What’s the Benefit of SQL in Business Analysis? 5 Practical Examples

Why do you need SQL in business analysis? Well, the short answer is that it makes your work more effective and efficient. In this article, I’ll show you some simple, real-world examples of how SQL can help your daily business analysis work.

Are you a business analyst, or do you frequently analyze business data? Or are you planning to get into this field? Then I’d recommend you learn SQL. SQL (Structured Query Language) isn’t just the language of relation databases – it’s also the language of data analysis. Let’s find out why SQL in business analysis is such a game changer using real-world examples!

Why Learn SQL?

SQL is an enormously powerful tool for any kind of data analysis, including financial, marketing, and business intelligence analyses. Once you understand its techniques, SQL makes analyzing data and spotting insights much easier. You can then use your findings to make smarter recommendations. And, even better, you don’t have to wait for the IT team to get around to your query requests – you can save time and write them yourself!

If you already know you want to learn SQL to analyze your business data, I recommend our SQL for Data Analysis track. This four-course track takes you from the absolute basics to creating reports in SQL. It even covers SQL window functions – an awesome set of tools that make complex data analyses easy. Even better, you don’t need any database or IT knowledge to take this course. You don’t even need to set anything up; it all happens in your browser.

Using SQL In Business Analysis

If you’ve been around business analysis at all, you know that this means working with data – lots and lots of data. But simply having data won’t help much; you have to know how to extract its insights and find its hidden messages. And this is where SQL comes in handy.

As we’ve already discussed, SQL is the language of databases. And as most data is stored in databases, knowing SQL is essential to accessing and analyzing that data. It helps you find the exact data you need, analyze it, and present it in a way that makes sense.

Why is SQL important for business analysts? Because it allows you to:

• Extract specific data from large datasets.
• Transform raw data into digestible, actionable information that can be understood at a glance.
• Analyze trends, patterns, and relationships in the data. This will help you make better, more accurate recommendations.

In short, SQL is the key that unlocks business data. Even if you use business intelligence and data visualization tools like Tableau or Power BI, SQL fluency can still make your work more efficient.

Now that you understand the importance of SQL, let’s explore some real-world examples of using SQL in business analysis!

Examples of SQL in Business Analysis

Example 1: Analyzing Sales Performance

Imagine you work for an e-commerce company. Your manager wants last quarter’s sales performance report. Specifically, they want to know the total sales, the number of orders, and the best-selling products. How will SQL make this process easier?

First, SQL queries aren’t that long or hard to write. In this case, we’d write three separate queries to get the information. Then we can pull that info into a report and hand it to the boss – without waiting for anyone else to extract the data for us!

So, here’s the query for total sales:

SELECT SUM(sales_amount) AS total_sales
FROM orders
WHERE order_date BETWEEN '2024-07-01' AND '2024-09-30';

This query returns the total sales made for Q3 2024. The

function does the heavy lifting by adding up all the information in the column. The statement simply tells the database what information to show in the result, and the FROM clause tells the database the name of the table where this data is stored. But because we only want data from Q3, we limit the results using the clause. will only return results where the order date is between July 1 and September 30.

Next, let’s find the number of orders in Q3. This query will return the number of orders placed between July 1 and September 30:

SELECT COUNT(order_id) AS total_orders
FROM orders
WHERE order_date BETWEEN '2024-07-01' AND '2024-09-30';

You already know what the

, , and elements do. The change here is the function, which counts the number of rows with a value in the column. In this case, it means we’re counting the number of orders placed during Q3. This gives you a quick snapshot of how many transactions were completed.

Finally, we need to see the best-selling products of all time. The query below will return our company’s five top sellers:

SELECT product_id, SUM(quantity) AS total_quantity_sold
FROM order_details
GROUP BY product_id
ORDER BY total_quantity_sold DESC
LIMIT 5;

This query finds the top 5 best-selling products in the database by summing up the quantity sold for each

. It helps you figure out which products are flying off the shelves!

Most of this query’s parts will be familiar to you. The new clauses are

(which puts rows into groups based on a shared value – in this case, the same product ID) and (which orders rows in the result – in this case, by the total quantity sold from highest to lowest).

Practical Tip: Running queries like these saves time and gives you the data you need in seconds. Here’s Why You Should Use SQL for Sales Analytics.

Example 2: Identifying Your Best Customers

Your marketing team is planning to launch a new loyalty program, so they want to know which customers have spent the most money this year. Your mission is to identify the top 10 customers with the highest spending. Here’s the query that will find that information:

SELECT customer_id, SUM(sales_amount) AS total_spent
FROM orders
WHERE order_date BETWEEN '2023-09-01' AND '2024-09-01'
GROUP BY customer_id
ORDER BY total_spent DESC
LIMIT 10;

This query sums up the total amount each customer has spent over the past year and orders the results by the highest spenders. The

groups all transactions by customer, and sorts them from highest to lowest. LIMIT 10 returns only the first ten results – i.e. your ten best customers in terms of money spent.

Takeaway: Being able to identify your top customers quickly is incredibly valuable for marketing and sales teams. Want to learn how to create more detailed customer analysis reports? Check out our  SQL Reporting track!

Example 3: Managing Inventory

You’re responsible for managing your company inventory, which means making sure you never run out of stock. You need a list of low-stock items so you can decide what to reorder. The following query will return items that have less than 100 units in stock:

SELECT product_id, product_name, stock_quantity
FROM products
WHERE stock_quantity < 100
ORDER BY stock_quantity ASC;

This query lists all products with stock levels below 100. It’s sorted in ascending order (1-10), so you can easily see which items are about to run out.

Takeaway: Managing stock levels can be stressful, but SQL makes it easier. With just a few lines of code, you can generate a low-stock report and stay ahead of potential problems.

Example 4: Measuring Marketing Campaign Success

Your company has just launched a new marketing campaign and needs to measure its impact. Specifically, your boss wants to know how many new customers made their first purchase after the campaign started. The following query will return a list of these customers:

SELECT COUNT(customer_id) AS new_customers
FROM customers
WHERE first_order_date > '2024-01-01';

This query counts how many customers made their first purchase after January 1, 2024 – an easy way to track the success of that particular marketing campaign.

Takeaway: Being able to tie new customer acquisitions to marketing campaigns is crucial for proving ROI. The following helpful resources will help you create effective SQL marketing reports:

• SQL for Marketing Course
• Why You Should Use SQL in Marketing Analytics
• How to Learn SQL for Marketing Analytics With LearnSQL.com
• How SQL for Business Can Boost Profit and Effectiveness

Example 5: Financial Forecasting

Now let’s imagine your finance team is looking for a revenue forecast for the next quarter. They ask you to calculate the average daily sales for the past quarter as a basis for future predictions. The following query will give you the info you need:

SELECT AVG(daily_sales) AS avg_daily_sales
FROM (
    SELECT SUM(sales_amount) AS daily_sales
    FROM orders
    WHERE order_date BETWEEN '2024-07-01' AND '2024-09-30'
    GROUP BY order_date
) AS daily_totals;

This query calculates the average daily sales from the last quarter. The subquery inside the FROM clause totals daily sales, and the main query uses that information to calculate the average daily sales total. This is a powerful way to predict future revenue based on past performance.

Takeaway: Accurate financial forecasting can have a big impact on your business’s bottom line. If you want to dive deeper into SQL for financial analysis, check out our article on 6 Advanced SQL Queries for Analyzing Financial Data. For an in-depth approach to the subject, consider our course Revenue Trend Analysis in SQL. (This is an advanced course, so you’ll need to understand basic SQL before you take it.

Keep Building Your SQL Business Analysis Skills

Now you know just how effective SQL is in business analysis. It doesn’t matter if you’re analyzing sales performance, managing inventory, or forecasting revenue; this language empowers you to make decisions faster and more accurately.

But learning SQL is a process. The more you practice, the more comfortable you’ll get with it. If you’re ready to start mastering SQL for business analysis, I highly recommend the SQL for Data Analysis track I mentioned earlier. It’s designed specifically for non-technical analysts looking to become more data-savvy.

And if reporting is a big part of your role, don’t miss our SQL Reporting track. You’ll learn how to create clear, compelling reports that communicate exactly what your data is saying.

Keep learning, stay curious, and happy querying!