“The most valuable data leaders of the next decade may never become Chief Data Officers.”

That might sound like a strange thing to say in a newsletter about data strategy and data leadership.

But hear me out.

For years, I assumed my natural career progression looks like this:

Analyst → Head of Data → VP Data → Chief Data Officer.

Climb the ladder. Build a bigger data team. Own more dashboards, pipelines, and models.

AI is about to break this trajectory.

And ironically, it might finally unlock the real potential of data professionals.

Let’s break it down.

The Real Skill That Will Survive AI

For the last years, the value of data professionals came largely from technical scarcity.

You needed people who could:

• Write complex SQL
• Build pipelines
• Train models
• Maintain infrastructure

Most business teams simply couldn’t do this themselves.

But that barrier is disappearing fast.

AI can already:

• Write SQL
• Generate dbt models
• Connect APIs
• Build dashboards
• Create semantic layers

Soon, many technical tasks inside data teams will become commoditized.

Which raises an uncomfortable question:

If everyone can access the data…

What exactly is the role of the data team?

My answer has been the same for years:

The real skill of great data people was never writing SQL.

It’s decision making.

Framing problems.
Formulating hypotheses.
Designing experiments.
Taking action.

And now something interesting is happening.

When Data Leaders Start Owning Marketing Budgets

A VP Data in my 10X Data Team community who works for a global consumer subscription scale-up recently shared something fascinating.

Next to his role as VP Data, he has now also become Head of Programmatic Marketing.

Meaning:

He still runs the data function.

But he now owns a sizeable portion of the marketing budget.

Within a week he onboarded himself to two ad networks.

Then he used Claude Code to build an API that allows him to manage ad spend via chat.

He can literally write:

“Increase spend on campaign X by €300 and add these creatives.”

And the system executes it.

But the interesting part isn’t the automation.

It’s the data mindset he applied.

He added logging to every action the system performs.

Why?

Because in many marketing tools, such as Google Ads, actions taken inside the interface - budget changes, creative swaps, campaign pauses - are not properly logged.

Which means you often can't reconstruct which decision led to which outcome.

By logging every action, he will now have a dataset that shows:

• which decision was taken
• when it was taken
• how ROAS changed afterwards

That’s a very data person instinct.

But the boldest decision was about his team.

He decided that marketing analysts will no longer just analyze campaigns.

They will own parts of the marketing budget themselves.

Not “provide insights”.

Own the spend.
Own the ROAS.
Own the outcome.

This Isn’t New - But AI Makes It Easier

When I read his story, it reminded me of something from much earlier in my career.

Fourteen years ago I was leading the data team at Rocket Internet, the world’s largest venture builder at the time.

One of my responsibilities was A/B testing across our portfolio companies.

But our job wasn’t just to calculate statistical significance.

We were responsible for:

• designing the experiments
• running them
• and ultimately delivering measurable business impact

If a test increased Revenue per visitor by 12%, that was our success.

If it didn’t move the metric, the experiment failed.

In other words: we didn’t just analyze the business.

We changed the business.

And in my opinion, this has always been the right model for data teams.

Why I’ve Always Been Skeptical About the CDO Role

If you've been following me for a while, you know that I’ve always had mixed feelings about the Chief Data Officer role.

Not because the role is useless.

But because in my view it should be transformational, not permanent.

The job of a great CDO is not to own data forever.

The job is to make the organization capable of using data without them.

In practice that means turning the executive team into data-driven operators.

Making the CEO, CMO, COO, and CFO into Chief Data Officers themselves.

Once that happens, the transformation is complete.

Which leads to a provocative question.

If your goal is to drive business outcomes with data…

why not become the CMO, COO, or CFO yourself?

The Future Career Path for Data People

Instead of the traditional path:

Head of Data → VP Data → CDO

We may increasingly see:

Data → Growth leader
Data → Product leader
Data → Operations leader

Because the real strengths of data professionals are:

• systems thinking
• experimentation
• quantitative decision making

Those are exactly the skills needed to run business functions.

If I were to go back into a traditional career path, I would 100% go down that path and aim to become a CMO, CPO or CEO - not a CDO.

What This Means for Data Leaders

If you lead a data team today, this shift has consequences.

Move your team closer to outcome ownership.

Not just:

• dashboards
• pipelines
• models

But metrics with accountability.

Examples:

• analysts owning marketing channels
• data scientists owning churn or conversion metrics
• engineers owning infrastructure cost efficiency

The closer your team gets to real business outcomes, the more valuable it becomes.

The Bottom Line

The most impactful data professionals of the next decade may not sit inside a data department at all.

They will:

• run marketing budgets
• own product metrics
• manage operational performance

Because when AI writes the SQL…

The real question is no longer:

Who can access the data?

The real question is:

Who owns the decision?

And the people who own the decisions will shape the future of data.

Cheers,
Sebastian