> must have already solved the problem at hand Assuming the problem at hand is analyzing the data. I don't know enough about what they're building to understa

> must have already solved the problem at hand Assuming the problem at hand is analyzing the data. I don't know enough about what they're building to understand what use cases they have in mind. (Their blog says "developing new features or exploring insights", which is a bit vague. See https://medium.com/gretel-ai/gretel-readme-fd0c4eff8a09 https://medium.com/gretel-ai/gretel-readme-fd0c4eff8a09 .) But if this allows people to take their production database, run a command, and generate fake data they can run integration tests against, then that's not analyzing the data. It's making it easier to run tests in an environment that is as close as possible to production, because data is part of an environment. Aside from that, I'd argue that some kinds of analysis may even still be possible. Let's say you have an e-commerce site, with one database table for user accounts and another table for shipping addresses. And let's guess that Gretel anonymizes this data by creating a user table with different usernames and an address table with made-up shipping addresses, but the overall structure is isomorphic: for every row in real data, there is a corresponding row in fake data, just with different values in it. And the (synthetic) keys aren't private (they data you generated, not collected from the user), so they can be preserved. Then you can, on the fake data, run a query and find out what percentage of users have given a shipping address. Of course it's just a guess that that's what they're aiming to do since so little detail is available. It's entirely possible that what I described isn't what they're planning to do. Maybe it doesn't sufficiently protect privacy. But the point is, until we know what they're building, I don't know if we can conclude that people should run away.