The Analyst Gets Access and Observes the Source System
Eventually, someone gives you access to the system. You log in and see it for the first time—not the dashboard, not the warehouse table, but the actual place where the data is born.
Suddenly, things start to click. That “active” flag? It’s only set when a user logs in for the first time. The "status_updated_at" timestamp? It’s overwritten on every status change, not appended. The reason signups drop to zero at midnight? There’s a daily reset job that quietly fails on Sundays.
Once you see how the system works, everything downstream starts making more sense. You're not just querying data anymore; you’re tracing its life cycle. You stop taking fields at face value. You stop assuming the pipeline always works. You stop trying to force a story out of incomplete information.
This is what separates good analysts from great ones. The great ones get curious. They walk over to the engineer and ask how a table gets filled. They sit with the ops team and watch what happens when a ticket is closed. They don’t just look at the data—they learn where it comes from.
Three things that make a huge difference:
- Shadow someone who uses or manages the system. Even 15 minutes can reveal things you’d never guess from the schema.
- Map out the data flow. Tools like dbt docs or even a whiteboard help connect the dots.
- Build a glossary of “gotchas.” Track the quirks—fields that lag, columns that mean something different than their name.
If you’ve ever been frustrated by confusing or inconsistent data, you’re not alone. But the more you understand the source, the less time you’ll spend guessing—and the more your insights will actually reflect reality.
