Did You Know? – 3 Quick SQL Tricks Every Analyst Should Know (But Probably Doesn’t)

SQL is one of the most essential skills in the modern data analyst’s toolkit. From querying customer records to building financial reports and tracking operational KPIs, SQL underpins nearly every data-driven decision in an organization.

But even experienced analysts tend to rely on the basics—SELECT, JOIN, WHERE, GROUP BY—without realizing that SQL offers powerful shortcuts, functions, and enhancements that can make analysis faster, cleaner, and more effective.

At PatMacTech UK Ltd, we believe in working smarter with data. In this edition of our “Did You Know?” Data & Tech Tips series, we’re sharing three underrated SQL tricks that can instantly upgrade your queries and improve the way you work with data.

✅ 1. Simplify Conditional Logic with `IIF()`

Most analysts are familiar with CASE WHEN statements for handling conditional logic in SQL. But for simple true/false or binary conditions, IIF() is a faster, more readable alternative.

🔹 Example:

sqlCopyEditSELECT 
    FullName,
    IIF(IsActive = 'Yes', 1, 0) AS IsActiveFlag
FROM Employees

🔹 Why It Matters:

Cleaner syntax than CASE for simple decisions.
Makes dashboards easier to debug and maintain.
Reduces the likelihood of typos or logic errors.

🔹 Real-World Use Case:

In HR or housing systems, transforming “Yes”/“No” fields into 1/0 flags simplifies integration with Power BI and Excel pivot tables. This is especially useful when preparing data for machine learning models or conditional formatting in reports.

💡 Supported in SQL Server 2012 and later.

✅ 2. Aggregate Text Values with `STRING_AGG()`

One of the most frustrating problems analysts face is flattening multiple rows into a single string—especially when grouping by a parent entity (e.g., listing all services linked to a client).

Before SQL Server 2017, analysts used FOR XML PATH hacks or messy subqueries. Now, STRING_AGG() makes this elegant.

🔹 Example:

sqlCopyEditSELECT 
    ClientID,
    STRING_AGG(ServiceName, ', ') AS ServicesUsed
FROM ClientServices
GROUP BY ClientID

🔹 Why It Matters:

No need for complex XML or string concatenation.
Cleanly returns comma-separated values (or any delimiter).
Saves time when preparing reports that require text summaries.

🔹 Real-World Use Case:

In housing analytics, you might want to list all repairs associated with a tenant or group survey responses in a single row per user. STRING_AGG() makes this simple and readable.

💡 Great for Power BI integration, where grouped text can be displayed in tooltips, cards, or table visuals.

✅ 3. Use `TOP N WITH TIES` for Fair Rankings

Analysts often need to extract “top 10 performers,” “top 5 spenders,” or similar filtered lists. But what if two or more entries tie for 5th place?

Instead of manually adjusting the TOP limit, WITH TIES automatically includes all tied entries based on the ORDER BY clause.

🔹 Example:

sqlCopyEditSELECT TOP 3 WITH TIES 
    CustomerID, TotalSpend
FROM CustomerSpending
ORDER BY TotalSpend DESC

🔹 Why It Matters:

Ensures fairness in rankings.
Prevents data exclusion when tied values exist.
Particularly useful in HR, sales, education, and sports analytics.

🔹 Real-World Use Case:

When reporting the top 5 housing officers by resolved cases, WITH TIES ensures those with the same case count aren’t left out—maintaining trust and transparency in performance reporting.

💡 Also useful for public leaderboard-style data where tie-breakers aren’t appropriate.

🧠 Bonus Trick: CTEs (Common Table Expressions) for Reusability

CTEs let you write modular, reusable code inside a query. They act like temporary named subqueries that make your SQL more readable and maintainable—especially for layered logic or reusable filters.

🔹 Example:

sqlCopyEditWITH ActiveUsers AS (
    SELECT * FROM Users WHERE Status = 'Active'
)
SELECT * FROM ActiveUsers WHERE CreatedDate >= '2024-01-01'

🔹 Why It Matters:

Breaks complex queries into digestible parts.
Enables cleaner joins and conditional logic.
Makes troubleshooting much easier.

💡 Essential for long stored procedures and analytics pipelines in BI projects.

🎯 Why These Tricks Matter for Your Organization

At PatMacTech UK Ltd, we don’t just build dashboards—we build data confidence. SQL is often the engine behind BI platforms like Power BI, SSRS, and Azure Data Factory. These little-known tricks can:

Reduce reporting load times.
Make queries easier to audit and scale.
Help non-technical teams understand what’s going on under the hood.

When your queries are efficient, your insights are faster—and your decisions are better.

🚀 Want to Get Better at BI?

Whether you’re an analyst, team leader, or business owner, our tailored data analytics training and custom BI solutions can help you:

Automate reporting
Visualize performance
Make better, faster decisions

👉 Explore our services and see how we help businesses unlock the power of their data.

📌 Stay Tuned for More

This article is part of our “Did You Know?” content series. Be sure to follow us for weekly tips on:

Power BI
SQL
Automation
Reporting best practices

Have a specific BI or SQL question? Email us at info@patmactechuk.net or drop us a message on LinkedIn.