It entirely depends on what you're trying to deal with. You may solve some problems there but looking at real data is a must for solving others.
How do you ensure your fuzzed data has statistical signal that approximates those present in the true data?
That’s testing not modeling. If someone asks you to do analysis of a drug trial and you come back with a report based on fictitious user data because “devs should not have access to real data” you’ll be laughed out the room.
That doesn't have to mean it's worse though. Testing with 10,000,000 fictitious accounts generated to cover lots of permutations of user data is a great idea even if the real data won't ever have some of those permutations. It's a testing technique called "fuzzing", and it's quite common.