Stranger Things, but to keep things from getting stranger, you need clean data. Data hygiene best practices.

The success of Stranger Things wasn’t expected by its creators or even by Netflix, according to the Los Angeles Times. The show launched with almost no marketing, and it slowly gained traction through a powerful mix of 80s nostalgia + modern storytelling and mostly word of mouth.

As philosopher Nassim Nicholas Taleb says, we live in a world where we try to rationalize data and predict default scenarios. But reality is shaped by the highly improbable, the unexpected “black swan” events that change everything.

In Stranger Things, people later created theories about how the recommendation made the show explode. In Growth and Marketing Ops, that’s literally what we try to do: create the conditions for a “black swan” moment, something unexpected that becomes a trigger for positive KPI spikes.

But then we fall back into strategies, KPIs, and GTM foundations.

Netflix’s GTM engine didn’t rely only on word of mouth. Their algorithm understood behavior and delivered the right content to the right person. This is the dream for Marketing Ops and GTM Engineering: reading signals, spotting patterns, and matching products with the right audience.

But beautiful ideas only work with excellent data hygiene: clean, standardized, deduplicated, categorized, and reliable data.

As Ops, our job is to make this possible.

This is how we move from big ideas → to real GTM execution. 

So don’t let these things get stranger: when a big idea arrives, we Ops are the ones who understand the strategic vision AND build the environment that makes it possible, connected data, captured at the right moment, cleaned, enriched, and ready to activate magic.

Long live OPS. 🚀

---

Outro (Data hygiene best practices flowchart)

Data hygiene best practices.

1 -> Begin with the audit

2 -> Understand the data, the empty fields, and the duplicate fields.

3 -> Talk with professionals to understand what is duplicated in the objects, because sometimes it's possible that the same email is not a sufficient condition for duplication.

4 -> Create uniform data standards and document them.

5 -> Create workflows in CRM to automatically deduplicate objects.

6 -> Validation rules to ensure that all the necessary fields are filled in.

7 -> Correct and validate your data.

8 -> Fix Bugs that create wrong data.

9 -> Create a flag to understand the % of the new leads that are duplicates or wrong.

10 -> Teach professionals to flag the wrong data.

11 -> Audit frequently.

Track the flag until you feel confident with the % of wrong data.

Want more tips for data hygiene. I recommend this post.