To really grok why this is useful, put yourself in the shoes of a data warehouse user or administrator. This is a farm of machines with disks that hold data too big to have in a typical RDBMS. Because the data is so big and stored spread out across machines, a whole parallel universe of data processing tools and practices exists that run various computations over shards of the data locally on each server, and merge the results etc. (Map-reduce originally from Google was the first famous example). Then there is a cottage industry of wrappers around this kind of system to let you use SQL to query the data, make it faster, let you build cron jobs and pipelines with dependencies, etc.

Now, so far all these tools did not really have a common interchange format for data, so there was a lot of wheel reinvention and incompatibility. Got file system layer X on your 10PB cluster? Can't use it with SQL engine Y. And I guess this is where Arrow comes in, where if everyone uses it then interop will get a lot better and each individual tool that much more useful.

Just my naive take.