Most of the leverage is in the
efficiency at exploiting data, and not just in technical terms but also operational economics. This is far more important than the data itself for a fundamental reason that has not been broadly internalized by the industry at large, and which makes it difficult to build a data moat.
Almost any proprietary data model based on proprietary data can be reconstructed with sufficient fidelity from unrelated external data sources to be competitive with that proprietary data model, at the cost of being somewhat more expensive to engineer all things being equal. I've demonstrated this many times in practice. As a corollary, a company with sufficiently efficient end-to-end data infrastructure could, in principle, commoditize all proprietary data model companies. This company does not exist, yet, and it would require some very specialized talent but every necessary ingredient exists.
This is a realizable endgame due to the reality that virtually all data companies, for good and practical reasons, are strongly incentivized to build on infrastructures that are literally orders of magnitude less efficient than is possible in principle. A company that was purpose-built on exceptional end-to-end data infrastructure engineering could capture much of the revenue in these markets in a surprisingly short time by commoditizing the data model and arbitraging efficiency.
Almost any proprietary data model based on proprietary data can be reconstructed with sufficient fidelity from unrelated external data sources to be competitive with that proprietary data model, at the cost of being somewhat more expensive to engineer all things being equal. I've demonstrated this many times in practice. As a corollary, a company with sufficiently efficient end-to-end data infrastructure could, in principle, commoditize all proprietary data model companies. This company does not exist, yet, and it would require some very specialized talent but every necessary ingredient exists.
This is a realizable endgame due to the reality that virtually all data companies, for good and practical reasons, are strongly incentivized to build on infrastructures that are literally orders of magnitude less efficient than is possible in principle. A company that was purpose-built on exceptional end-to-end data infrastructure engineering could capture much of the revenue in these markets in a surprisingly short time by commoditizing the data model and arbitraging efficiency.