How data analysts can leverage AI to work more effectively

The fun part of being a data analyst (and the sometimes not so fun part!) is that there’s always new tools and skills to learn.

Just when you start to feel comfortable and like you at least somewhat know what you’re doing, there are new advancements—which means more learning for you.

Sure, you can continue on as you were before and stick with all your current tools and skills, but you’ll eventually be left behind.

As the world of technology changes, so does the work of a data analyst. And it’s important to keep up to stay relevant and help you work more effectively.

What is AI?

So what even is AI anyways?

Nowadays, AI is such a buzzword that it’s a little hard to tell..

People are eager to slap the AI title onto every product and team.

Why? Because it’s sexy and it sells.

But AI itself isn’t a new concept, it’s actually been around for a long time.

What’s really caused all of this buzz though is the democratization of AI to everyday consumers through Generative AI chatbots such as Open AI’s ChatGPT and Google’s Gemini.

These tools are able to not just retrieve information but adapt and respond in a way that it generates new information based on the data it was trained on and the inputs of consumers.

Imagine you’re looking for a dinner restaurant that meets your requirements for location, cost, and the type of cuisine, and you’re able to get a detailed answer with a list of options personalized to you.

This saves you minutes of time scrolling on Google and social media to conduct your research. Now imagine this power and time saving in the scope of Data Analytics.

Here’s 3 ways I use Gen AI tools daily to support my work as a data analyst and be more efficient.

1. Brainstorming

Brainstorming is perhaps one of the simplest use cases where AI can be implemented into almost anything.

Imagine you need a list of ideas to supplement your own, gain unique perspectives, and spark your creativity.

I use AI tools to help me brainstorm all sorts of things as a data analyst, for example:

• What metrics should I use to track retention?
• What are some of the pros and cons of each data visualization tool?
• How can I optimize my SQL queries and write better code?
• How could I write a query that does X for Y?

AI tools are able to do the research for me, and save me so much time from opening 100s of Google tabs to answer basic questions.

Of course you have to take everything with a grain of salt and not forget to use your human brain, but brainstorming with AI can give you a higher starting point for a project.

2. Explaining code

Have you ever gotten code sent over from a coworker (or worse, a super old and unmaintained legacy query) and you have no idea what it does?

Maybe it uses a bunch of nested subqueries or it’s super complex and long and would take you a long time to read through and figure it out.

You could ask AI to explain it to you in only a few seconds and you’re left with a high level summary of what the code does.

Of course, you should always be careful with sensitive company info.)

Sometimes, I’ll even get crafty with my prompt engineering and ask AI to “explain X concept or code to me like I’m 5 years old”.

That’s the real beauty of AI—the more specific you are, the better tailored results you can get back.

3. Debugging

Ah, bugs and error messages—a coder’s worst nightmare. If you haven’t spent an hour looking for a bug to find out it was a missing comma or bracket, can you even call yourself a coder?

Luckily AI tools can be super useful to help us find and fix those pesky bugs and even explain why our code isn’t working.

This can be a huge time saver and save you from many headaches.

If your code is ever not running or not passing QA for some reason, AI can be a great tool to resolve the issue—and sometimes humble you!

It’s hard to even believe that I did Data Analytics before all of the AI hype that started in 2022 with the launch of ChatGPT, which democratized AI into the hands of consumers—both technical and non-technical.

A lot of people claim that AI will replace data analysts, but a true analyst knows that coding is the easy part of the job.

The hard part is dealing with stakeholders and applying the data with business acumen.

These are soft skills that make it impossible for AI to replace us.

AI is not our competition, it’s our friend.

AI tools are something all data analysts should embrace because although it can’t replace data analysts skills wholesale, data analysts who use AI will replace data analysts.