A few years ago, I wrote an article for Harvard Business Review, pointing out that data scientists should pay more attention to predicting "invariables" rather than predicting "variables". See here.
The gist is: it is a lot more reliable to predict an outcome that is invariable, and there are plenty of problems out there of this nature that are being ignored. So those are low-hanging fruits.
It's more sexy to say you can predict when someone dies, or when the peak flu season occurs, etc. but because the outcomes have high variability, the predictive accuracy is "upper bounded".
This past month, I have been waiting and waiting to see if my credit card company would become smart. The chip on my chip card died. Every time I use the card, at least once a day, I am accumulating data points - each one tells the same story: the chip is dead - the chip is dead. The chip is DEAD.
It usually takes at least three failures for the tablet software to allow me to swipe. Sometimes, the software is so dumb that it will keep asking for re-tries infinitely. When will the software learn that my chip is dead? Ideally, some system is monitoring the data and be able to predict that my chip has failed which triggers a new card to be sent to me.