October 27, 2010
Today InfoWorld’s Peter Wayner reflected on how a number of programming languages that have traditionally been eschewed by enterprises are making their way up the chain and into mainstream business use. Among these languages are MATLAB and R (the open source variant of S), a movement that is due, in part at least, to the rapid rise in vast data sets that require statistical analysis so companies can seek out consumer patterns and look for trends to sell more products, serve better ads, target clients more effectively and so on.
This means that programmers who are skilled with a number of languages, some of which have been unofficially reserved for the academics, stand a better chance of becoming invaluable if they are able to diversify. Furthermore, provisioning of these scientific languages on the cloud means they might become more ubiquitous in the enterprise since the cost of running such applications on someone else’s infrastructure is lower. Also, due to the ever-occurring refinement of user interfaces from scientific applications to the cloud, using, say for example MATLAB or R becomes quite a bit easier.
While MATLAB is traditionally used by those with large-scale mathematical issues to address, its adoption in the enterprise is growing due to the growing volumes of data that requires analysis. As more data is collected from an increasing number of sources, companies are looking to MATLAB to handle some of the complexity.
As CIO magazine points out, one particular use of the statistical techniques matches users of websites with the most relevant advertisements or page suggestions due to complex algorithms like those tackled by MATLAB. After all, as the amount of data logs swells “it’s one thing for a human to look at the list of top pages viewed but it takes a statistical powerhouse to squeeze ideas from a complex set of paths.”
Another language that has made its way from science to the enterprise is R, especially with the rise in need for rapid statistical analysis on large-scale data. Just as with MATLAB, many companies are reliant on looking for customer patterns in the vast logs they manage and R, “another Swiss Army knife of numerical and statistical routines for hacking through the big data sets” is far more powerful than traditional enterprise tools.
Several on-demand resource providers are opening these languages to a wider array of users via simplified interfaces and arrangements with the license holder if not open source. Enterprises are now finding that they have highly complex data analysis tools at their instant disposal to run large-scale analysis projects—and even better, that if used on a cloud or remote resource, the cost could be less for far more of a computational punch.
Full story at CIO Magazine
Frank Ding, engineering analysis & technical computing manager at Simpson Strong-Tie, discussed the advantages of utilizing the cloud for occasional scientific computing, identified the obstacles to doing so, and proposed workarounds to some of those obstacles.
The private industry least likely to adopt public cloud services for data storage are financial institutions. Holding the most sensitive and heavily-regulated of data types, personal financial information, banks and similar institutions are mostly moving towards private cloud services – and doing so at great cost.
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