These identifiers act as bridges connecting related information across different tables.
Engagement Message
What identifier would you use to connect customer info with their purchase history?
Let's see a practical example. You have a Customer table with Customer ID, Name, and Age, and a Sales table with Customer ID, Purchase Amount, and Date.
The shared Customer ID lets you connect customer details to their purchases and answer questions like average spending by age group.
Customer Table
Sales Table
