If you can. I thought the context of this thread was free-form postmortems, where people give a narrative explanation of why the startup failed. It's hard to aggregate that.
Also, there's the risk of aggregate counts simply reflecting the population of each job function, unless you weight them. Then it'll just say that every engineering-focused startup failed for engineering reasons and every sales-focused startup failed for sales reasons, which may be true but isn't terribly helpful for a founder trying to figure out what to do.
It's an interesting thought, almost like a "glassdoor.com" on startup failures. I'd be curious if Glassdoor actually implements some sort of a weighting scheme to counter the bias that unhappy people are more likely to post reviews, so a poorly run group would have an outsize impact.
It's because of that case I think free-form postmortems might be more insightful, despite their own obvious faults. Perhaps NLP and sentiment analysis could shed some light.
Also, there's the risk of aggregate counts simply reflecting the population of each job function, unless you weight them. Then it'll just say that every engineering-focused startup failed for engineering reasons and every sales-focused startup failed for sales reasons, which may be true but isn't terribly helpful for a founder trying to figure out what to do.