Katie Bauer!

There is no universe where I’d be able to do justice to a book this dense by trying to summarize it in a Twitter thread. But I have been thinking about how it applies to data products, defined here as “things a data scientist might produce during the course of their work”

An idea introduced early in the book is that each medium of communication is a container for a previous form of communication. Thus, telegraphs contain print, print contains the written word, the written word contains speech, and speech contains thought itself.

You can apply this line of thinking to data products as well. Recommender systems predict the probability of a user clicking through an item in a feed. That prediction is a container for a summary stat, probably the mean of all possible outcomes given the input data.

That point estimate is in turn a container for a statistical distribution of those possible outcomes, which is a container for a statistical model that compresses the distributions for each input. And THOSE input distributions are containers for the data generating process

TL;DR it's statistical turtles all the way down

This may seem navel-gazey, but it’s getting at another of McLuhan’s points. We don't recognize new media when we first encounter it. We only see the old media format inside the new media container, so the new format alters the way we think without us even realizing it's happening

As new media formats extend our senses, we also fail to recognize how much of ourselves we pour into them. We don't realize they're extensions of us, and we become infatuated with them, much like Narcissus fell in love with his own reflection.

It’s easy to see this happening to various degrees to social media users who get invested in their on-site personas, but like I said, I’m more interested in talking about how this applies to data work.