10 Reasons SQL is Non-Negotiable for Data Analysts

Using SQL has not always needed for my roles even when a job description says it will be. When I first started analytics work, Microsoft products were the go-to. Think Microsoft Excel, Access, and of course, PowerPoint.

Many years later, Excel & PowerPoint are still powerhouses. I’m a senior analyst that’s been in corporate roles for years. While each have differences, how data is used and processed has more similarities than you’d think. More often than not, though, reporting & analytics is more of a product of ‘this is how things have always been done.’

Knowing what is possible is half the battle in growing as an analyst & what you can do with the tools. The same is true of SQL. You can do so much with spreadsheets. You can do even more if you layer in SQL into your toolset.

I have been on 3 interviews where SQL was a requirement of the role on the job description that didn’t pan out that way when I was in the job. In one of the interviews, SQL was never actually mentioned. In the other 2 interviews, it was at least brought up in the conversations. In all 3 of those roles, they didn’t actually use SQL. They just had an idea that they wanted it.

How did that work out?

• I worked with the IT department to put a data warehouse in place. Then we could connect Tableau workbooks directly for better automation & timely updates.
• I reluctantly built a data warehouse (which is not a strong skill of mine), but again – I could process large quantities of information on demand.
• In the 3rd, a data warehouse somewhat existed, but it was really the production environment the tech was based on. A true data warehouse was built. I mapped what we need and how, and the processes evolved with more robust reporting, again, on demand.

All 3 of these companies were primarily using Excel, but they recognized that to scale, it was time to level-up. They didn’t really know how, so they put buzzwords into the job description, and SQL served as a bridge to get them where they wanted to go.

Even if you find yourself in an organization that uses solely spreadsheets, I have a feeling there is more to that story. There are many reasons to learn SQL.

SQL is more than a querying language – it unlocks scalable data analytics & amplifies your career opportunities.

Learning SQL is step 5 of my recommendations on how to become an analyst, so the most obvious reason to learn SQL is to become an analyst. Here’s the path leading up to SQL, but it’s time to grab a cup of coffee☕ & dive into how learning SQL will help you grow as an analyst.

1. You’ll be able to handle large data sets, faster

As data sets grow, spreadsheets simply become impractical. SQL was built to handle a large amount of data, efficiently.

Over the years, Excel grew to handle more rows, but it’s still a pain to work with a spreadsheet with hundreds of thousands of rows. I experienced this today when I deleted ~200k rows from a spreadsheet that had look-up formulas to sheets from external workbooks. I immediately regretted it because it froze up trying to process. As my impatience & frustration grew waiting for Excel to do a basic function, I wasn’t even able to open emails in Outlook.

After a 20 minute walk, I returned to find Excel still working hard but going nowhere. I forced Excel to close & approached the problem differently.

Power Query can help, but it doesn’t solve all performance problems when trying to handle a lot of rows.

2. Data structure

I was an analyst for years working with Excel & Access. I used it to track employee performance, forecast the future and whatever report an executive could dream up.

Then I learned SQL, and that’s when I learned how data is structured.

I can definitely spot when it isn’t structured well now, at least! Having your data laid out well makes what you’re trying to do easier. Plus, inevitably you have to do layer in more data. Good data structure simplifies this.

SQL environments are generally relational databases. You don’t need to know SQL to work with Excel. BUT generally knowing how to structure data is key to repeatable & scalable analysis – even if Excel is your tool of choice.

3. You’re not limited to a couple of spreadsheets

SQL is excellent for manipulating data and combining it across sources. Need sales data from your CRM mixed with employee information from HR’s tool of choice? Yes, you can download the data and work some magic, but it’s really limited compared to what can happen when you bring in other tools.

Inevitably, you’ll find that you need to join data, and SQL can help you bridge the gap. A lot of data? SQL can handle it. Need to store & tie it together? SQL for data analysts makes quick work of it. Well, relatively quick anyhow.

In SQL, you can clean, transform & process data across sources in a repeatable, efficient way. This leads to another reason SQL for data analysts is a non-negotiable.

4. Automation is possible at scale

When you build the process to clean or join or anything else, that process is built to do it again when you execute – manually or automatically. Write the code once & barring any underlying data changes, you can run it or have it run as often as needed. Lather, rinse & repeat. This becomes a kind of magic when you’re talking about large data sets that bogs other tools down.

5. Complex Data Structuring for Analysis

There are times when you will get a data set that lists rows in a way where you need 1 row to tie to another in the same data set. Think of a client row associated with another client. Or how about a list of employees where 1 is a manager of another. Depending on how the data is set up, this scenario can be a pain.

This is a great time to break out subqueries, joins and windows functions to let SQL handle this. I find that if I’m tempted to break out Power Query in Excel, it’s time to move what I’m trying to do into SQL.

6. SQL integrates with all other data tools

SQL has been around for many, many years. It is a go-to for data handling. The perk of this status is that so many other tools integrate well with SQL. Even new tools bring SQL online for solid integration before other options. It’s the go-to standard.

I even learned SAS courtesy of PROC SQL where you can write SQL code & skip SAS-specific queries. Ultimately want a visual? SQL won’t do that, but you can connect to Tableau via SQL. Power BI. And even Excel.

And while SQL integrates well with other tools, read here for how SQL can be used FOR data analysis?

7. You open up on demand, real-time reporting

Data sets that are real-time tend to be HUGE! Whether you’re tracking ever second, minute, or half hour, other tools struggle to handle the volume of data. This is where SQL shines.

Connect a tool like Tableau through a SQL query that transforms the data to a digestible level of detail & BAM! 💥 You’re in business. With SQL queries in place & connected to the end user reporting, the reports will update seamlessly. Your intervention is just maintenance while anyone that needs the updates have what they need to make a difference in the org.

8. Learn 1 data querying language & the rest are easy

Even if you don’t see it, chances are there is a relational database somewhere around you. And SQL is the standard language for interacting with databases.

There are a few different flavors like Transact SQL vs Oracle SQL & even more. But whether you’re working with one that expects a . or a _, they have more similarities than differences.

So learning SQL can help you begin to interact with databases regardless of which one.

It’s even helped me learn tools like SAS. Thank you PROC SQL!

There comes a time when you’ll want to learn Python, R, or some other language, and having SQL under your belt will make learning the next language will be so much easier.

9. You’ll be able to talk the talk

SQL is so widely used across an organization with databases, so knowing SQL for data analyst also sets you up to have better conversations across your company and even within your team if there are varying skillsets. I find it difficult when I’m working with an area with a lot of unknowns to know what questions to even ask.

Knowing SQL can act as a bridge when you’re talking to an IT department or directly with a data engineer.

10. You’ll be a more marketable data analyst

I have not always leaned on SQL for data analyst roles at the start, but to get the interview I had to know SQL. Whether that’s right or wrong as it wasn’t a true expectation just doesn’t matter.

The hiring manager believed it to be an important skill, gave me work that didn’t use it, and I was able to use influence + my experience to get what I needed.

Plus, if you find a job that is just looking for Excel, chances are the pay isn’t as nice as one that has a broader skillset – like SQL for data analysis.

Fast Track to Learning SQL for Data Analysts

Every step you take to grow as an analyst is a HUGE win! From being able to talk the talk to making more money, learning SQL is a smart move that’ll pay dividends throughout your career. It is part of the path I recommend to become a data analyst when you’re ready to level-up.

Mastery of SQL doesn’t come easy, but I’ve turned the process into a power-packed, quick win style workshop to teach you about databases & SQL in just an hour. Seriously – you’ll be writing your own queries, without fear of crashing the database, in just an hour.

Grab the pre-launch special now for $10 to master the basics in less time than your evening commute.