With all the buzz around Artificial Intelligence (AI), it’s hard not to notice something curious—many of the bootcamps that once sold Data Science and Analytics as the golden path are now shifting their focus to AI and Machine Learning.

This shift has sparked a very real question for many professionals and aspiring data folks: Is AI taking over analytics? Is there still a viable career path in data? The Short Answer? Yes—But It’s Evolving. Let’s be clear: data is still the crude oil of the digital economy. AI doesn’t replace data—it runs on it. But what has changed is the way data professionals need to think about their roles, skills, and future.

Back in 2018, I wrote a post titled A Newbie’s Guide to Big Data, which was essentially a journal of how I found my way into the data space. I was surprised to see that it still gets views today, which tells me this journey is still relevant to many. But given how much the landscape has changed, it felt like the right time to revisit and update that conversation.

Data Refinery?

The truth is, data still needs refining. It must be collected, cleaned, secured, governed, interpreted, and used responsibly.

According to Gartner, by 2025, 75% of organizations will shift from piloting to operationalizing AI—highlighting the growing demand for roles that bridge data, governance, and automation.

With AI now in the mix, the stakes are even higher. New challenges arise around privacy, transparency, and ethical use, opening fresh opportunities in areas like:

• Data Security & Privacy — Compliance, encryption, access controls, and ethical usage.
• Governance & Compliance — Managing bias, ensuring traceability, and meeting regulations.
• AI-Enabled Analytics — Using AI to assist—not replace—data storytelling and insight generation.
• MLOps & DataOps — The evolving infrastructure to scale AI responsibly.

There’s no one-size-fits-all path. Many of us already apply data thinking in our day-to-day work—whether that’s in spreadsheets, dashboards, customer insights, or writing SQL queries.

How Can You Upskill in Data During This AI Era?

If you’re wondering how to stay current and relevant, consider the following paths:

• Learn Prompt Engineering: Understanding how to work with AI tools like ChatGPT can help you accelerate analysis, automate tasks, and craft better insights.
• Explore Responsible AI & Governance: New ethical and legal frameworks are emerging—especially around data used to train models. Governance is a growth area.
• Get Familiar with Tools like Microsoft Fabric, LangChain, or Hugging Face: These tools bridge traditional data work with modern AI pipelines.
• Take AI-Focused Data Courses:Look for certifications or short courses in MLOps, Explainable AI (XAI), or privacy-preserving machine learning.
• Build Something: Whether it’s a smart dashboard, a chatbot, or an automated insights generator—real-world projects help you stand out.

Final Thoughts: The Edge is in the Integration

AI hasn’t replaced analytics—it has elevated it. The professionals who learn to leverage AI in their data workflows will have the edge. Not because they know it all, but because they’re willing to keep learning and adapting.

And that’s the real opportunity: pivoting, not panicking.
The world still runs on data—and now, it needs smart people who can make sense of it in a smarter world.