1. kdb+ was (and maybe is) a good solution to the problem that we had: doing complex data manipulation/simple statistical calculations against billions of rows of time series data. Hadoop is the term du jour for data processing, but truth of the matter is that finance doesn't have really huge data. At best, it's a couple of terabytes, and most of the time, you are working with a small subset of it. Running KDB+ on a beefy server or two would usually do the job (rather well).
2. Maybe because I studied math, but I find k/q's vectorial/functional sematics appealing. I think the syntax is horrible, but the semantics is very neat.
3. Finally, because it helped me keep my job. It was rather amazing to me that all these Ph.D. statisticians that I worked with couldn't bring themselves to learn kdb+ effectively. Apparently this stuff can be very hard for even the smartest people (or maybe they thought it was such a niche skill with a low ROI).