Use Data Or Be Used By Data!

The July 27 issue of Seotistics is here for you!

Dashboards are everywhere and tbqh often overengineered for simple use cases.

I've audited too many organizations having this issue here.

So what can we do instead?

The answers here are simple and feasible, you just need to know them!

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The Dashboard Curse

You did some data work, maybe worked on tagging and data engineering... now it's time to show the data.

The first thing that comes to your mind is a nice dashboard in Data Studio.

The issue is... most of them are barely used by your audience and they end up going back to the previous tools.

In Web Analytics, it's much easier to produce something valuable compared to other industries.

Maybe this is related to an active monopoly of the web industry 🤔

For example, most sales people would rather stay inside Salesforce than using a custom dashboard.

Why is that?

Because Salesforce offers all they need and custom dashboards.

So you'd need to go the extra mile and integrate other data sources, model the data, etc.

The Market

No matter which company you go, the cycle is often the same:

pay someone to prepare a dashboard
they have no clue
you get billed
no one uses the dashboard

After working with quite some partners, I can attest what the other data professionals say too!

In Web, it's even worse because you see the usual dashboards that don't move any decision:

Traffic metrics
Some SEO stuff (that is also wrong)
PPC metrics (occasionally)

And even the idea that there is a universal dashboard that can be used in any context.

Truth is, you need to ask the right questions first and understand which metrics matter.

Do you recall all of this?

Maybe not if you are new here, the idea is that you have to figure out what needs to be answered!

You can even ignore that classification and go broader

Some examples:

Which products have the highest potential in terms of net profit?
Which content cluster could have the highest impact in terms of visibility?
What are my best sources for acquisition?

And you can even go deeper, this isn't the place to discuss it.

Why Dashboards Fail

There are many reasons that plague dashboards:

weak planning and management
no one actually uses them
more chaotic than having no dashboard
janitor work (aka no automation)

In most cases you have to impose yourself over clients and state how to do it.

If not, you will produce the most useless dashboard ever.

During my work experience, I have realized that the issue is not the data product but the entire process around it.

The best way is to start from questions you want answered and processes, as said above.

Some Valid Alternatives (To Dashboards)

What many ignore is that you can definitely make it easier: reports, emails or quick summaries.

Not every single data request needs to have its own dashboard.

Actually, if you think like that you will soon bankrupt the company lol

Bigger companies are more cost and time sensitive so churning more data products is terrible.

The nice guy advice is to "evaluate every request" is equally as bad.

I've personally tested it and it doesn't work.

Dedicating full attention to every minor ad-hoc request you get will burn you out.

Did LLMs Change Something?

Yes, LLMs certainly had an impact because now you can create web apps and mockups super quickly.

In fact, this is what I currently teach and what people are doing anyways.

It's super crucial to understand LLMs as a technology to survive today.

You have 0 valid reasons to sleep on them... because everyone else is using them.

And unlike Python/SQL, the technical barrier is almost null.

Once again, LLMs are nothing else but capacity multipliers. You need to be decent at what you do to see actual results.

You give Claude to a random person and the results would still be mediocre.

This is why it's important to first have some knowledge and THEN learn LLMs.

Organization-wise, LLMs are just tools and aren't strategic by definition.

The only issue is that now you can produce slop easily and people can feel more entitled.

Self-Service & Other Fairy Tales

The concept of self-service is that people should analyze data themselves.

The intervention from data people should be minimal.

Once again, this has been disproved countless times and with some experience in Analytics, you'd realize that it doesn't work!

There are many underlying assumptions:

that people know what they want
they are able to understand most of the tools

So if you ask me, the best solution is to create data products like dashboards, yes, but you guide the users.

No one wants to actually write documentation, communicate with end users or even simplify things.

All of this isn't cool and doesn't get you recognition in front of colleagues.

The Perfect Pyramid

Based on my experience and what usually works, the idea is simple:

Solid foundations are what I promote and teach.

You need your data nicely stored in a warehouse and then processes that are documented.

Without full observability over how your data is transformed, you have 0 control!

Then, you can consider data products that will be used by some people in your organization.

The problem here is thinking that dashboards are evil or useless.

You still need them as they are a comfy way to visualize data without building an actual app.

Get familiar with tools like Antigravity IDE and naturally, your terminal! I explained this many many times in the past issues.

Where many get lost is in the number of dashboards and in how they are built.

At the top you have a few scripts or specialized use cases like GA4 Explorations.

Those don't scale companies and are only useful for specific individuals.

You can't scale an organization with a mass of GA4 Explorations and some Python scripts.

This is also why I am completely against most of the mainstream content.

It just doesn't work, not even for small companies.

They are tools for specialists, after all.

Chatting With Data & LLMs

You have heard of Conversational Analytics, the equivalent of Voice Search in SEO.

Both of these topics are predictions and never actually happened.

Today it's possible to build a chatbot that can answer your questions about data but the output is nothing special.

The point is that Analytics isn't showing data... the decisional side matter a LOT.

Which is why rather than chatting with data or having another MCP, I prefer real automation and processes.

My friend Steen Rasmussen calls this "decision laundering".

Data is NOT used to make any new decision.

What You Can Do Today

No need for superpowers or even technical skills, this is pure management and common sense.

Audit the following:

how many dashboards do we have? Do we use them all?
Is there something we can simplify in our process?

Check how many useless dashboards or explorations you detected and let me know!

There are corporations with hundreds of failed data products and this is the current status of the industry.

I bet you can survive with a few of them, possibly avoiding overlaps.

My friend Ergest recommends some nice ways to handle the dashboard sprawl in his article.

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Marco Giordano
Data/Web Analyst

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