The market opportunity is very large, but also insanely difficult to tap.
For one, you have an uphill battle on trust. Customers have to trust your data is secure, and btw it’ll never be 100% secure by many standards.
On the other hand, people have to trust the synthetic data is good enough to use Practically.
So you have to both convince management and convince engineers. Arguably management is easier to convince, but.. best of luck on the endeavor!
That’s not even discussing the technical challenges - I’ve implemented this all technically and have had it deployed to production systems. Building a robust system that is secure and produces valid synthetic data is a challenge.
https://medium.com/capital-one-tech/why-you-dont-necessarily...
The market opportunity is very large, but also insanely difficult to tap.
For one, you have an uphill battle on trust. Customers have to trust your data is secure, and btw it’ll never be 100% secure by many standards.
On the other hand, people have to trust the synthetic data is good enough to use Practically.
So you have to both convince management and convince engineers. Arguably management is easier to convince, but.. best of luck on the endeavor!
That’s not even discussing the technical challenges - I’ve implemented this all technically and have had it deployed to production systems. Building a robust system that is secure and produces valid synthetic data is a challenge.