In partnership with
The Future of the Content Economy
beehiiv started with newsletters. Now, they’re reimagining the entire content economy.
On November 13, beehiiv’s biggest updates ever are dropping at the Winter Release Event.
For the people shaping the next generation of content, community, and media, this is an event you won’t want to miss.
When I first started working in data, I thought it was all about speed. Faster queries, faster reports, faster everything.
I would spend late nights chasing efficiencies, rewriting SQL to save seconds, obsessing over the perfect visualization layout.
But I was exhausted and somehow always behind. Then I met someone who worked half my hours and seemed twice as effective. He was not louder, smarter or luckier than me, he just had systems that made his work lighter.
That is when I realized the difference between productive data professionals and efficient ones.
Productive people finish tasks. Efficient people redesign how those tasks happen.
Here is what I have learned from watching, and then later one becoming one of them.
They Automate Friction, Not Work
Most people think automation means writing Python scripts or setting up complex pipelines.
Efficient analysts start smaller, they notice pain.
If something annoyed them twice, they fixed it once.
One click to refresh data instead of five. A Slack reminder that replaced a mental note. A saved query template that killed ten minutes of repetition.
They were allergic to friction, not necessarily obsessed with the code like most people think.
They Use AI as a Mirror, Not a Machine
Everyone is feeding prompts into ChatGPT those days hoping for shortcuts. Efficient data people use it differently.
They ask AI to explain, not execute. To summarize a query, to translate a technical finding into business language. Not to do their work, but to help them see their work more clearly.
It is reflection, not automation.
They Document for Themselves, Not Compliance
When I asked one senior analyst why his documentation was so clean, he laughed. He said “it is not for anyone else, it is for me six months from now”.
That line stuck with me. He did not write pages of Confluence notes to tick boxes, he kept a running log of his thoughts, decisions and experiments made along the way.
They Design for Use, Not Applause
You can tell who built a dashboard for themselves and who built it to impress.
The flashy ones have a dozen charts that answer nothing. The efficient ones have three that drive action.
They think like operators, not artists. Their dashboards work because they are designed for decision making, not decoration.
Personally, I have failed on this one for a very long time as I used to obsesses about the visual aspect of the dashboards/reports I created. I still believe they add value, but if you had to choose one, then for sure you should aim the ones that addresses a pain point.
They Build Feedback Into the System
The best analysts do not wait for performance reviews to learn what is working and what not.
They create small loops, Slack pings when metrics move, quick surveys after reports, a one line “was this useful?” message to the stakeholders.
The feedback is constant, quiet and fast.
And that is why their work keeps improving while everyone else is waiting for direction.
The Real Reason They are Efficient
Efficiency is about mental space and not speed.
The most efficient data professionals I have met are faster and calmer. Their workflows breathe. Their minds are not cluttered with a hundred micro decisions.
Because when the small stuff runs itself, you finally have time to think about the big stuff, the why behind the data, not just the what.
So, start there.
Automate one friction.
Capture one insight before you forget it.
Ask one better question (now AI can help you with answers).
The calm will follow.
Every week, I share practical lessons on data, AI and systems thinking, the kind that make your work (and week) lighted.
Subscribe to get the next edition.
Read also: Why "Data Analyst" is the Wrong Title