I can give it a try. :)
There is an assumption that unique data enables unique insights, which can presumably can be monetized in some fashion. As long as no one else has access to your unique data, you have pricing power for the unique insights. There are loads of companies, big and small, trying to execute this business model.
A problem with this model is that for practical purposes there are no such things as "unique insights". The only thing unique data grants you is a cheap path to a specific set of insights. For every set of "unique" insights, there is almost always many sets of unrelated data sources that can be analytically combined to deliver the same insights. In the slightly seedy underworld of data model brokers, I've seen some very impressive examples of this. The way these alternative data models make money, despite the creation process being more expensive, is that they are positioned as a cheaper alternative to companies that think "unique data" means they can extract monopoly rent. The explosion in data source availability has slowly made these clever alternatives more prevalent.
Currently, the alternative to having access to the unique data is to do significantly more expensive computations on what are typically larger and more diverse data sets. This keeps it from becoming a runaway race to the bottom due to the higher cost. Note that in both cases, the parties are using conventional data infrastructure stacks with their implied limitations.
In recent years I've done extensive studies of the cost structures of these types of businesses. It turns out that if you can reduce the end-to-end data infrastructure costs by an order of magnitude for the reconstructive approach then your total costs will be far below the break even point for the conventional "unique data" approach. Furthermore, the computational work required to replicate one high-value data model is substantially reusable for other data models, so the more data models you reconstruct, the lower the marginal cost of reconstructing additional data models this way. Done at scale across enough data models, the amortized cost of reconstructing unique data can be less than using the unique data! People have been idly thinking about what it would take to do this for a few years. Collecting the rare skillsets required to pull it off is a major hurdle for any company.
The notion that it is possible to reduce end-to-end data infrastructure costs by (at least) an order of magnitude relative to conventional data infrastructures is well-supported but it raises another question: why can't the "unique data" companies do the advanced engineering required to have such an infrastructure themselves (it isn't something you can do with open source currently)? The simplest answer is that it is difficult to justify extremely expensive engineering efforts outside of an organization's core expertise solely to prevent erosion of the market value of their unique data. Fundamentally, it is a shift to competing on infrastructure instead of data, which is an improbable transition for companies.