How can we assume this "tracking" directly populates a dataset without any data loss or misinterpretation? The weakest link here is always the assumption of perfect pipeline integrity between observation and actual data use.
In my experience, even with automated systems, raw data often requires significant cleaning, transformation, or manual intervention before it's reliable for analysis.
If the tracking mechanism has a bug, or if an API for a new model changes and the script isn't updated, that model's growth simply won't be reflected in the dataset.
We've seen this snap when a simple certificate expired and broke an entire data feed for weeks, creating a huge blast radius of outdated information.