Attention: As of January 2024, We have moved to counting-stuff.com. Subscribe there, not here on Substack, if you want to receive weekly posts.
Great news, I got all the achievement trophies of Armored Core 6 over the weekend, meaning I don’t have an giant distraction trying to distract me from writing posts.
A couple of days ago, Cat Hicks wrote a lovely short piece about “craft”. It touches on a bunch of aspects of that complicated word, but one point that stuck with me was the part where some people used the term as a weapon to exclude people. In one example, there were folk who say that software engineering is a unique craft that is different from other work. In the view of such people, it’s a special club of practitioners, an in-group where different rules apply.
I’m pretty sure that everyone reading this has seen variations of this behavior before, if not with software and engineering then just about in any profession or pursuit that exists. Almost any search about “a real ${pursuit}-er” will pull up any number of “no true Scotsman” arguments scattered around the internet that reflect this. Real photographers use film! Real cake makers don’t use pre-made mix! Real woodworkers don’t use screws! Real data scientists have a computer science or stats degrees! Real cyclists don’t use e-bikes! Anecdotally, it’s a sign of someone who has no idea what they’re doing.
Often, discussions about the notion of “craft” does a dictionary citation that doesn’t really help things so I’m going to skip that. For here, I’m roughly talking about the vibe around “an endeavor a person can spend time mastering”, whether it’s a field of work, study, hobby, or pastime. There’s an element of depth and skill that isn’t immediately obtainable, so it’s impressive when we encounter someone who can show us those depths.
We live in a wonderful universe where just about any pursuit can be elevated to the level of “craft”, including traditional art forms to quirky talents. As someone with an ever-growing set of hobbies, I’m always on the lookout for people who are craftspeople — people who get deep into a craft — because I look up to them as my benchmarks for learning the hobby. I want to understand things enough to appreciate details in what they do even when I can’t do it myself.
Over time, after watching countless people in a broad array of endeavors fighting on the internet over the prestige of “their craft”, I’ve come to understand that being a craftsperson is a state of mind. It’s a state that turns on and off within a person, and the masters we observe just happen to have it turned on more than others. It’s essentially the act of paying attention to details in order to improve.
There are tons and tons of people who participate within a designated “craft” without necessarily being in a craftsperson state of mind.
As an example, we have lots and lots of people in data science now, and it’s more or less become a field of work, though not exactly a field of study. You can make a decent argument that the work of data science, however you define it, constitutes a craft that people practicing it can devote multiple lifetimes to mastering thanks to being an intersection point of multiple fields.
Let’s just declare that everyone who does data science is practicing the craft of data science.
One hallmark of the thing being a craft is we see plenty of arguments about it. We definitely see heated debates on the internet about who IS and IS NOT a data scientist. Effectively, some people are concerned about social status. Who deserves the status and the commensurate income, who doesn’t. None of the energy spent on the arguments is going anywhere towards making anyone better at data science. But I suppose it’s a pastime for some.
But the actual doing of the craft of data science involves a bunch of activities — working with systems, analysis methods, building tools, sharing knowledge. Oftentimes those abstract tasks manifests as writing SQL or code, debugging systems, having meetings, talking to people. It’s our day job. Do the work, deliver the value, then be done for the day.
But it’s possible to take it further. If you wanted to be a craftsperson in data science, if you want to use your time to being damn good at DS work, there’s a ton of stuff out there to work on improving. Yelling about borders drawn in sand to declaring that you’re inside a box isn’t useful to anyone, not even yourself.
And so, that’s why in my head I separate the people who “work within a craft” from people who “are craftspeople working at their craft”.
Working within a craft is exactly that, accomplishing work within a field. A musician plays what is asked of them. A data scientist makes the dashboard as requested. A chef delivers their 50th dish of the day. They’ve done it plenty of times before and have the work down. From an external standpoint, they’re just doing their jobs as necessary. Maybe these folk are craftspeople, maybe not. It’s not possible to tell externally.
Out of that big population of people that are working in a craft, the craftspeople I’m separating out are energy finding ways to get better at what they do. That’s not to say they’re some kind of “special artists” who spend every waking moment on their craft to the exclusion of everything else. Nor am I saying they can’t be self-absorbed jerks who want to horde prestige too, but it’s not ALL they do.
The craftspeople are finding ways to get better at what they do. There’s reflection on what is it they’re doing even for routine tasks and if they notice a flaw or inefficiency, they try to make it better instead of letting it be. Maybe they spend extra time on it, maybe they don’t. I don’t see why this can’t be done only during normal work hours. However these craftspeople find the opportunities to constantly improve what they do, the result is that the extra proficiency eventually winds up creating outstanding examples that actually help give form to the boundaries of what their craft can do as a whole.
As an example, I’ve seen translations of songs where the lyrics weren’t didn’t just have the meaning brought over clearly, but the translation could also be set to the same music and convincingly sung. Having worked on songs before, that is an astoundingly difficult task that oftentimes requires the stars to align — I’ve tried with ‘meh’ results. For me, that sort of work puts a stake in the ground for translation that declares that “this point is within reach of some in this field, if not within your skills”.
Throughout this, I need to make clear that I’m not arguing here that just “working within a craft” is in any way bad — it’s the default state. Most days, I go to my day job, do my data work, then go home. No boundaries are pushed, no new unsolved problems. I’m also not thinking how do I become a better data scientist in any way. I just want to do my work, go home and be with family. Turning on the “find ways to do things better” switch takes a lot of energy and resources. I’d burn out quickly if that was the default state.
But once a week, I do something related to getting better at the craft of DS. It’s mostly doing a tiny amount of self reflection about stuff that’s happened and thinking about it before emailing a large group of people about it. But the important thing is that the effort as made. Same applies to my hobbies where I’ll never be in any position to push any boundaries. I’m still doing, and thinking about, the work and how to do it better. I might never appear to be a craftsperson to the outside observer looking at my workmanship, but I’m internally doing the important part.
So in the end, what I’m saying is that, depending on the time and energy budget available to us, we can make a choice about whether we want to turn on “craftperson mode”. Some people will have a natural inclination to do so — I know if my brain latches onto a topic, I have trouble stopping myself from diving very deep into it. For others, it’s a more deliberate action they have to take.
In music circles, there’s a term that parallels this concept called “deliberate practice”. If you ever learned an instrument as a kid and was forced to practice for 60 minutes by your parents, you might have just sat down and mindlessly banged on the piano keyboard until the clock was up and you could run off to play video games instead. It also wasn’t very useful in terms of helping you become a better musician. Deliberate practice is the opposite of that where you pay attention to what you’re doing and actively seek and correct mistakes.
Guess what, if you practice like that enough with your instrument, you’d eventually get good enough that people might start thinking you’re a craftsperson. It can be done with just about anything — pick the one you’d like.
Standing offer: If you created something and would like me to review or share it w/ the data community — just email me by replying to the newsletter emails.
Guest posts: If you’re interested in writing something a data-related post to either show off work, share an experience, or need help coming up with a topic, please contact me. You don’t need any special credentials or credibility to do so.
About this newsletter
I’m Randy Au, Quantitative UX researcher, former data analyst, and general-purpose data and tech nerd. Counting Stuff is a weekly newsletter about the less-than-sexy aspects of data science, UX research and tech. With some excursions into other fun topics.
All photos/drawings used are taken/created by Randy unless otherwise credited.
randyau.com — Curated archive of evergreen posts.
Approaching Significance Discord —where data folk hang out and can talk a bit about data, and a bit about everything else. Randy moderates the discord.
Support the newsletter:
This newsletter is free and will continue to stay that way every Tuesday, share it with your friends without guilt! But if you like the content and want to send some love, here’s some options:
Share posts with other people
Consider a paid Substack subscription or a small one-time Ko-fi donation
Get merch! If shirts and stickers are more your style — There’s a survivorship bias shirt!
For me, the practice of craft in applied statistics and data analysis (aka DS) is the patient work of excavating the questions that the data can answer beyond the immediate questions posed (the “in the craft part”). All praise to John Tukey.