As an analyst, I’m obsessed with metrics, tracking, and KPIs. That’s why the concept of "Planning Before Building" resonated so deeply with me during our TS Academy session.
Automation shouldn’t be deployed just because a tool looks cool or promises to save time. It needs to
align with an actual SMART goal. If you can’t measure the performance impact of your workflow whether it's reducing processing time by 40% or eliminating manual copy-paste errors completely you shouldn't be building it yet.
Automation doesn't replace the need for clear
Every Data Analyst knows the pain of the "Dirty Source."
You sit down to build a clean dashboard or run an evaluation report, but instead, you spend the first three hours chasing missing files, manually copying rows from an email attachment, and fixing mismatched data types.
I’m no longer looking at automation as just a way to reply to emails faster. I see it as a superpower for anyone who handles data, tracking, or evaluation. It ensures our insights are built on a solid foundation, free from human copying errors.
If you are a professional working
I used to look at tools like ChatGPT and Claude through the lens of a Data Analyst. To me, they were highly capable, isolated calculators. Great for fixing a Python script or summarizing a research paper, but ultimately operating as siloed browser tabs.
A Thread..
I used to look at tools like ChatGPT and Claude through the lens of a Data Analyst. To me, they were highly capable, isolated calculators. Great for fixing a Python script or summarizing a research paper, but ultimately operating as siloed browser tabs.
A Thread..
to connect data, APIs, and automation engines to build reliable, scalable, and ethical workflows.
I'm in the sandbox, and I'm documenting every single breakthrough, breakdown, and system build right here.
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