“The data analyst is dead.” The claim that sparked some of the most heated debates in 2025.

What’s Really Happening to Data Analysts?

Let’s start the year with a bang.

We’ve all heard the chatter. The role of the data analyst is being questioned: some say it’s dying, others say it’s evolving.

In 2025, few topics were as emotionally charged in the data world as this one. I’ve been in many of those discussions myself - some productive, some more like battlefield debates.

So, to kick off 2026, let’s get real about what’s actually happening.

This newsletter is about:

• The real future of the data analyst role (and why it’s not as black and white as people want it to be)
• The shift towards leaner, more efficient data teams
• The role of dashboards in an AI-driven world - and what needs to change
• The rise of analytics agents and how they'll change the way we work

This isn’t just about the future - it’s about how to stay relevant, valuable, and ahead of the curve as a data professional in 2026 and beyond.

The Problem: Is the Analyst Obsolete?

If you’ve been in any data circles lately, you’ve heard variations of this:

“We don’t need analysts anymore - AI can do that now.”

This is not just theory, it's happening - and fast. It’s shaping hiring decisions, product strategies, and the future of data teams, especially in VC-backed startups and scale-ups. And here’s what’s making it so messy:

• Vendors are pushing “no analyst needed” tools.
• Founders and CEOs are starting to buy into it.
• Some analysts feel under attack. Others are pivoting.

Two camps have emerged:

• One says: “The analyst is dead - AI replaces them.”
• The other argues: “The analyst is more essential than ever - AI can’t replace business context or critical thinking.”

Both have valid points.

But here’s the truth: The role is not dying. It’s evolving.

The Evolution: Where the Analyst Role is Going

1. Data Teams Will Get Leaner

Oversized data teams are out. The pressure for efficiency has been real even before the AI wave. And it will get worse before it gets better.

In the future, expect:

• Fewer traditional analysts
• More hybrid roles (like full-stack analysts or analytics engineers)
• Stronger emphasis on business impact and decision-making

Companies want analysts who don’t just deliver dashboards - but who understand the business problems and drive action.

I recently had an interesting conversation with a well-known CEO and serial Founder who built some of Europe's most successful internet companies (and who requested to remain anonymous) and he said it clear: "I don't hire any traditional analysts anymore. Everyone we hire has to know the business and must be able to know how to code."

2. The Analyst’s Skills Need to Go Beyond SQL + Viz

Let’s be blunt: knowing SQL and building pretty dashboards isn’t enough anymore (if it ever was).

Analysts need to:
→ Understand the why behind the numbers
→ Ask better questions
→ Translate messy business problems into structured analytical thinking
→ Work closely with business leaders, not just other data folks

And yes: this was true before AI. But now, AI is accelerating the shift.

3. Two Future Paths for the Analyst

The “classic” analyst is disappearing. But that doesn’t mean there’s no future - it just means it’s branching.

Here’s where analysts are heading:

• Towards analytics engineering: Building semantic layers, enabling analytics agents, maintaining structured data environments
• Towards business decision-making: Becoming business domain experts who know data, business context, and make business decisions

My recommendation: Pick your lane - or get stuck in the middle.

The shift is comparable to the wave of Data Scientists that became data engineers when they realized that they are lacking the foundations for their algorithms a couple of years ago.

Dashboards: Still Here, But Misused

Let’s talk dashboards - a key data product produced by data analysts.

I’ve been vocal for years: most dashboards answer what happened, but people wrongly expect them to answer why.

That’s a big problem.

Even worse, teams often drown in dashboards without clear priorities. Here’s how I believe dashboards will be used in the future:

• Outcome dashboards:
• Track goals you can’t control directly
• e.g. revenue, profit, customer growth
• Input dashboards:
• Track what you can control
• e.g. emails sent, sales calls made, feature releases

Every business domain should have:
→ 1 Outcome dashboard
→ 1 Input dashboard
→ A clear link between the two

Not more. Not 768 dashboards no one reads.

If you can’t map inputs to outcomes, your dashboards are noise.

The last puzzle piece: The Analytics Agent

Here’s where it gets exciting. Enter analytics agents.

What is an analytics agent?

I strongly believe that conversational analytics is one of the most overhyped topics in the data domain. There's not a day where I'm not approached by a new vendor in this space.

Most of those vendors get it wrong. A chatbot that just answers questions about your KPIs is not going to cut it.

You need an AI-powered assistant that:

• Understands company goals
• Knows which projects or initiatives are running
• Has access to structured and unstructured data (e.g. access to conversations with customers, hypothesis about improving company goals)
• Monitors leading indicators (input metrics) and lagging indicators (outcomes)
• Flags risks and suggests actions before goals go off track

These agents won’t just answer questions. They’ll proactively push insights and even implement these insights (e.g. setting up new landing pages or launching new marketing campaigns).

But there’s a catch: they only work on top of a solid semantic layer combined with unstructured data.

The New Roles in Future Data Teams

To support this new setup, data teams will shift in structure:

You’ll need:

• Analytics Engineers​
  → Responsible for building and maintaining the semantic layer
  → Ensuring data is reliable, structured, and ready for AI agents

• Business Decision-Makers​
  → Work with analytics agents
  → Interpret signals, test initiatives, and make decisions with confidence

• Fewer classic analysts, and more people who can own end-to-end questions and drive action

In short, AI isn’t replacing analysts - it’s changing what being a great analyst looks like.

Why Big Companies Will Lag

Change won’t happen at the same pace everywhere.

Expect this shift to happen faster in:

• VC-backed startups
• Tech-forward scaleups

Slower adoption in:

• Larger corporates
• Risk-averse industries

But it will happen. The smart move is to prepare now.

The Bottom Line

Here’s the takeaway for 2026:

The analyst isn’t dead. The old analyst is.

The future of data work is:

• Leaner teams
• AI-enhanced workflows
• Fewer dashboards
• More strategic thinking
• A shift from reporting to decision enablement

If you’re a data analyst today, ask yourself:

• Am I moving towards analytics engineering or business decision-making?
• Am I helping my company connect actions (inputs) to goals (outcomes)?
• Am I preparing to work with AI agents, not compete with them?

The good news? The future is full of opportunity.

But only for those ready to evolve.

Until next week.
Sebastian