Big Data, Business Intelligence And The Programming Language “R”

Big Data, Business Intelligence And The Programming Language “R”

Why Your Next Business Intelligence Hire Will Use R

As anyone working in the field knows, business intelligence is an evolving landscape. New technologies, combined with a rapidly expanding marketplace, are disrupting legacy solutions and established platforms.

Despite such growth, however, there have been a few constants, such as the R programming language. As other technologies gain popularity or fade away, R has steadily grown into a standard for data exploration. This will only continue with the latest wave of medium and small business BI adoption.

R’s longevity stems from its perks, many of which come from its history.

R was created to take advantage of superior parallel processing abilities in newer systems. Before this, SAS was the default data language. SAS was created before parallel processing was commonplace, however, so even though it now supports it, the functionality isn’t ingrained in the language. Because of this, SAS analysis functions fail to take full advantage of parallel processing power in the way that R does.

Pleasing Graphics

business

One of the most immediate benefits of R’s parallel processing functionality is the ability to create aesthetically pleasing graphics. With only a small amount of extra code and preparation, R allows you to create navigable graphics from data analysis results. The graphics are presentable enough to export and use in a report, or to quickly gain new insight into a data set.

Along with such tangible benefits, R is also receiving some much-deserved recognition. Companies that previously fought against the language, such as IBM and SAS, have since acknowledged the value of R and started implementing support for it in their software.

If such news comes as a surprise and you’re now considering the implications of R usage among major BI vendors, it should be noted that this doesn’t undermine the efforts of companies such as Revolution Analytics. Rather, such adoption should bolster providers of standardized R packages and modules in what is otherwise an unstructured open source environment.

That said, R adoption can lead to some issues. There’s a steep learning curve with R, which means retraining a SAS expert could be difficult. Companies looking to take advantage of the new R integration from IBM, SAS, or Revolution Analytics’ toolset might instead look for applicants already familiar with the language.

Fortunately, there are plenty of statisticians trained in R use, and they come in the form of recent graduates. Being open source, R is ideal for educational use in colleges and scientific research. Many undergraduates and post-graduates leave college with sufficient R experience to join your company and begin BI work. This is made even more convenient by the fact that many R capabilities are open source (aka free) and thus can be downloaded and installed in a day.

With adoption of R by a wider audience (including 6 of the largest enterprise software vendors), it’s time for companies to look harder at what the language can do for them and their own evolving business intelligence efforts.

By Keith Cawley

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