4 Obstacles To Your Analytics Career


Use Data Or Be Used By Data!

The June 30 issue of Seotistics is here for you!

An Analyst is a superior thinker and a person who can dissect topics with precision.

Some of my students and readers alike commit the usual mistakes that can be easily avoided.

We will take some of the concepts from my last issue to see why.

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1. Automation VS Analysis

Many many people confuse the 2... and this is bad for your learning!

These 2 services are covered by different professionals and are completely different.

The good thing is that both can be learned by everyone and can boost your career.

I will show you the main differences between the 2, straight from a preview of a landing page I am working on:

Learn the Difference

Analytics vs Automation

Choose the right solution for your business needs

Focus
📈 Analytics
Strategic Decisions
What to measure and act on
Automation
Efficiency
Remove manual tasks
What You Get
📈 Analytics
Strategy Framework
Plans and decision models
Automation
Working Scripts
Tools ready to use
Problem Solved
📈 Analytics
"We have data but don't know what it means"
Automation
"Too much time on manual tasks"
Impact
📈 Analytics
Strategic advantage
Better decisions, better focus, more opportunities
Automation
Immediate gains
Time and cost savings, fewer errors

📈 Analytics For

  • Strategy needs
  • Data insights
  • Defining what to do
  • Better decisions

Automation For

  • Manual reports
  • Saving time and money
  • Scaling processes
  • Quality control

At an advanced level, automation is handled by engineers, whereas analysis should be offered by domain specialists.

This is exactly why I recommend marketers and analysts to focus on Analytics and don't overdo it with Automation.

For sure, you should build prototypes and mockups but no one expects you to be an engineer.

(If they do, run).

2. The Course Loop

How many times I open LinkedIn and see people attending 1 billion courses?

This will dilute your attention and make it almost impossible to memorize what you need.

The most notable examples are:

  • Studying the foundations of a topic (once you already know them)
  • Repeating the same topic 1 billion times

Now LLMs exist and most of the topics you don't need to learn like before.

If this is you, the best you can do to break the loop is to start practicing.

The advice to build a portfolio was effective years ago, now you need to go beyond.

Most of these generic courses are outdated, run away if you see the following:

  • teaching you Python (as their primary goal)
  • Machine Learning /any other complex topic

You need solid foundations and something that is specific to YOUR background.

Gone are the days of knowing some Python and landing a 6-figure job.

Now it's time to become a specialist in an industry and use data skills as a support.

I recommend everyone to use data skills as a complement to what they already know.

Pure Analytics skills without an understanding of the business won't bring you far in 2025 and beyond.

If you really want to improve your business understanding with Web Data, here I am:

OK... So How Do I Stand Out?

Practice in one industry or for a specific business model (e.g. B2B SaaS, Ecommerce).

Don't become another generalist with mediocre coding skills and some Udemy certificates.

Fall in love with the basics and be able to answer the most common questions.

No business person will ever ask you to build a complex system to measure X and Y...

if they do, it's your duty to educate them.

The main issue with being a "stock" analyst is that getting hired is harder and you won't last long.

So it's pretty much super convenient to find your speciality and get going.

Even though standing out is much harder today, you still have margin for promoting yourself.

Even something simple and easy like sharing a dashboard online can help you at first:

Or even documenting and writing about what you produce, as long as it's not too "mainstream".

Once you have something tangible, you can start honing your skills to serve a specific industry/business model.

How would you translate basic data knowledge into highly specific business insights?

Yes, this is exactly the point where you can be different and make a name for yourself.

If you already work and just learn some more Analytics skills, this is even easier!

3. Shiny Tool Syndrome

Be honest, how many articles did you read or save about LLMs?

The answer is: too many.

Don't worry, you aren't exactly behind as influencers love to say.

Many companies will take 3-4 years if not more before they even allow to use some tools.

And remember, your country matters a LOT.

Most of the information online strictly refers to the USA (or the Anglosphere).

In some other countries, you will never see the same speed of innovation.

In other cases, it's the opposite so put things into context and never ignore the geographical argument.

So how do you understand if you are ready to study cool tools?

Tick the boxes if you are 100% sure this is your case:

Are you ready to learn complex stuff???

If you scored 5 greens, congrats, you are ready to approach more complex topics!

(Be honest, almost no one will actually score 5 lol) 👀

This is a nice way to say that unless you are working with big projects and have solid use cases...

you will not be different from the 99% of the other professionals.

That's completely OK and we should normalize it.

Most of my job is repeating the same routine over time... and I love it!

Jobs with a lot of unpredictability are not scalable and will eventually burn you out.

For sure, you need some change but not too much.

So far the only real advantage is being able to use LLMs properly to code faster and build useful stuff.

That's it.

I've shared many examples over the past months of how I use LLMs effectively daily.

Ok But The LLM Hype?

It's to sell of course!

Excluding what I just said, the rest you don't need.

This is why I stopped reading many "popular" online resources as they only share smoke and screens.

Even the entire argument of "AI traffic" can be debunked if you actually open your analytics tools and check their traffic share.

But LLMs are still powerful allies for other tasks, like coding and building mockups.

So it's not only hype but you need to be aware they will not make you rich.

4. Bubbles & Silos

This must have happened to you too.

You talk to an agency and they make their recommendations based on what they usually do, not YOUR business needs.

It happens all the time, actually.

Or even professionals refusing to look beyond their department or functional area...

That's because both scenarios describe bubbles (or silos).

There are many examples in SEO:

  • defining random metrics as KPIs
  • pruning content because it doesn't get organic traffic
  • consuming only SEO content

And there are magnific examples in Web Analytics too:

  • not knowing marketing channels
  • sticking too much to default Google tools and settings
  • obsessing over implementation (GDPR won't get you rich)

Out of all the obstacles to your career, this is definitely the worst.

I still fight with agencies from time to time because they can't accept the fact that good companies don't use Google data like in the docs.

No real company is using default options.

You will miss out on a lot of clients and opportunities if you stick to your silo.

This is different from being a specialist, though.

A specialist is in a niche, not in a silo!!!

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📚 Recommended Reads - Peak Content 🗻

Read these, once more and once again:

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Marco Giordano
Data/Web Analyst

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Bernerstrasse Süd 169, Zurich, Switzerland
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Seotistics - Web Analytics + Business + Strategy

The Seotistics newsletter is written by Marco Giordano, a Data/Web Analyst with the goal of combining business and web data. Tired of the usual boring Analytics content without any business impact? Seotistics teaches you how to use Analytics, web data and even content in your workflow while helping you with Strategy.

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