What does it mean to clean the data?

Do you remove those weird implausible outliers? They're probably garbage, but are they? Where do you draw the line?

If you've established the assumption that the data collection can go wrong, how do you know the points which look reasonable are actually accurate?

Working with data like this has unknown error bars, and I've had weird shit happen where I fixed the tracing pipeline, and the metrics people complained that they corrected for the errors downstream, and now due to those corrections, the whole thing looked out of shape.