Once there were two sons of two entrepreneurs. They both decided to follow their fathers’ examples and build businesses out of ideas and relationships. One became a salesman by selling products; the other became a salesman by learning data science. I don’t know which was wiser, but I know which was surprised when he realized he was a salesman. The harder he looked for meaning in data, the more important it became to speak clearly and simply.
I was asked a question on Quora:
When did you have to analyse data and give recommendations based on your result?
Someone wants to know about the workflow when doing data work. No? So I tell her my version. I don’t know if my version is a better version than all the others, but I know it is a good version. It works for me. I learn from data. That’s the point, isn’t it? I sometimes convince people to move forward. That’s my job, to move people forward for good reason. Making a recommendation is easier than making a difference with a recommendation. …
I went bowling with my friend the other week and he teased me for my comments after I threw the ball. “I didn’t deserve that,” or, “that one I deserved,” sounded to him like I was confused about the laws of physics.
I was talking about the things I could control, whether I was fighting my body. Sometimes I would release late, or I would wrap the ball around my body instead of bowling straight, or I was distracted, or my feet or eyes were not where they should have been, or something else. When working at odds with myself, any pins I knock down I don’t deem as deserved. …
I wrote a piece of software lately that got the job done. I was ready to move on, but the next morning, I could hardly understand what I had done, or how I had done it.
This is strange. I’ve written a lot of software. I usually have no problems writing concise and clear systems. Thank goodness I’ve been reading Ousterhout’s A Philosophy of Software Design.
Ousterhout focuses his efforts on reducing complexity, specifically making it easier to understand and change a system. If the next developer can’t understand what I’m doing, it’s too clever. …
People use drugs, legal and illegal, because their lives are intolerably painful or dull. They hate their work and find no rest in their leisure. They are estranged from their families and their neighbors. It should tell us something that in healthy societies drug use is celebrative, convivial, and occasional, whereas among us it is lonely, shameful, and addictive. We need drugs, apparently, because we have lost each other. — Wendell Berry, The Art of the Commonplace: The Agrarian Essays
You don’t know yet that there can be a last birthday, a last day getting ready for school, a last Snapchat — the joke well landed and shared by everyone who knows the acid troll was made for them, for them to laugh. …
“Sometimes truths are what we run from, and sometimes they are what we seek.”
― R.D. Ronald, The Elephant Tree
Tonight I stare into myself,
see what no longer
tightens the muscles
under my ribs,
starts the sweat in
It was disappointment
that caused the sweating,
enough to kill an elephant
— or maybe just maim it —
if the elephant thought
judgment is real,
as real as the mahout’s whip or
as heavy as the load
chained behind it
dragging through the dirt.
An elephant can also
rip a tree from the ground
leaving roots startled
they have no power anymore,
no power to pull from the soil
the food growing there. …
There’s this thing we do with social media that gives us small hits of dopamine when it works well. You know the feeling — we all chase it these days — you post and like so you can feel good. We sometimes make the same mistakes with data, building tools that give us that same flush of excitement, but don’t lead us anywhere useful.
A data-driven organization is an aligned organization, an informed one, one that values awareness and functionality and is willing to pay the price to have these things. A data-obsessed organization can be one that never learned to get off the dopamine high, even when things are going well. Dopamine’s great, but so is getting work done, which isn’t always as fun. …
Sometimes I work hard, but nobody’s there to notice — or they don’t like what I’ve done. I’ve learned that a confident practice is the wisest way to handle things, whether or not anyone else notices.
A confident practice is the message I keep on repeat. Whenever I give people training advice, career advice, startup advice, or product launch advice (really any advice I give at all), it includes the phrase, “confident practice.” I thought for hours (probably hundreds) about what goes wrong with technical careers. I mentored dozens of people in their early software careers across two decades. I listened to podcasts and read articles and books. …
Hello. I’m David Richards. This is my user’s manual. If you want to find out what’s quirky, what’s broken, or whether to bother while working with me, you’ve come to the right place. This article starts with ideas from Inc. and Mindaugas.
I build web apps very quickly (but relatively cleanly — it’s not a free for all, I’ve just learned not to gild those particular lilies after some practice). My data work is deliberate. …
I interrupt your regularly scheduled program to tell you there’s yet another thing for you to do in your busy day — but you want to know this. And you can do it quickly and with confidence. Later I’ll write articles to show more sophisticated ways of doing this.
What is it you need to do? You need to predict the revenue for your product.
Isn’t that for the accountants? Sure, let them do their jobs, but inform yourself too. If you know what’s happening and why, you’re going to be fine.
How do I do this?
Predict recurring revenue by asking framing questions. Framing questions give you a general sense of what’s going on. What was the company’s revenue in recent months? How fast is the company growing month over month? Is there anything important that’s happening now, such as a season or new marketing event? These questions limit the possibilities, or frame your answer to something within a reasonable range. …
A good data model, the kind that makes me wet my pants for jealousy, grows up naturally. Full disclosure, I’ve never wet my pants over data models, brilliant or otherwise. I’m just saying I get excited when someone’s gotten it so right they are at the pinnacle of their discovery. My envy leads to learning about their algorithms and data pipelines and try to mimic them. That’s backward.
A better way is to start small. If you can start a model on a napkin over lunch, you’re probably on track. On a napkin I can see the shape my data might take and how it generally works. Am I making intuitive leaps? Have I over complicated things? Do I actually have this data or know what data I have? …