Honestly, this stuff is so bad that the headache of it might fully justify huge finance compensation, and I’ve had colleagues who turned down huge bonuses and raises to leave finance companies solely to avoid this type of stuff and seek a career where the headaches bother them less and they are paid less.
Even medical records do not present the same degree of esoteric data formatting and mismatching. It’s not really even a matter of data cleaning. It’s that there is _no_ way to clean the data, and the only useful approach is to pay 10s of thousands of dollars to data vendors whose products have intractable errors, and then build huge data vslidation and imputation systems around it.
When it boils down to fiduciary duty to the client, and you have a contractual obligation regarding portfolio composition, then you can’t live with “good enough” data cleaning. Even one single asset with an incorrect identifier from your data vendor can cause you to e.g. invest in an Israeli company in a portfolio with a client obligation to invest in no Israeli companies (that is a real example I encountered before).
Not an engineer, so while I understand this in a general/abstract sense, my understanding is limited to, "Cleaning/transformation is messy and a time sink due to non-standardization of data."
Similarly, say a vendor aggregated website visits/pageviews but didn't account for the fact that 1/3 of the traffic was coming click-bots in developing countries. If they presented you with the raw data you could figure it out and filter those countries out, but if it is aggregated, you might not discover the issue.
Then, there could be even simpler ones like determining the opening price for a stock. If say the first print of stock XYZ trades 10 shares at a price of $20, but a millisecond later, 100k shares trade at $20.11, which print should you use in your simulation algorithms as the opening print?