I often ponder how much of the "old world" will get "digitalized" — translated in numeric form, bits. And how much will just disappear. The question might seem trivial if you think of books, but now think of architecture, language itself (as it evolves), etc.
There's almost no question in my mind that most new data will endure in some form, by virtue of being digital from day 1.
The endgame for such a company, imho, is to become the "source entity" of information management (in abstracted form), whose two major products are one to express this information in the digital space, and the other in the analog/physical space. You may imagine variations of both (e.g. AR/VR for the former).
Kinda like language in the brain is "abstract" (A) (concept = pattern of neurons firing) and then speech "translates" into a given language, like English (B) or French (C) (different sets of neurons). So from A you easily go to either B or C or D... We've observed that Deep Learning actually does that for translation (there's a "new" "hidden" language in the neural net that expresses all human languages in a generic form of sorts, i.e. "A" in the above example).
The similarities of the ontology of language, and the ontology of information in a system (e.g. business) are remarkable — and what you want is really this fundamental object A, this abstract form which then generates all possible expressions of it (among which a little subset of ~1,000 gives you human languages, a mere 300 active iirc; and you might extend that into any formal language fitting the domain, like engineering math/physics, programming code, measurements/KPI, etc.
It's a daunting task for sure but doable because the space is highly finite (nothing like behavior for instance; and you make it finite through formalization, provided your first goal is to translate e.g. business knowledge, not Shakespeare). It's also a one-off thing because then you may just iterate (refine) or fork, if the basis is sound enough.
I know it all sounds sci-fi but having looked at the problem from many angles, I've seen the PoC for every step (notably linguistics software before neural nets was really interesting, producing topological graphs in n dimensions of concepts e.g. by association). I'm pretty sure that's the future paradigm of "information encoding" and subsequent decoding, expression.
It's just really big, like telling people in the 1950's that because of this IBM thing, eventually everybody will have to get up to speed like it's 1990 already. But some people "knew", as in seeing the "possible" and even "likely". These were the ones who went on to make those techs and products.
Digital data is arguably more fragile than analogue, offline, paper (or papyrus, or clay tablet) media. We have documents over 3000 years old that can still be read. Meanwhile, the proprietary software necessary to access many existing digital data formats is tied to obsolete hardware, working examples of which may no longer exist, emulators for which may not exist, and insufficient documentation may exist to even enable their creation. Just as one example, see the difficulty in enabling modern access to the BBC's 1986 Domesday Project.
There's almost no question in my mind that most new data will endure in some form, by virtue of being digital from day 1.
The endgame for such a company, imho, is to become the "source entity" of information management (in abstracted form), whose two major products are one to express this information in the digital space, and the other in the analog/physical space. You may imagine variations of both (e.g. AR/VR for the former).
Kinda like language in the brain is "abstract" (A) (concept = pattern of neurons firing) and then speech "translates" into a given language, like English (B) or French (C) (different sets of neurons). So from A you easily go to either B or C or D... We've observed that Deep Learning actually does that for translation (there's a "new" "hidden" language in the neural net that expresses all human languages in a generic form of sorts, i.e. "A" in the above example).
The similarities of the ontology of language, and the ontology of information in a system (e.g. business) are remarkable — and what you want is really this fundamental object A, this abstract form which then generates all possible expressions of it (among which a little subset of ~1,000 gives you human languages, a mere 300 active iirc; and you might extend that into any formal language fitting the domain, like engineering math/physics, programming code, measurements/KPI, etc.
It's a daunting task for sure but doable because the space is highly finite (nothing like behavior for instance; and you make it finite through formalization, provided your first goal is to translate e.g. business knowledge, not Shakespeare). It's also a one-off thing because then you may just iterate (refine) or fork, if the basis is sound enough.
I know it all sounds sci-fi but having looked at the problem from many angles, I've seen the PoC for every step (notably linguistics software before neural nets was really interesting, producing topological graphs in n dimensions of concepts e.g. by association). I'm pretty sure that's the future paradigm of "information encoding" and subsequent decoding, expression.
It's just really big, like telling people in the 1950's that because of this IBM thing, eventually everybody will have to get up to speed like it's 1990 already. But some people "knew", as in seeing the "possible" and even "likely". These were the ones who went on to make those techs and products.