Re: That there real world...
OK, I get that. What they seem to be doing is cleaning up real world logs to present their system's best guess of what the logs should look like to train another system to spot discrepancies in real world logs.
What could possibly go wrong?
Scenario 1: Say they get 90% of the input doing one thing, 5% doing something else and 5 off 1% doing individual other things. They decide that the 90% is what's right, clean up the remaining 10% to look similar and train the second system on those. The second system gets more examples of the 5% & starts flagging them as errors. In fact that was a legitimate outcome but because the imputation system fudged the data the second system was mistrained. Note that in order to do its thing the imputation system must have noted these variations and could usefully have flagged these as something to be reviewed by an actual real live expert.
Scenario 2: Same sort of results but all the discrepancies are simply failures in the logging system. The second system starts throwing errors looking at real world data because the logging system is making similar errors. The logging system is not fit for purpose and no amount of cleaning of the training data is going to fix it.
The application area seems to be logistics. Any time I've been on the (non-)receiving end of a logistics error it's been fairly clear to me that something hasn't been scanned in or out when expected. What's missing is Real Intelligence when designing and implementing the system to raise and alarm in real time when the expected has failed to happen. No amount of Artificial Intelligence applied after the event is going to fix the problems in anything like an effective manner.