Wrong but useful. Working @Twitter, tweeting about data science and people management.
"Wait, it's all her views?" 
"Always has been"
You can add location information to your Tweets, such as your city or precise location, from the web and via third-party applications. You always have the option to delete your Tweet location history. Learn more
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
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.
I recommend thinking about metrics as a form of media, a new technology that extends and alters the senses of a business. They work as mental prosthetics for decision makers, helping them quickly understand things about a company that they otherwise would struggle to summarize.
They also abstract the company's performance from the realities of the end users that the company makes decisions on behalf of. People become OBSESSED with metrics, so eager to move them that they forget their product is being used by people with particular goals in mind.
Whatever the original purpose of the product was, it's quickly deprioritized in favor of getting users to do stuff that will cause the trend line of a metric (probably revenue or DAU) to go up and to the right. That's how you get stuff like this.https://twitter.com/LukeW/status/1327349136702672897 …
McLuhan doesn't really seem to be describing this progression of media and ensuing fragmentation as a problem per se, more as an inevitable process. But even though he's not explicitly negative, it does leave me feeling a little ambivalent about my profession
A data scientist's job is to quantify things, after all, and quantification enables segmentation and fragmentation. Perhaps my solace should be in the fact that in order for things to be segmented, they first have to be understood as continuous.
Data scientists and the metrics they make can be a unifying force. Consistent measurement allows everyone to be on the same page and move in the same direction. Creating metric is creating a tool to direct a company's focus.
Directing focus at the right things (or as close to right as possible) might be the most difficult and important part of a DS's job. It's worth taking seriously.
Twitter may be over capacity or experiencing a momentary hiccup. Try again or visit Twitter Status for more information.
