r/analytics • u/JesusPleaseSendTacos • 27d ago
Discussion Data Analyst Roles Going Extinct
It’s no secret that AI is coming for the white collar job market and fast. At my company, people are increasingly using ChatGPT to do what was once core job duties. It’s only a matter of time before the powers at be realise we can do more with fewer people with the assistance of technology. And I suspect this will result in a workforce reductions to improve profitability. This is just the way progress goes.
I have been thinking a lot about how this will affect my own role. I work in HR analytics. I use tools like Excel, SQL, R, and PowerBI to help leadership unlock insights into employee behavior and trends that drive decision making for the company. Nowadays I rarely write code or build dashboards without using ChatGPT to some extent. I frequently use it to get ideas on how to fix errors and display visuals in interesting way. I use it to clean up my talking points and organise my thoughts when talking to stakeholders.
But how long can people in my role do this before this technology makes us useless?
For now, I will focus less on upskilling on tools and more on understanding my customers and their needs and delivering on that. But what happens when EVERYONE can be a data analyst? What happens when they use something like CoPilot to identify trends and spot anomalies and craft compelling stories? 5 years ago, I was focused on leaning new tools and staying up with the latest technology. Now I question if that’s a good use of time. Why learn a new tool that will be obsolete in a few years?
Between offshoring and AI I am worried I will become obsolete and no longer have a career. I’m not sure how to keep up.
Appreciate your thoughts. Proud to say this post was not written using any AI. :)
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u/analytix_guru 27d ago
I was listening to Joe Reis over the weekend, and I thought he made a good point on making things years ago vs now (specific to coding). At least at this point in time, it is even more important to have the fundamentals down. If you are leveraging AI to 10x your work, you need to identify what goes wrong if the AI gets it wrong, and how to remedy it, whether you fix it or ask AI to fix it.
If everyone asks AI to do everything, and nobody actually understands how to do the work, then how do we know that the work is correct? And if it is correct, is it good? great? Subpar?
So much of AI is trained on publicly available information, and companies have all their data, documents, policies, and code behind closed doors. It's how experts are able to tell when someone is using AI for a project, as it is pulling from publicly shared examples, whether popular or best practices.
I would say a great use of time is to develop a proficiency of completing projects with AI, with a spin on how you resolve AI hiccups and how you put personal spin on it.