A Data Science Central Community
Sooo, this is obviously an outdated thread which I think deserves rekindling (so, yeah they got Osama) as everyone is probably still questioning what value each platform brings to them.
I think the Rexer Analytics survey is really beginning to demonstrate some of the dynamic shifts (and some hints to the reasons in these shifts) in platform choice. I would look at these as I think that, despite some of the survey quirks, the information is rather telling.
In my own experience, I have set up SAS shops, open source shops, open source plus Statistica, SPSS, and other more 'data-sciency' (for what that term is worth) shops. For the statistical work, I am fairly agnostic to the feature sets of each platform (though I would far prefer to write code in R or Python vs. SAS) , but I find that as a value-added approach to many businesses for which regulatory validation of analyses are not an issue (such as in clinical trials), Statistica in league with R as a code based extension (particularly at the server level - such as RevolutionR) unequivocally outsteps the competition in my experience. Outside of using specific financial risk model forecasting methods ARCH, GARCH, etc), there is nothing I have found in 20 yrs that SAS can do that Statistica cannot equally as well.
But sometimes it's the 'comfort' that comes with buying the shoe, almost precisely the same in quality and function as another shoe, but which costs 3x or more the price, that will cause brand attraction. (Ask me about the most recent fight I've had to wage....what dreck!)
As far as customer support, I've begun to feel as if SAS has somehow stratified its support ethic, placing more efforts in those companies who are there prime sources of revenue, and frustrating some of the smaller guys as 'acceptable' churn. Nothing really unusual here, but to your SMB folks, don't promise something you don't intend to give...right?
At one time, SAS was bar none an extraordinary support and service organization with their products. But more recently, I've begun to reevaluate their service side. I'm noticing that rather untrained sales reps are placed in the position to explain how to interpret how SAS does things. To have a sales rep develop and demonstrate to a group of statisticians a forecast using the SAS forecasting suite in such a poor an untrained way is somewhat arrogant by SAS and insulting to me, the person (and statistician) who would be considering their enterprise purchase. I just don't get why SAS would ever let their reputation risk smudging by decisions like these, unless they are arrogant enough to believe there is simply nothing that can touch them based on their brand name alone. In a recent experience I was engaged in, perhaps they could be right.
I notice that they have also appeared to rely on sales reps (versus technical staff) to visit public schools districts which have plunked down half a million on their EvAAS product. I was able to attend one of the post-mortem sessions of an analysis which evaluated teacher effectiveness. It was truly painful to watch the rep try to tap-dance around some quite impressive questions from teaching staff in order to understand what they were receiving. (I examined the *very* flimsy output as well...and what the district was getting certainly did not add up to the robustness that SAS was selling - but that's another story for another audience).
On the other hand, with StatSoft, I've seen nothing but the highest regard for their clients (they have a very fanatical hands on - onsite sort of support ethic in my experience - they just don't say no - and they are also world benefactors, given their support of nations in economic crises - this simply cannot be dismissed and should be highly supported). Of course, a robust set of tools which are adapting to take advantage of new technologies in a very easy to use, cost effective, and scalable manner don't hurt either.
While I think that code based technologies help one understand what (and more importantly why) one is doing what they are doing in an analysis, a GUI interface such as Statistica as I've found doesn't inhibit that understanding. In fact given the emerging spectrum of analytic talent out there (it can be scary sometimes who bills themselves as 'analyst' or 'data scientist'), I would much prefer someone concentrate on conducting a proper analysis than become frustrated with code to the point that the software ends up being used primarily as a VERY EXPENSIVE REPORTING TOOL (yes you all have seen this at one company or another).
I think that with the added graphics capacity of Statistica (it has always been a leader here), you have an extraordinarily powerful tool that can allow experienced statisticians/analysts, develop analysts internally to a higher level - yet another portion of the added-value rubric. It is quite nice when a tool, given its method of use can be used as an educational medium as well as a production suite, without obscuring the concepts needed to develop the analysis in code - the point for the analyst is to clearly understand the statistical concepts FIRST, not coding. In business, forcing both is in my opinion a burden ramp up time for getting analysts to a productive level. I say this and I'm quite comfortable in SAS or R and do eventually recommend code based development as a downstream part of an integrated curriculum.
In all, I see each product has its value and niche. But to say that SAS is the king of the giants is simply nonsense. And given where value in total cost of ownership, ease-of-use, scalability, extendibility, and level of service/support are all critical to business in the 'inference of information' age, I think that Statistica is a no-brainer, which is why I have migrated over to them (and leaves change in my pocket to build up hardware for the big-data and heavy visualization/animation work).
Now, once they begin to support CUDA processing and begin research on Dremel/Drill structures for really speedy data I/O , I believe they could begin to consider carrying a scepter at least :).