I see there are no questions yet for the author of the week. I would like to start then.
As a Sr. Business Analyst, part-time SQL Server DBA and a Software Engineer with over 25 years experience under my belt - I would like to know what is so compelling about another programming language, one that is specific to statistical analysis? Why should I care from a business analyst perspective? Or what would this language do for me that other tools "out there" won't do?
I see "R" used by a lot of data center administrators to analyze usage data. A lot of theoreticians, researchers and statisticians also use it. Every time some theoretician comes out with a new algorithm or a new way of relating or describing data, an "R" function or package follows soon after. As a DBA you can use it to analyze the performance data provided by the database, and the systems they are running on.
I was a SAS programmer for 25 years before I ever looked at R. I started using it because it made certain tasks easy. In particular, it made understanding data that resided in external databases a simple task. R is very good at accessing all manner of DBMS.
The problem with massive amounts of data is how to understand it and discern patterns that may be important.
R is frequently used to summarize data, create predictive models, uncover unusual observations (e.g., fraud, data entry errors, serendipitous findings), and create pictures that communicate complex information simply. It is particularly effective at creating graphs that describe change over time.
One of the hot terms in business systems these days is predictive analytics. I would argue that R is the bleeding edge platform for predictive analytics.
Having said that, there are things that I would not use R for, including managing large databases (in the gigabyte to terabyte range) and massaging large amounts of text data. Even though it can do it, there are much better tools (e.g., Dedicated DBMS solutions, SQL, Perl, Python) for such tasks.