The biggest myth in data teams is that data workflow is a straight line.

Data → Model → Insight → Decision

That’s not how real systems work, it only looks like that on slides.

But in reality, nothing is linear.

Before you even get to insights, you’re already stuck in definitions, broken data sources, conflicting metrics, and teams that don’t agree on what “truth” means.

Then comes modeling, where assumptions change mid-way.

Then governance, access, and compliance slow everything down.

And even after launch, adoption fails if the business wasn’t aligned on the problem from the start.

Most data teams fail because they assume the workflow is clean and linear, not because of poor analytics.

Data work is messy, iterative, and full of rework.

That’s the reality.

Which part of the data process slows your team down the most?