The Danger of Beautiful Data

Clean data isn’t always correct. Here’s how “beautiful lies” sneak into your reports.

When we work with data, there’s one question that should never leave our mind:

Can we trust it?

It sounds obvious. But in practice, it’s one of the most neglected questions in everyday data work.

We like to think that once data is clean and well-formed, we’re safe. That “tidy” means “true.” But that’s a trap.

Clean data can be completely wrong. Perfectly formatted, logically consistent, technically valid — and still garbage.

A client once told me, beaming with pride, that his website’s page impressions had suddenly jumped.
He congratulated his team. Internal emails went around. Everyone celebrated the technical improvements they had made just weeks before — apparently, a resounding success.

But when I looked at the numbers more closely, I saw something odd: the number of sessions hadn’t changed at all. Traffic was flat. And nothing about the new setup should have caused users to click more often.

A few hours of digging later, we found the culprit: during their “technical improvements,” the analytics tracking code had been embedded twice on some pages. Every visit was being counted twice.

The data was clean.
The dashboard looked perfect.
And yet the story it told was completely false.

In my years working with online marketing data, I’ve seen this pattern repeat itself endlessly. I strongly believe more analytics setups are wrong than right.

Tracking snippets misplaced, filters swallowing important data, attribution models configured backwards — and still, everyone stares at those dashboards as if they were gospel truth.

Because the interface looks professional. Because the numbers move. Because we want to believe we’re measuring reality.

This is what I call a data blind spot:
When clarity of presentation hides uncertainty of origin.

Every dataset we touch should face two simple but uncomfortable questions:

1. Is it valid? (Does it represent what we think it does?)
2. Is it complete? (What might be missing that we don’t see?)

If we don’t embed these checks into our workflow — both technically and mentally — we’re just decorating our assumptions with charts.

So next time you’re handed a “clean” dataset, resist the comfort.
Poke at it. Challenge it. Ask who collected it, how, and why.

Because in data work, the biggest errors don’t come from messy spreadsheets.
They come from beautiful lies.

Thanks for reading,
Stefan