I added this blog to Randy Au's webring for data writers. Take a look here, or below:
I'm struggling to describe why I think this is so interesting.
Most of the writing I've seen about doing data work is misleading. It misrepresents what data work actually looks like.
Everyone writing online wants someone to read their work. Social media give powerful hits of attention when they favor your work. It's easy and natural to start writing for an algorithm.
But that produces a very particular type of writing. Folks rehash the same threadbare topics - responding to what novice readers think they need to know (should I learn python or R in 2024?). Folks write hyper-technical or academic content because it's prestigious, it's concrete, and our employers are less likely to care that we're sharing it. Now, LLMs are churning out surface-level content too.
In all, there's a distorted image of what good data work looks like. That image is so strong that it actually changes the reality of data work. Hiring managers start to require skills that they think should be common. Executives start to expect magic ML solutions from small data teams. Novice data scientists train for hyper-technical (but fictitious) jobs. This backwards pattern was stronger five years ago, but it's still around now.
Instead, what I want to find is a few authors I trust to discuss the realities of doing data work. Randy does a great job here. I've enjoyed reading a couple of blogs from his webring too.
I don't know that webrings are the cure, but a different set of incentives are welcome.