Welcome back for 2026!

I recently came across two data science services being sold online, both by individual consultants working remotely.

The first (on Fiverr) promised: "I will build you a dashboard from your data that meets your specifications."
Price: $100

The second was a half-day "check-up" of a company's data capabilities, described as: "suitable for internal use when making strategic choices."
Price: $10,000

Same type of consultant. Same general skill set. 100X difference in price.

Now, you could argue the difference was all due to experience - a beginner vs a seasoned pro.

But that misses the point. I've seen plenty of data scientists with 20+ years' experience who are still selling the equivalent of $100 dashboards.

Meanwhile, less experienced consultants who position themselves strategically are commanding significantly higher rates.

Experience helps. But it's the type of work that ultimately determines the value.

Strategic vs implementation.

Here's what I mean:

The $100 service is purely implementation. You tell them exactly what you want, they build it, hand it over. Done.

There's no strategic thinking. No consideration of whether this solves your actual problem. No transformation of your business.

You're literally just buying a set of hands to execute your specification.

The $10,000 service is entirely strategic.

The buyer doesn't know if their organisation has a functional data operation or a disaster waiting to happen.

That uncertainty could cost them millions.

The consultant conducts an intensive assessment of their data team's capabilities, processes, and outputs. Yes, there's a deliverable - a summary report. But what that report actually provides is a transformation.

From: "I don't know how my data team is performing."

To: "I have the knowledge to make confident decisions about the data team."

That transformation is worth $10,000.

Notice the pattern:

Implementation work (e.g. "Build me a dashboard") = low value, commoditised, easily replaced

Strategic work (e.g. "Help me understand if our data capabilities can support our growth plans") = high value, differentiated, hard to replace

This is why some data scientists stay stuck as "the person who builds dashboards" while others become strategic advisors to executives.

It's not because they're better at Python or know more algorithms.

It's because they've shifted from selling deliverables to selling transformations.

From implementation to strategy.

The question isn't: Can you build a model?

The question is: Can you help stakeholders understand what decisions they should make and why?

That's the work people actually value.

And that's the work that gets recognised and rewarded - whether you're a consultant or an employee.

Talk again soon,

Dr Genevieve Hayes