Dataqualityistherealbottleneck
The gap between a genuinely good financial AI system and a mediocre one is rarely the model itself — most teams eventually converge on similar modeling techniques. The real gap is almost always in the data feeding it, and it's usually invisible until something goes wrong. A timestamp that's off by a few seconds looks harmless until it means a system is technically trading on information it shouldn't have had yet. A corporate action — a stock split, a dividend, a ticker change — that isn't properly labeled can silently distort a return series for years without anyone noticing, because the numbers still look plausible.
These aren't exotic edge cases; they're the ordinary condition of real-world financial data, which is messier, more inconsistent, and more full of quiet gaps than most people outside the work assume. A model built on flawed inputs will still produce confident-looking outputs — that's exactly what makes bad data so dangerous. It doesn't announce itself as an error. It just quietly shifts the ground the entire system stands on, long before anyone traces a bad result back to its source. Getting the modeling right matters. Getting the data right is what makes the modeling worth doing at all.