So, Is Data Analytics Dead?!

So, Is Data Analytics Dead?!

The tools are not the differentiator anymore. Here is what is.

Last week someone commented under my Youtube:

This message gave me mixed feelings, because part of me strongly disagreed, and other part of me got very scared, as I also see the change in the profession. So is data analysis actually disappearing? Let’s talk about it.

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We are generating more data than ever

If anything, the demand for people who can work with data is growing, not shrinking.

Every business transaction, every customer interaction, every click, every sensor reading — all of it gets stored. The amount of data being created globally is not slowing down. Companies are sitting on more of it than they know what to do with.

The problem is not a shortage of data. It is a shortage of people who know what to do with all of this information.

That has not changed. But something else has.

What actually changed

Here is what I noticed. The tasks performed by junior analysts are now done by AI: writing code, building a dashboard, pulling an ad-hoc report. It all can be either done with AI assistance way faster than before, or it is fully automated.

Entry level roles have been hit the hardest by this.

A few years ago, knowing SQL was enough for a junior data analyst. Then PowerBI/Tableau became nice to have. Then Python became a differentiator.

The tools are no longer what set you apart.

So is there a differentiator now?

Listen, this is my opinion, but I think soft skills became the differentiator: business knowledge, domain understanding, communication, storytelling. And I think it is time to start niching down.

The case for niching early

This is where I want to be honest with you, including about my own experience.

Throughout my career I moved across industries.

I started at KPMG, where I worked in Consumer sector: FMCG, automotive, advertising and others. Then FP&A at Marks & Spencer and Gap, which is pure retail. Then I worked in gambling. Then a consultancy that worked with anyone who was willing to pay. Then telco.

Every time I moved, I felt like I was starting from scratch. The tools, the analytical thinking carried over.

But the business context — the terminology, the KPI, the way decisions get made — that I had to rebuild each time. And it is very hard, when you join the meeting and everyone is looking at you asking is it STD or IDD and confused you have no idea what they are talking about.

For someone entering the market today, I think there is a real argument for picking a lane early.

Choose an industry — finance, FMCG, insurance, retail, healthcare — and go deep. Learn the acronyms. Learn which metrics the business actually cares about. Learn how budgets get set and how performance gets measured. Even as a very junior analyst, if you already speak the language of the industry, you are ahead of most of your competition.

That specificity is hard to replicate quickly, even with AI.

One path worth considering: start at a Big Four

If you are early in your career and the idea of committing to one industry feels too narrow, there is a middle path worth knowing about.

The Big Four accounting firms — Deloitte, PwC, EY, KPMG — give you exposure to multiple industries while you are technically working at one company. As an analyst or consultant there, you might work with a bank one quarter and a retailer the next. You build breadth without having to job-hop, and you build it fast.

I started at KPMG, and the exposure I got there to different businesses, different problems, and different ways of working was genuinely formative. It is a demanding environment, but for someone who wants to learn quickly, it is hard to beat.

What to do with this

If you are building your career in data right now, here is how I would think about it practically:

Pick an industry and start learning it. Not just the data side — the business side. Read industry publications. Follow the news in that sector. When you look for datasets to practice on, choose ones from that industry. When you build portfolio projects, frame them around questions that business would actually ask.

Target your job search. Once you have chosen a vertical, look for analyst roles inside companies in that space, or for consultancies that specialise in it. The more you can demonstrate that you understand their world, the stronger your application.

Business knowledge takes time to learn, so start early. It becomes a genuine advantage that compounds as your career progresses.

Data analytics is definitely not dead. But knowing the tools is not enough anymore. The question is what you bring to the data that AI cannot.

Keep pushing 💪,

Karina

This week on YouTube

How to create a data science portfolio - which projects to include, how README should look like

https://youtu.be/0F6JovxjrE8

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