In some solutions with a manageable scale and a reasonable tolerance for a certain amount of data loss and inconsistency, this approach may be just fine. There are very good reasons for inconsistencies between sets of data that come from different sources. When 20 million fact rows are obtained from an online ordering system and .1% don’t have matching customers records that come from the system used to manage the master customer list, it may simply be a question of timing. Or, maybe the definition of “customer” is slightly different in the two systems. Chances are that there are also legitimate data quality issues affecting a small percentage of these records.
Whatever the case may be, a data conformity or potential data quality issue in this design scenario falls on the model designer to either fix or ignore. Again, this may or may not be a concern but it is a decision point and might raise the question of ownership when questions about data quality are raised.
Paul then goes on to show how this gets fixed in a traditional model and where you need to watch out with SSAS Tabular. Good essay worth reading.