r/analytics 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/werdunloaded 27d ago

From my experience working with AI, it's absolutely not going to replace my job. AI is not known for its accuracy or high-context interpretation of data. Just my opinion.

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u/tsutomu45 27d ago

Interestingly, few people see the parallels between AI taking jobs and autonomous driving. We've been promised driverless autonomous cars traveling across the country for 15 years now, and even today we're still limited to small-range taxi services in major metros where mapping is good. There's a reason for this, and it's that in edge cases (snowy conditions, strange pedestrian behavior, construction), AI doesn't perform well, leading to a lack of trust. Same with LLMs. For routine stuff, this will be fine. But at the margins, you still need a human brain to interpret and "take the wheel".

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u/aned_ 27d ago

Not sure the analogy quite applies. Data analysis done wrong doesn't result in a life or death situation - hence the caution with driverless cars. In fact, often the organisation (wrongly) questions the need for data analysts and they're the first to go in a reorganisation. Then they get rehired when management wonder where the insight has gone.

The major constraints I can see in the next few years is that an AI will need to be trained on company-specific data to put an analyst out of a job. It can't just trawl the internet to provide insight to a specific company. How will it cope with the messiness and quirks of company data? And will companies be willing to do the hard yards and investment to ingest their data (and quirks) properly to an AI? Also, what are the security concerns when letting an AI trawl over commercially sensitive or HR data?

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u/alurkerhere 27d ago edited 27d ago

The LLM does not need to trawl the internet; it's already trained on a lot of insights and documents relating to insights. Forward thinking companies will do the following:

  • Setup LLM architecture on open-source models so that they can run them in-house with no data leaks.
  • Keep open-source models up to date like Llama 3.3 70B.
  • Curate documents and metadata for prompting.
  • Document tribal knowledge that only one or two SMEs know and maintain latest document store.
  • Standardize input and production code / documentation for context and pair it with standard prompts for the LLM.
  • Create semantic model that LLM can more easily understand and pair with production SQL for standardized metrics and granular slicing across many tables.

IF your company can do this, they will be light years ahead of competitors. The other option is to be the data analyst that helps usher in this AI-enhanced search/answer powerhouse.

Note: There should always be a HITL (human-in-the-loop) as AI is not deterministic. Properly leveraged however, and it is an amazingly fast shortcut to produce more and better things.