Many teams today rely on data, but not everyone can work with it. SQL remains one of the most necessary skills for anyone who deals with information — analysts, marketers, finance teams, product managers, and even non-technical roles.And the numbers prove it:
  • SQL has ranked as one of the most in-demand skills on LinkedIn for several years.
  • In a 2024 Stack Overflow Developer Survey, SQL appears among the top 3 most widely used languages across the world.
Even with the rise of AI and automation tools, SQL hasn’t faded. It became more important. AI can help you write SQL faster, but you still need to understand what you’re asking, why you’re asking it, and how to read the results.This article explains why SQL still matters for every team and how AI can make the learning curve shorter.

4 Reasons Why learning SQL for data analysis is a MUST

1. SQL Is the Base Layer of Most Company Data

Even if your team uses dashboards, BI tools, or automated reports, everything underneath is usually powered by SQL.That includes:
  • Power BI
  • Tableau
  • Google Data Studio
  • Excel data models
  • Snowflake
  • BigQuery
  • Microsoft Fabric
  • Any relational database running your operations
When something breaks, or when you need a specific slice of data, someone has to open the hood — and that requires SQL.Without this skill, your team will always wait for “someone technical” to extract a simple answer.

2. SQL Is Still One of the World’s Most Used Languages

Many people think SQL is outdated. The stats say the opposite.In the Stack Overflow 2024 survey, SQL ranked as the third most-used programming language worldwide.This tells us one thing:SQL is not dying. SQL is aging well. And every analyst who wants to stay relevant needs to be comfortable with it.

3. SQL Makes Your Team Faster (and More Independent)

Teams lose a lot of time waiting for data:
  • Waiting for IT
  • Waiting for engineering
  • Waiting for BI teams
  • Waiting for the “data person” to be free
Usually, the question is simple:
  • “How many customers bought product X last week?”
  • “How many orders failed?”
  • “Which region is dropping in revenue?”
When your team knows SQL, they stop waiting. They get answers immediately. And this saves hours every week.

4. Companies Prefer Employees Who Know SQL

According to a 2024 analysis of 1,000 job postings by 365 Data Science, SQL is mentioned in 52.9% of data analyst roles. That makes SQL the most frequently required programming skill, even ahead of languages like Python.What that means:
  • More than half of all advertised data analytics roles expect candidates to know SQL.
  • If your team knows SQL, they match a large portion of what employers ask for — across fields like marketing analytics, finance, operations, and many business analytics roles that rely on relational databases or structured data.
  • This makes team members more flexible — able to handle tasks from data extraction and cleaning to reporting and analysis.

AI Makes Learning SQL Easier Than Ever

New tools like:
  • Microsoft Copilot
  • ChatGPT
  • BigQuery’s AI Studio
  • Snowflake Copilot
  • GitHub Copilot SQL
…allow beginners to write good SQL by asking natural-language questions.But here’s the key insight from multiple AI studies:AI works best when the human already understands the language.A recent report found that productivity increases only when users understand the structure of the data and know how to refine AI-generated queries.This means AI will not replace SQL learning. It will speed it up. Instead of taking months, someone can now practice SQL with AI side-by-side and correct mistakes instantly.

The Practical Skills Your Team Actually Needs

To work with SQL comfortably, your team doesn’t need to become engineers.They need the fundamentals:
  • Knowing how data is structured
  • Writing simple SELECT, WHERE, GROUP BY queries
  • Understanding joins
  • Filtering and aggregating data
  • Checking and validating results
  • Turning queries into reports
AI can help with syntax, but humans need to understand the context, the business logic, and what question they are trying to answer.

How Teams Can Reduce the SQL Learning Curve

Here are simple steps:

Step 1: Start with real company data

Learning becomes easier when the team sees examples from your own work environment.

Step 2: Use AI to draft SQL

Don’t teach SQL with random sample tables.Use the data your team already sees every day — orders, customers, transactions, and marketing results.When people work with familiar numbers, they understand problems faster and stay engaged.

Step 3: Practice debugging queries

Your team doesn’t need to write perfect queries from scratch. They can ask tools like ChatGPT or Copilot to create a starting query:“Show total orders per day for the last 30 days.”Then they review and adjust it. This removes the fear of a blank screen and helps them learn patterns faster.

Step 4: Learn the data model

Debugging teaches more than writing. Your team should practice things like:
  • Why a JOIN created duplicate rows
  • Why does a number in SQL not match the dashboard?
  • Why did a filter remove too many records?
These situations happen in real work. Solving them builds real intuition.

Step 5: Build a small project

SQL is much easier when people know:
  • Which tables exist
  • What each column means
  • How tables connect
Once they understand the structure, writing SQL becomes straightforward.

Why Now Is the Best Time to Train Your Team

Data is not slowing down. AI is accelerating it.Teams that cannot read or manipulate data fall behind much faster today.SQL will stay with us for many years. And teams who understand it will always have the advantage.The Data Analysis & Business Intelligence Diploma from IMP  gives your team hands-on, practical SQL skills — not theory.Your team learns:
  • How to write SQL for real business problems
  • How to join multiple data sources
  • How to clean and prepare data
  • How to use SQL with Power BI, Excel, and Microsoft Fabric
  • How to apply SQL in analytics, reporting, automation, and dashboards
They also learn how to use AI tools effectively to:
  • speed up SQL writing
  • analyze results
  • debug queries
  • build insights faster
By the end, your team can work independently, answer their own questions, and support your business decisions with real data.If you want your employees to master analytics quickly and confidently, SQL is the right place to start — and this diploma makes the journey easier.