Wrong but useful. Working @Twitter, tweeting about data science and people management.
"Wait, it's all her views?" 
"Always has been"
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There's a lot of pressure in the modern workplace to be data driven, but you don't have to try too hard to find a salty data scientist who can tell you a story (or two or three) about a time that "product intuition" or "strategic bets" were used to justify a decision instead.
While this is frustrating, insisting that every decision be purely quantitative also misses the point. It's almost as if people think performing the Sacred Ritual of A/B Testing protects them from being wrong, when the real goal of using data is being better than yesterday
IMO, rather than attempting to predict the future, the most practical, scalable and immediately valuable use of data is to understand how the present compares to the past. This is the real reason why being data driven is worthwhile--it's a form of accountability.
If you've ever budgeted to save money, or tracked your daily footsteps to become more active, congratulations! You've taken something it's easy to make excuses about and forced some objectivity into it. You're keeping yourself honest, which is what being data driven is all about.
If you want to be data driven, pick a way to measure something, apply the measurement regularly, and keep all the measurements in the same place. Then look at the trend to see if the measurement is consistently moving in the direction you expect. That's really all it takes.
but which one in the photo are you
You can’t drop knowledge like this on a Saturday night, we’re not ready
i can if i have had two glasses of wine
Yeah, this is something that my perspective has shifted about since joining industry. I've started to frame the goal as being "data-informed" rather than data-driven. The most important goals are the hardest to quantitatively operationalize, +
qualitative understanding is essential for designing data acquisition and interpreting the result. The hazards of metrics is something I'm still pondering over too e.g. @math_rachelhttps://www.fast.ai/2019/09/24/metrics/ …
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