What are Outliers in SQL for Data Analysis?

When working with actual sales and marketing data, the truth is, not all behaviors are normal. Most customers are likely to spend anywhere from $20 to $200, but then you spot one customer with an eye-popping $12,000 order. You know those extreme values as outliers in database analysis, and unless you take them into account, they can quietly skew averages, skew dashboard results, and even business decisions.

Outliers aren’t always about ‘bad data’, though – sometimes they’re actually a sign of a premium customer, a viral campaign, or some unexpected business happenings. The real key to success as a data analyst isn’t just to blindly cut out the outliers, but to figure out where to find them, how to make sense of them, and put them into the right context for the business.

Before diving deeper in, these beginner-friendly resources are a good place to start, building a solid foundation in analytics and SQL to get going with :

• What Is Data Analysis? A Complete Beginner’s Guide
• What Is ETL? Extract, Transform, Load with Tools & Process
• SQL for Data Analysis: Queries, Joins, and Real-World Examples

How Outliers Appear in Sales and Marketing Data

Outliers are values that sit far away from the normal pattern in a dataset. In business analytics, they often appear naturally.

We commonly see outliers in:

• Very high order values in sales data
• Unusually large discounts in pricing data
• Extremely high ad spend for one campaign
• Rare spikes in website traffic
• Abnormally long customer session durations

These values may come from VIP customers, flash sales, tracking errors, or exceptional campaigns.

How Outliers Affect Business Metrics

Outliers have a direct impact on commonly used metrics.

A single extreme value can:

• Inflate the average order value
• Distort revenue trends
• Stretch chart scales
• Hide the behavior of normal customers

For example, if most customers spend $50–$150 and one customer spends $15,000, the average revenue per customer becomes misleading.

Practice Dataset: Sales Transactions (Use This for Queries)

To make this article practical, here is a small dataset we will use in examples. You can copy this into your database.

Table name: sales_transactions

Columns:

• order_id
• customer_id
• order_amount
• order_date
• channel

Sample data:

INSERT INTO sales_transactions VALUES
(1, 101, 45,  '2025-01-01', 'Organic'),
(2, 102, 120, '2025-01-01', 'Paid'),
(3, 103, 75,  '2025-01-02', 'Organic'),
(4, 104, 60,  '2025-01-02', 'Referral'),
(5, 105, 90,  '2025-01-03', 'Paid'),
(6, 106, 55,  '2025-01-03', 'Organic'),
(7, 107, 130, '2025-01-04', 'Paid'),
(8, 108, 85,  '2025-01-04', 'Organic'),
(9, 109, 70,  '2025-01-05', 'Referral'),
(10,110, 15000,'2025-01-05', 'Organic');

Notice the last record. An order of 15000 is clearly an outlier compared to the rest.

How We Can Spot Outliers Using Simple SQL Exploration

The first step analysts usually take is sorting values.

SELECT *
FROM sales_transactions
ORDER BY order_amount DESC;

Explanation:

• Highest order values appear first
• The extreme value becomes obvious
• We can visually confirm unusual behavior

This simple step is often enough to identify suspicious data points.

How Business Context Helps Confirm Outliers

SQL alone does not tell us whether something is wrong. Business understanding completes the picture.

A $15,000 order might be:

• A bulk enterprise purchase
• A corporate annual plan
• A data error from tracking
• A duplicated transaction

This is why strong analysts never rely only on queries. They ask questions about the business context.

How Percentiles Help Explain Extreme Values

Percentiles help us understand where most data sits.

SELECT
PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY order_amount) AS p95_value
FROM sales_transactions;

Explanation:

• This returns the 95th percentile order value
• Any value above this is statistically rare
• It helps us quantify how extreme an outlier is

This method is widely used in sales and customer analytics.

How Averages Change Because of Outliers

Let’s compare the average order value with and without the extreme record.

SELECT AVG(order_amount) AS avg_with_outlier
FROM sales_transactions;

Then compare with:

SELECT AVG(order_amount) AS avg_without_outlier
FROM sales_transactions
WHERE order_amount < 1000;

Explanation:

• The average drops significantly when we exclude the extreme value
• This shows how strongly outliers affect metrics
• It explains why analysts must always check for extreme values

This exercise helps beginners understand the real impact of outliers.

How Outliers Can Actually Be Valuable Insights

Outliers are not always bad data.

Sometimes they represent:

• High-value enterprise customers
• Viral marketing campaigns
• Premium product purchases
• Exceptional customer behavior

In sales analytics, the biggest customers are often technically outliers, but they are the most valuable segment.

Good analysts investigate outliers instead of automatically removing them.

How Outliers Affect Dashboards and Reports

Dashboards often break when outliers exist.

Outliers can:

• Stretch Y-axis scales
• Make normal data appear flat
• Create confusing spikes
• Reduce stakeholder trust

This is why many BI dashboards apply filters, caps, or segmented views for extreme values.

How Outliers Are Handled in Real Analytics Work

In professional environments, teams usually define clear rules.

Common practices include:

• Flagging extreme values instead of deleting them
• Showing separate dashboards for high-value customers
• Using the median instead of the average for skewed data
• Documenting how outliers are treated in reports

This ensures consistency across the organization.

How Beginners Often Misunderstand Outliers

New analysts often assume every extreme value is an error. This is rarely true.

Outliers may represent:

• Real customer behavior
• Rare but valid transactions
• One-time marketing events
• Edge cases that reveal opportunity

Learning to interpret outliers thoughtfully is part of becoming a strong analyst.

How We Should Practice Understanding Outliers

The best way to build intuition is through practice.

We should:

• Sort data frequently and inspect extremes
• Compare metrics with and without extreme values
• Ask business-oriented questions about unusual records
• Practice with datasets like the one included in this article

This develops both technical and analytical thinking.

Final Thoughts for Freshers in Data Analysis

Understanding outliers in SQL for data analysis is not about writing advanced queries. It is about building analytical judgment. Outliers tell stories about exceptional customers, unusual campaigns, and hidden data quality issues.

When we learn to study outliers instead of fearing them, our insights become sharper, our reports become more trustworthy, and our value as analysts increases significantly. Learn how to detect and Remove Outliers in SQL for Cleaner Analytics in the next article.