The Bad News (Data) Bear(er)s


Most data scientists think the hardest part of experimentation is the statistics.

It’s not. It’s telling people their ideas didn’t work.

Here’s a reality check about experimentation: Even at companies like Google and Netflix, 70-90% of experiments don’t show positive results.

That means if you’re running A/B tests, you’ll be delivering “bad news” far more often than good news.

Now imagine being the data scientist who constantly tells people their ideas didn’t work. How long before stakeholders start avoiding you?

This is why office politics becomes critical when you’re doing experimental work. You’re not just doing data analysis - you’re managing egos and expectations.

Bloomberg Product Analytics Manager Miguel Curiel and I tackle this exact challenge in the latest Value Boost episode of Value Driven Data Science.

In just 10 minutes, you’ll learn:

  1. Why running experiments is politically riskier than regular analysis [01:50]
  2. The mindset shift that turns experiment “failures” into wins [03:56]
  3. How to overcome the “it worked for Netflix” objection [05:07]
  4. The simple strategy for reducing political friction around data work [08:24]

Listen now on Apple Podcasts or Spotify, or click the link below:

Episode 85: The Office Politics Survival Guide for Data Science Experiments

Because even perfect data won’t save you if you can’t navigate the politics.

Talk again soon,

Dr Genevieve Hayes

Data Science Impact Algorithm

Twice weekly, I share proven strategies to help data scientists get noticed, promoted, and valued. No theory — just practical steps to transform your technical expertise into business impact and the freedom to call your own shots.

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