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.
On that last point, you can execute an effective denial of service attack on most GIS databases with a well-crafted set of polygons and polygon intersection queries. It doesn't even require malicious intent. (Yet another thing I learned the hard way.)
