Big data isn't going to go mainstream, it's already there

Jodie Ng

Editorial Assistant

To IDC Directions, big data has already gone mainstream.

Big data is undeniably becoming more wide-spread as it continues to grow. Part of the reason for that is because many things are fueling the columns and rows that make up big data, but what’s interesting is that big data, in return, is driving cognitive computing growth.

Carl Olofson, IDC research vice president for databases and data tools, reiterated at the IDC Directions 2016 conference that NoSQL technology is just as influential in big data trends. TechTarget reporter Jack Vaughan writes “they are not replacing existing relational apps. Instead they are ushering in new apps with a new class of functionality – one very much aligned with emerging Web and mobile operations.”

A post last week touched upon big data and what that really means. It briefly explained that companies are collecting and analyzing an overwhelming amount of data on a massive scale, but what it did not discuss is storage. Organizations are hoarding and mining all this data, but where does it go? Where can it go?

Paul Turner, chief marketing officer at Cloudian, talks about the need for IT to implement cloud capacity storage layer as the long-standing industry standard, RAID storage, is no longer suffices. He encourages professionals to ask these two questions. First, how do they plan to consume storage and second, is there a need to move to a cloud-based, capacity-oriented object storage model.

Storage isn’t the only thing riding the big data wave. In recent years, machine learning methods are hopping onto the big data train as well. In fact, according to a podcast by TechTarget reporters Jack Vaughan and Ed Burns, growing evidence suggest many professionals confide in machine learning when big data is accumulated. Use cases cited are risk estimation in insurance, credit scoring and digital ad placement.

It’s time we all board the big data train and understand how its growing, its storage needs and how methods like machine learning can benefit from big data.