If you are a data scientist, then there is very little use for pre-packaged, generally inflexible commercial tools.
Part of the reason OSS is so prevalent and useful in data science is that you will often need to combine and/or modify a procedure to fit the needs at hand -- and then deploy it without a bunch of lawyers and sales reps getting involved at every step.
Since data scientists are expected to be proficient programmers, you should be comfortable digging into the source code and adding functionality or making it more user friendly.
I've come close to recommending the purchase of non-free(as in GPL) a couple times only to find that some industrious person has set up a project in Git that provides most if not all of the functionality if commercial software. In the cases where it doesn't, it at least addresses the core issue and I can modify and extend from it. It's much easier to modify a prototype than start from scratch.
Bottom line: be wary of commercial software for data science unless you've done your due diligence in the OSS space and can honestly say that you could not find any projects that could be modified to suit your needs.
Commercial software is not only less flexible but you're effectively in a business partnership with these folks and that means your fates are somewhat intertwined (at least for the projects that depend in this software).