In science, a lot of people use ASCII and flat files. I used to really dislike it, but over time I understood that there are certain practical reasons to do this which deserve respect.
Due to the volume and novelty of data that we work with, we are often pushed into a corner between human time and machine time. Each data set comprises a new set of concepts, and each is huge. In this corner, sometimes a character-delimited file is the best solution. There is not time to carefully craft a binary format and then document it so it will not be forgotten later, nor is there time to wait for a general-purpose format parser to operate on tens of billions of records. We need a solution that can be designed in 1 minute and be legible by all of our tools without modification.
Typically, I have used tabs in the place of the ASCII separators. This ensures readability without any kind of parsing. Also, this lets me use the default behaviors of well-worn, bug-free tools in the core of the Unix toolchain for basic data processing tasks. Frankly, this is not a bad compromise.
If you are passing messages around a web stack, JSON, XML, and friends are ideal solutions. If you have to occasionally deal with CSV, use a parser. I just want to note that for many tasks in data analysis, it's OK to simply use the dead and dusted convention of mixed delimiters and data.
As these things develop, I will be trying to investigate how to use more modern formats such as binary JSON representations in my work, and I'd be curious what solutions people here suggest for working with very large data (e.g. many trillions of observations).