Seeing the Day Through a Data Lens
A Day of Learning, an AI share, and a quiet realization
I’m privileged enough to work in an organization that has a monthly Day of Learning. The goal is to follow a topic you want to learn about, something you can’t always pursue in the normal course of your work. People hold knowledge‑sharing sessions around things they think others will find interesting. There are many ways to learn.
Unfortunately, I haven’t taken advantage of this day in a long time. I always felt too busy. I’d end up scheduled in meetings or something important would come up, and then the day would be gone.
This week changed that, kind of.
Day of Learning was on Thursday. When I planned my day the night before, I had a short list of things I wanted to continue digging into. I built my plan around chasing those threads, not around learning new tech. Thursday morning came and I told myself I was going to reorganize my day to explore two new bits of tech I was curious about. And then at 8:30, I had a meeting.
The day went back and forth like that. By the end of it, I hadn’t looked at either of the new tech on my list. But I had learned a lot. I spent time in a new‑to‑me codebase and answered questions from the day before. It was better than how I usually use the day.
We also had a team AI sharing. We have a Teams channel where people post things they find interesting. I love that channel. It gives me so many things to explore. For Day of Learning, my new boss asked everyone to share something they had learned. It could be an article, a tip, anything.
As the person with the shortest amount of time using AI, I was dreading it. I sat there hoping time would run out before it got to me. S shared an amazing idea around testing and a suite he built. D shared impressive work tied to the plugin he’s been building. K shared a couple of great tips. K does this every day. He’ll say, “Want to see this?” while sitting next to me, and it’s always worth saying yes.
We got to the last two people and there were still a few minutes left. I couldn’t get out of it. I shared a couple of things I’d been playing with. It turned out not everyone was using the tools I rely on for my data work. I also talked about how I’d had Wiggins help me craft a prompt for Quinn, after teaching Quinn the new codebase, to create some data tracing and a data dictionary for me.
Later that night, I realized I should record a simple video about the data tool I shared. There may be someone else out there who doesn’t know about it, and they might find it valuable.
This brought me back to a question I wrote about in a previous Substack. When do you become an expert? When is it okay to share your learnings?
On Friday, I was reading OpenAI’s December report on the state of enterprise AI adoption. It talked about feature usage. Among monthly active users, about 19 percent have never used data analysis, around 14 percent have never used reasoning, and roughly 12 percent have never used search.
These are tools I use every day. I work across agents, with one agent helping me do work for another as I move between analysis and writing. My Anitta agent is still a work in progress, but I’m not giving up. I can see how we get there.
That was the reminder I needed. I see things through a data lens. Most of what I do flows through it. That’s not a lens everyone shares, and it’s definitely not one everyone is obsessed with the way I am. I’m not sure I could look at things without it.
So maybe during my Day of Learning, I learned to accept that difference. That it’s okay, and that it gives me something specific to share. I should stop comparing what I know with what the other amazing engineers I work with know.
Oh, and the two tech items I wanted to learn about? I managed to get one of them installed today. I’m exploring it now, after going down another data rabbit hole.