SQL for Data Analysis: Unlocking Business Intelligence and Advanced Analytics

Published 3 months ago

Introduction

Structured Query Language (SQL) is a fundamental tool for managing and analysing data. It is widely used for Business Intelligence (BI), enabling organisations to make data-driven decisions. This blog explores the role of SQL in data analysis, writing complex analytical queries, and understanding data warehousing concepts.

1. Using SQL for Business Intelligence

What is Business Intelligence?

Business Intelligence (BI) refers to the process of collecting, processing, and analysing data to support decision-making. SQL plays a crucial role in BI by extracting and transforming data for reporting and insights.

Key SQL Functions for BI

Aggregation FunctionsSUM(), AVG(), COUNT(), MIN(), MAX()
Example:

SELECT department, SUM(sales) AS total_sales
FROM orders
GROUP BY department;

Joins and SubqueriesCombining multiple tables to derive insights.
Example:

SELECT customers.name, SUM(orders.amount)
FROM customers
JOIN orders ON customers.id = orders.customer_id
GROUP BY customers.name;

Window FunctionsRANK(), DENSE_RANK(), LEAD(), LAG() for ranking and trend analysis.
Example:

SELECT name, sales, RANK() OVER (ORDER BY sales DESC) AS rank
FROM employees;

Data FilteringUsing WHERE, HAVING, and CASE statements to refine analysis.
Example:

SELECT product, SUM(sales)
FROM sales_data
WHERE year = 2024
GROUP BY product
HAVING SUM(sales) > 50000;

2. Writing Complex Analytical Queries

Common Analytical Techniques

Common Table Expressions (CTEs)

Improves readability and maintains complex queries efficiently.
Example:

WITH monthly_sales AS (
    SELECT date_trunc('month', order_date) AS month, SUM(amount) AS total_sales
    FROM orders
    GROUP BY month
)
SELECT * FROM monthly_sales;

Pivoting and Unpivoting Data

Transforming rows into columns and vice versa.
Example:

SELECT *
FROM sales_data
PIVOT (
    SUM(sales) FOR year IN (2022, 2023, 2024)
) AS pivot_table;

Recursive Queries

Useful for hierarchical data (e.g., organisational charts, category trees).
Example:

WITH RECURSIVE employee_hierarchy AS (
    SELECT id, name, manager_id FROM employees WHERE manager_id IS NULL
    UNION ALL
    SELECT e.id, e.name, e.manager_id
    FROM employees e
    JOIN employee_hierarchy eh ON e.manager_id = eh.id
)
SELECT * FROM employee_hierarchy;

Time Series Analysis

Using LAG(), LEAD() for trend analysis.
Example:

SELECT order_date, sales,
       LAG(sales, 1) OVER (ORDER BY order_date) AS previous_day_sales
FROM orders;

3. Data Warehousing Concepts

What is a Data Warehouse?

A data warehouse is a centralised repository used for storing, managing, and analysing large volumes of data. It is optimised for analytical queries rather than transactional processing.

Key Concepts in Data Warehousing

ETL (Extract, Transform, Load)

Extracting data from various sources.
Transforming data into a standard format.
Loading data into the warehouse for analysis.

Star and Snowflake Schema

Star Schema: A fact table is surrounded by dimension tables.
Snowflake Schema: Normalised form of Star Schema with additional levels of dimension tables.

Fact and Dimension Tables

Fact Table: Stores business events (e.g., sales transactions).
Dimension Table: Stores descriptive attributes (e.g., product details, customer information).

Indexing and Partitioning

Indexing speeds up query performance.
Partitioning divides tables into manageable segments.

OLAP (Online Analytical Processing)

Supports complex queries and multi-dimensional analysis.
Example OLAP query:

SELECT region, product, SUM(sales)
FROM sales_data
GROUP BY CUBE(region, product);

Conclusion

SQL is a powerful tool for Business Intelligence and Data Analysis. From writing complex queries to managing data warehouses, it plays a vital role in transforming raw data into actionable insights. Mastering these SQL concepts will enable businesses to make informed, data-driven decisions efficiently.

Obafemi Emmanuel