Ask HN: What is the biggest untapped opportunity for startups?

It's not that big problem for you then.

Figure out a way to reduce the not-in-San-Francisco penalty for startups.

There are some very good reasons why startups flock to the bay area, including "lots of available talent" and "that's where the VCs are", but there are also problems with being in the bay area -- talent is considerably more expensive (due in part to the cost of housing) and visa issues (particularly under the current presidency) being the first two which come to mind.

If you can find some way to give non-San-Francisco startups the same advantages that San Francisco startups have -- better tools for remote workers, for example, so that companies can easily hire from anywhere rather than needing to be where the largest number of potential employees are found; or something to make VCs interested in investing in companies which aren't within a narrow radius of Sand Hill (since I've never dealt with VCs, I have no idea what such a solution would look like) -- then you'll create a huge amount of value for companies around the world and it should be easy to transfer some of that value into your pockets.

Container ships are already going slower:

https://www.theguardian.com/environment/2010/jul/25/slow-shi...

I spent many years repurposing cartographic tools myself. :-)

Three big missing features off the top of my head:

- Insufficiently correct and high precision computational geometry, which compounds with the iterative/recursive nature of many complex sensor analytics. Many people don't notice unless they ground truth their analytic process; I learned this the hard way. For many industries, 1% cumulative computational error is a catastrophic bug for analytics and the reality can be much worse in many common systems.

- Lack of first class tessellation types and operators. Once your data scientists have them, they'll wonder how they lived without them. Such things are completely useless for cartography and therefore don't exist in those platforms.

- High-performance computational geometry. This is particularly noticeable if you work with sensor coverages (like drone data). Your typical cartographic system has serious difficulty joining a few terabytes of complex polygons, but these are tiny data sets for many remote sensing sources. It literally takes weeks or months to run these types of queries. You can optimize this to be much faster but there was no pressing market need in cartography and cartographic systems aren't designed for scale-out generally.

A friend is working on an IoT problem with two of your three capabilities: storage and analytics. He has voiced similar concerns. (To be fair, he gripes constantly that nothing exists which would work for him, within the budgetary and timing constraints.)

His company provides sensor systems for civil engineering projects. A single large bridge can have sensor packs every 50m or so - per beam. The amount of sensor data coming in for a single municipality or region is already staggering. Vibration and stress analytics are required on a daily basis.

The final requirement, one which you didn't mention, is that this setup should be fairly low maintenance. If you need a team of rocket scientists to operate and just keep it from falling over, the cost structure will be unsustainable.

A service that provided all this in a platform with sane APIs and good BI integration should be making tons of money.