There’s a certain fetishism around raw data in digital analytics.
Raw data feels powerful. Complete. Untouched by GA4’s sampling, interface quirks, and processing rules. It gives analysts a sense of control.
You can answer any question, build any model, run any query. As long as you (or ChatGPT) know your way around BigQuery.
But most marketers and digital teams don’t need that level of detail.
They need clarity. Direction. Confidence.
And for that, raw data is usually overkill.
You don’t need raw data to analyse your website traffic
I’ve heard this too many times:
“We can’t analyse anything until we have the raw data in BigQuery.”
This idea has become a badge of honour. A way to delay analysis while blaming tools or infrastructure.
At digital analytics conferences, the prominent industry voices told us we needed raw data for meaningful insights.
Back then, it was only available to GA360 users. The rest of us assumed we couldn’t do anything interesting or important.
Now that everyone has access to GA4’s event-level data, it’s easy to fall into the same trap.
But most useful questions don’t require raw data.
You don’t need GA4 exports to understand how people find your site, which content engages them, or where they drop off. You just need to work with what’s already in front of you: standard reports, Explorations, Looker Studio, and good questions.
Raw data doesn’t guarantee better insight. But it always gives you more ways to get lost.
Most questions can be answered with aggregate or sampled data
The real bottleneck in most teams isn’t access to raw data. It’s the lack of clear questions, thoughtful analysis, and actionable recommendations.
When people fixate on raw data, they are often trying to solve the wrong problem. They don’t know what they want to know.
And for asking and answering meaningful questions, perfect data isn’t necessary.
Insight doesn’t come from completeness. It comes from focus. On the right segments, meaningful events, and interpretation.
Insight ≠ Infrastructure
Raw-data-first strategies can make sense. Especially if you are building a customer data platform, training machine learning models, or integrating complex systems.
But that’s not what most teams are doing.
Most of the time, we are just trying to figure out:
- Why did conversions drop last week
- Which channel brought the most engaged users
- Whether the new landing page outperforms the old one
- How mobile visitors behave differently from desktop users
You don’t need BigQuery to answer these.
You need:
- A few well-planned custom events
- A report that makes sense to your stakeholders
- The confidence to use the tools you already have
Start smaller, get smarter
If you don’t trust your current data, don’t rush into raw data. Start by cleaning up what’s already there.
If you’re unsure what to measure, don’t build a data warehouse. Build understanding.
Ask:
- What do we want to optimise?
- What decisions are we trying to support?
- Can we answer these questions today?
- Are we acting on any of our current insights?
Data granularity doesn’t limit most teams. Time, priorities, and clarity limit them.
Raw data won’t fix that. It might even make things worse.
Start with what you already have:
- GA4
- Looker Studio
- Your brain