Embedded/edge ML folks: what actually eats the most time ,getting data, or cleaning/labeling it (time series sensor data, not computer vision/audio)? [D]

I'm trying to understand where people doing sensor based ML on microcontrollers (IMU, accelerometer, vibration ,that kind of time-series data) actually lose the most time. When you've built something like this, what was the bottleneck: Getting enough real world data in the first place? Cleaning / la