Nearly 97 percent of data scientists believe big data
technology solutions need improvement and the top three obstacles data scientists
foresee when running analytics on Big Data are: complexity of big data
solutions; difficulty of applying valid statistical models to the data; and
having limited insight into the meaning of the data, according to Revolution
Analytics.
When asked whether they expected their use of various
analytics platforms (SPSS, SAS, R, S+, MATLAB) to decrease, stay the same, or
increase over the next 24 months only open source R registered a majority of ‘increased use’ responses with 47 percent.
Over 86 percent of statisticians polled consider
themselves ‘data scientists’.
As an industry, there is no general agreement on how ‘Big
Data’ should be defined. Survey results show factions of data scientists view
the threshold of big data at a terabyte, a petabyte, or just above what can be
reasonably managed for any given job.
The next generation of R-based technology from
Revolution Analytics is being designed to give data scientists more computing
power, speed and agility when handling big data sets. Big data is changing the
way we analyze and interpret data we’re now in the age of Big Analytics,” said
Jeff Erhardt, COO of Revolution Analytics.
These survey results underscore our commitment to
enabling companies to affordably solve data management and computation
challenges through innovations in statistical analytics. They also reflect the
growing demand for R-enabled analytics, which give companies the right algorithms
and data visualization tools to process larger data sets.
By Telecomlead.com Team
editor@telecomlead.com
