Firms looking to make their analysis of large amounts of data faster and more accurate are increasingly turning to the R statistical programming language to see the best results.

This is according to Johann de Boer, digital analytics manager at Open Universities in Australia. He told a recent Google Analytics User Conference in Sydney that this can eliminate many of the difficulties involved with analyzing large data sets, saving enterprises a significant amount of time.

CIO.com reports that the expert demonstrated at the event how he was able to perform a full analysis, including fetching and cleaning up information, merging it together, processing and visualizing the results, all within a few minutes through the use of R.

“To do that manually, it would have taken maybe a week to be able to fetch all the data and clean it up and so on,” he explained. “With R you can do all of that a lot quicker and because it’s reusable it means you can use parts of the analysis and other analyses you do in future so it doesn’t go to waste.”

Another key advantage is that it can make gathering data from different sources much easier by removing manual processes. For example, if a firm needs to know details such as meteorological information, population statistics or health data to aid in their decision-making there are packages available that mean users do not have to go out to various sources and fetch the right figures thesmselves.

These time-saving features mean R is becoming increasingly popular and because of this, it was recently announced by Kognitio that its solutions will now offer full support for R.

Commenting on the capabilities, it was noted by analyst at 451 Research Matt Aslett: “The introduction of the ability to parallelize R queries gives Kognitio further differentiation and should strengthen its claim to act as a specialist in-memory analytic platform that complements existing data warehousing and Hadoop deployments.”

However, organizations looking to utilize R need to be careful, as Mr de Boer noted it is not a one-size-fits-all solution. Users therefore need to take the time to understand the solution and determine which operations it will be most useful for.