There are also a number of open source alternatives, including Octave, Scilab, Sage, and PySci, one of the aforementioned Python libraries. All of these tools help with the complicated statistical analysis that is now becoming common for firms trying to understand what the customer did and what the customer may want to do in the future.
Programming languages on the rise: R
Statistical analysis is being increasingly done in R these days, although some purists call the language S, its original name. Tibco sells a commercial version called S-Plus.
[ For an in-depth primer on what enterprises are doing with "big data," see "The big promise of Big Data: What you need to know today." ]
There probably won't be an S++ because the language is more a version of LISP or Scheme with additional features for computing statistical functions and then displaying the results in pretty pictures. If the boss wants the computer to churn through billions of lines of log files looking for patterns, clusters, and predictive variables, R or S is a well-loved solution.
R is another Swiss Army Knife of numerical and statistical routines for hacking through the big data sets -- collections big enough that it might be better called a Swiss Army Machete. Lou Bajuk-Yorgan, senior director of product management for Tibco's Spotfire S-Plus, says its software is used by a number of clients who are studying how business or engineering projects might work or why they fail to work. Analyzing weather patterns to find the best places to build wind-powered generators is one example.