> reasoning about all of the potential impacts of the change was hard It's far easier for us as humans to think "this change will affect new data going forward

> reasoning about all of the potential impacts of the change was hard It's far easier for us as humans to think "this change will affect new data going forward" rather than "when I make a change in the present, it will transform all data from the past as if it had always been the case." Perhaps comic book writers would make good event sourcing engineers - but I digress. One of the saving graces of event sourcing is that you can spin up a parallel world where you make those changes, rerun your entire event history, and do a diff between your production derived data and your proposed production derived data as of a point in time, identifying the actual situations where pathological cases would occur. But this takes a lot of discipline, and it's not perfect, because events don't exist in a vacuum - they are snapshots of a human being taking an action relative to the data they saw at the time, and if you're retconning the data they saw, it might not match the reasons they took the actions they did. A common pathological case I find is: "Someone overrode value X to Y, now I have the ability to automate that fix - but did they mean to freeze Y for a different business reason outside the one I did? If at some time in the future it changes upstream from X to Z, should I keep the user's intent to freeze Y or change it to Z because the freeze to Y was just because we didn't have the fix?" Relational approaches don't solve this, to be sure, and in fact there's worse tooling to have good answers to this question. But they do incentivize the discipline of talking to stakeholders before making a giant wide-scoped UPDATE on data at rest. Event sourcing's power to retcon makes it far too easy to say "let's just reinterpret the history, what's the worst that can happen?"