The first problems you mention perhaps explain why some aggregators sell insights vs. raw data. This approach solves for non-excludability (presuming that desired insights vary by buyer) and also allows price discrimination (e.g. for MLS, you're given the non-competitive economic/investment question the data needs to answer beforehand and price accordingly).
The flip-side problem is that generating quality insights has historically required non-scalable human effort and drastically reduces margin. Arguably, new AI 'data analysts' can change the game here by allowing data owners to create an insights business with more scalability and less investment (this is our entire focus at Datacakes).
The first problems you mention perhaps explain why some aggregators sell insights vs. raw data. This approach solves for non-excludability (presuming that desired insights vary by buyer) and also allows price discrimination (e.g. for MLS, you're given the non-competitive economic/investment question the data needs to answer beforehand and price accordingly).
The flip-side problem is that generating quality insights has historically required non-scalable human effort and drastically reduces margin. Arguably, new AI 'data analysts' can change the game here by allowing data owners to create an insights business with more scalability and less investment (this is our entire focus at Datacakes).
Very interesting, thanks for sharing