You might get separate groups for Premium, premium, PREMIUM.

Instead:

SELECT UPPER(TRIM(customer_type)) AS customer_type, COUNT(*) AS customer_count 
FROM customers 
GROUP BY UPPER(TRIM(customer_type));

Now logically equivalent values can be grouped together.

🧹 20. Cleaning Product Names

Suppose product names contain unnecessary spaces and inconsistent capitalization.

SELECT UPPER(TRIM(product_name)) AS cleaned_product_name FROM products;

You can also remove unwanted characters:

SELECT REPLACE(TRIM(product_name), '-', ' ') AS cleaned_product_name FROM products;

Example: ' wireless-earbuds ' can become wireless earbuds.

💼 21. Real-World Business Example

Suppose an e-commerce company stores customer names inconsistently.

You have ' alice ', 'ALICE', 'Alice', ' alice'

You can create a normalized version:

SELECT UPPER(TRIM(customer_name)) AS normalized_name FROM customers;

This produces ALICE, ALICE, ALICE, ALICE.

The cleaned value can be used for analysis or as part of a data-matching strategy.

String normalization alone does not guarantee that two records represent the same person.

🧩 22. Combining Multiple String Functions

SQL becomes particularly powerful when functions are combined.

SELECT UPPER(TRIM(customer_name)) AS cleaned_name FROM customers;
SELECT LOWER(TRIM(email)) AS cleaned_email FROM customers;