System News
IT - Big Data
28 Nov 2016
10 Big Data Predictions for 2017 and Beyond (Slideshow)
Datamation, November 28th, 2016

"Big data analytics is no longer a new technology. Today, most businesses recognize that they need to be actively mining their data stores for insights if they want to remain competitive in the constantly evolving marketplace. They've seen the benefit of big data solutions - and now they want more.

For 2017, the primary trends in big data will revolve around refining enterprises' core big data capabilities. They are looking for ways to analyze more data, more quickly. Having seen the payoff from their initial investments in big data technology, they are looking to expand their big data projects to achieve even greater financial results..."
(Get More Information . .)

1 Dec 2016
Big Data Poised to Get Much Bigger in 2017
GIGAOM, December 1st, 2016

"Big Data is only going to get much bigger, so big in fact that companies attempting to deal with massive and more complex data sets are going to find it much harder to manage and derive value out of those massive lakes of data. At least that's what Neeraj Sabharwal, Head of Cloud and Big Data at Xavient Information Systems is telling us.

GigaOM had the opportunity to discuss the future of Big Data analytics with Sabharwal, who shared some interesting insights and statistics on how Big Data is poised to change in 2017. Sabharwal is no stranger to Big Data and has a long and storied experience with the technology..."
(Get More Information . .)

30 Nov 2016
New Method to Improve Predictions
Science Daily, November 30th, 2016

"Researchers at Princeton, Columbia and Harvard have created a new method to analyze big data that better predicts outcomes in health care, politics and other fields.

The study appears this week in the journal Proceedings of the National Academy of Sciences.

In previous studies, the researchers showed that significant variables might not be predictive and that good predictors might not appear statistically significant. This posed an important question: how can we find highly predictive variables if not through a guideline of statistical significance? Common approaches to prediction include using a significance-based criterion for evaluating variables to use in models and evaluating variables and models simultaneously for prediction using cross-validation or independent test data..."
(Get More Information . .)

See all archived IT - Big Data articles See all articles from this issue