Oracle has made an interesting move in leveraging the open source language R to fill a gap in its portfolio when competing against the likes of IBM (which acquired SPSS) and ‘best of breed’ players like SAS.
Similar to SAS’s data manipulation extensions for common databases (pushing down the manipulation, filtering etc. to the database) though more like Revolution Analytic’s integration with Netezza (now part of IBM), Oracle has converted base R scripts for execution within the database. Though this is really a first release (e.g., lacking fine grained workload management), it is great move in burnishing its credentials in a space gaining prominence in organizations.
Some thoughts to ponder:
Use of R eases adoption by the upcoming generation of statisticians, actuaries (generally referred to as ‘data scientists’). This is a great advantage in terms of dislodging incumbents such as SAS and SPSS and increasing its portion of software license fees. In this model, greater use of R based analytics in operations results in larger number of processors (technically cores) that are licensed from Oracle. While, it allows organizations to use open source R for desktop based modeling and development.
This addresses the biggest shortcomings of R to date, which has been lack of scalability. Though there are commercial implementations such as those from Revolution Analytics, the ubiquitous nature of Oracle in enterprise computing environments makes it so much easier to scale ideations from the innovation lab into commercial reality.
Though there is some integration with Oracle’s reporting suites, it is a bit of a kludge. Longer term, it will not be surprising to see CRAN suite of visualizations natively implemented in OBIEE and leverage by default when accessing R script output. Tighter integration will aid in visualization development which in today’s world mimics business presentation prep in the early 1990s - anyone remember Harvard Graphics on DOS?
Commercial support for R should aid in its adoption and usage. The latest Redmonk language survey indicates it is the sixth fastest growing language (currently noted as in Tier 2 cluster along with MATLAB and Scala). It will be interesting to see if growth accelerates sufficiently for it to lead the pack when it comes to high level languages for analysis and general computation.
Next on my wish list is an IDE that can aid R scripting in a way that IntelliJ aids Java and Scala (RStudio is just very first gen, IMHO). Thoughts?