[talbina]

There was a company that applied to YC that wanted to do the "Google Docs for Statistics" but was rejected. They wrote about it in a blog post but I can't find it. They ended up not launching.

It will be worth it to connect with these people to see if there is anything that can be learned from them.

[glaugh]

Definitely let us know if you think of their name or dig up their blog post. Sounds interesting.

[TrevorBurnham]

I believe the company talbina's thinking of is mine: We applied as Theoryville, got interviewed, got rejected, applied to Betaspring (http://betaspring.com/), got accepted, changed our name to DataBraid, and proceeded to fall apart over the course of a summer.

I do think the idea has a lot of potential, and what StatWing has built is already more complete than what my team managed to build in 3 months. A few suggestions I'd offer based on that experience:

1. Parsing CSVs is easy in theory, but painful in practice, because CSVs in the wild tend to be full of junk. I would provide a JSON API that makes it easy for developers to put data in your system directly, allowing people to build their own CSV parsers for you.

2. Use GitHub as your model. You want people to collaborate around data the same way that developers collaborate around code. Just about every day when I was doing DataBraid, we'd discuss a use case and then say "Oh, GitHub already figured out the right way to do this." The most compelling use case here is that researchers can run different sets of tests on the same data and discuss which approach is the most valid/insightful.

3. Getting to revenue will be hard, but having paying customers will make it much, much easier to attract investment. So find the MVP that people will pay for and put everything else on a "nice-to-have" list.

Best of luck!