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:

1. Getting enough real world data in the first place?
2. Cleaning / labeling / organizing the data you have?
3. Actually building and training the model?
4. Getting it optimized and deployed on the device?

I am working on a project that aims to eliminate some of these pains and wanted to get some validation on this topic first before I go and add more features. It is essentially edge impulse, but hardware agnostic, gen ai native, and targeted for time series data. I am still trying to figure out what the best vertical would be as there are many to choose from.