What I Notice That Others Miss
Five observations from working closely with data systems.
Working closely with data systems has taught me that the obvious problem is often not the real problem.
The problem isn't always bad data. Sometimes it's irrelevant data.
A pipeline can be technically correct and still create unnecessary work. One of the most valuable decisions in a data system is deciding what does not need to be collected, processed, or monitored.
A technically correct dashboard can still be useless.
Showing more metrics doesn't necessarily create more value. The real question is whether someone can look at the output and understand what decision they should make next.
The person who understands why a system exists has a different kind of knowledge.
Systems accumulate decisions over time. Knowing the history behind those decisions often matters as much as knowing the code. Context is part of the system.
Automation should remove repetition, not judgment.
Good automation handles predictable work so people can spend their time on investigation, prioritization, and decisions that still require context.
More data does not automatically mean more intelligence.
Intelligence comes from identifying the signal that matters. A smaller, relevant dataset can be more valuable than a massive dataset nobody knows how to use.
Continue exploring: System Playground · Failure → Learning · All of Thinking & Systems
I like building things. But I'm more interested in understanding why they should exist.
That's the space where engineering, data, product thinking and business decisions start to overlap.