Data Analysis is more than just making charts; it's about transforming raw, messy data into actionable insights. While beginners stop at GROUP BY, professional analysts know that the real power of SQL lies in data cleaning, time-series analysis, and process modeling.
In this comprehensive guide, we will walk through a complete analysis workflow using a realistic e-commerce dataset. We won't just run queries; we'll solve actual business problems.
The Scenario: E-Commerce Performance Review
Imagine you are the lead analyst for an online retailer. Your stakeholder asks: "How is our user retention trending? And who are our most valuable customers based on their recent spending behavior?"
To answer this, simple aggregations won't cut it. We need advanced techniques.
1. Data Cleaning & Preparation
Real-world data is never clean. Before analyzing, we often need to categorize data or handle missing values.
Binning Data with CASE
Continuous variables (like price or age) are hard to summarize. Analysts often "bin" them into categories using CASE statements.
Handling NULLs with COALESCE
NULL values can ruin reports. COALESCE returns the first non-null value, perfect for setting defaults.
SELECT
customer_name,
COALESCE(phone_number, 'No Phone Provided') as contact_info
FROM customers;
2. Time Series Analysis
Business happens over time. Analyzing trends (Month-over-Month, Year-over-Year) is perhaps the most common task for an analyst.
Date Truncation & Trending
Instead of looking at daily data, we usually aggregate by month or week.
Note: Syntax for date extraction varies by SQL dialect (e.g., DATE_TRUNC in Postgres, strftime in SQLite).
3. Advanced Analysis with Window Functions
This is where you graduate from "SQL User" to "Data Analyst". Window functions allow you to perform calculations across a set of table rows that are somehow related to the current row.
Running Totals
How much cumulative revenue have we generated year-to-date?
Ranking & Top N Analysis
Who are the top 2 customers in each region? A simple LIMIT won't work here because we want the top N per group. Enter RANK() or ROW_NUMBER().
4. Complex Logic with CTEs (Common Table Expressions)
When queries get long, they get hard to read. CTEs (WITH clauses) let you break down complex logic into readable steps.
Let's say we want to find "High Value Churned Customers"—customers who spent more than $500 but haven't bought anything in the last 6 months.
WITH CustomerStats AS (
SELECT
customer_id,
SUM(amount) as total_spend,
MAX(order_date) as last_order_date
FROM orders
GROUP BY customer_id
),
ChurnedCustomers AS (
SELECT *
FROM CustomerStats
WHERE last_order_date < DATE('now', '-6 months')
)
SELECT *
FROM ChurnedCustomers
WHERE total_spend > 500;
This readable, step-by-step approach is crucial when collaborating with data teams.
5. Month-over-Month (MoM) Growth
Finally, accurate growth metrics often require comparing current performance to past performance. LAG() allows you to access data from the previous row without a self-join.
Summary
Data Analysis in SQL goes far beyond retrieving rows. By mastering these patterns, you can answer sophisticated business questions directly in the database:
- Binning & Cleaning: Categorize data for better summarization.
- Date Maths: Understand trends over time.
- Window Functions: Calculate running aggregates and rankings.
- CTEs: Organize complex logic.
- Lag/Lead: Analyze growth metrics.
These are the tools that separate data entry from data analysis.