The role of SQL in Data Analytics: Why Every Analyst Should Learn SQL

Dennis-Kirimi — Tue, 11 Mar 2025 14:20:10 +0000

Introduction

Where is this coming from?

Data has become the main factor any time a person or organization is making decisions or insights. This calls for an analyst who is able to manipulate, transform and extract insights from this data.

Any time an analyst is interacting with data, there are a number of tools and languages available to complete the tasks. SQL is one of them!
SQL, Structured Query Language, is a standard language used for managing and manipulating relational databases. This is a must learn language whether you're a beginner or a professional in the data field.

The role of SQL in Data Analytics

We've just mentioned that SQL is used for interacting with relational databases, right? SQL remains to be the unsung hero of data analysis.
Data is stored in databases. A database is an organized collection of data stored electronically. The data could be in form of tables with rows and columns. But how do we interact with the data in the databases?
SQL helps analysts to retrieve, clean and manipulate data with ease.

But Why Should Every Data Analyst Learn SQL?

1. SQL helps an analyst get the data you need from a database for your analysis.

Can you image of a company that is over 20 years old? Yes, majority of this business data is stored in relational databases such as MySQL, PostgreSQL, and SQL Server. SQL helps you, as an analyst, pull the required data, it could be last year's sales data and perform further analysis without having to retrieve the others.

2. SQL is built for interacting with large datasets.

Excel might work for small reports. But what if you're dealing with millions of rows of data? That's where SQL comes in again, hurray!
SQL makes the interaction with large datasets easy and pardons the computer from crashing.

3. Data Cleaning

Most of the times, data comes in a dirty format which includes missing values, duplicates among others. SQL gives you a fair room to use in-built functions to clean the data and prepare it for visualization. This could mean removing duplicates or replacing missing values with the most appropriate values depending on the analysis.

4. Merging different tables

In databases, data is stored in schemas and tables holding different data and the data in those tables could be related.
With a simple query, one can join the tables and create a view to perform further analysis. This can hardly be achieved if using tools like Excel.

5. SQL integrates with a number of popular visualization tools

After you're done interacting with the data from a database, the next step is always visualizing or creating reports.
SQL gives a direct pipeline to connect with those visualization tools like Power BI or Tableau.

6. SQL ensures collaboration across teams.

In most organizations, SQL is a central and common language in the data department. Data analysts, engineers and scientists often communicate through data pipelines and majorly databases. Databases and no SQL?? Doesn't make sense!

SQL is more than just a language. It's a critical domain sensitive skill that every analyst should learn whenever interacting with databases.

If you haven't started learning SQL yet, now is the perfect time!!

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