For years, big data has been one of the hottest buzzwords across all industries.
Big data is the term used to describe the process of analyzing complex set of data sets to discover information that could help make better decisions or find certain patterns that were previously unknown.
For example, Amazon is able to recommend products based on your previous buying patterns. Singapore healthcare providers are able to dig in to patient records to come up with more individualized treatment plans.
It's one of those things big companies and startups constantly talk about when people ask what "the next big thing" in tech will be.
But despite its hype, big data is still considered a relatively obscure concept, failing to reach wider roll-out in companies outside tech and highly data-driven sectors.
Gartner, for example, says big data still has a long way to go. It put big data at the "Peak of Inflated Expectations" in its Cycle for Emerging Technologies Map last year. This year it slightly improved, moving to the tip of the "Trough of Disillusionment" category, which means it's slightly getting better in terms of wide scale usage.
Still, it's mostly just hype.
Gartner Gartner 2014 Hype Cycle for Emerging Technologies Maps
Big data solutions provider Talend also revealed in a survey last year that only 10% of respondents were engaged in a large scale big data implementation, despite seeing nearly 40% growth in interest in big data within their organization.
"There is still a significant gap between those businesses expressing an interest and those taking the plunge and actually implementing the (big data) approach," said Yves de Montcheuil, VP of Marketing at Talend.
Part of the reason for this imbalance in realizing the need for big data and actually implementing it can be found in corporate culture, says Santhosh Nair, VP of Strategy and Business Development at Wind River, an Intel-subsidiary focused on information appliance software.
"Organizations are very slow to change, especially when you move to regulated industries, like defense, medical, aerospace, and energy," Nair said at GE's Data Forecast event held on Tuesday.
"It's this organizational inertia of not wanting to make a mistake. The cost of a mistake or a learning opportunity is very high and nobody wants to be the first to do that," he added.
"This data-driven, decision-making culture is not very common in 'traditional' organizations, where decisions are made in a certain way - and it's not always with data as an input."
Another reason is because a lot of companies are just too focused on short-term growth. Nair said big companies in general are faced with the short term pressure of doing something for this year or this quarter, that sometimes, "impedes what you need to do for long-term sustainability."
"You need to be in for the long-term. There's no quick, big win here. You need to invest to grow your business and revenue streams, and that's the challenge," he said.
But there are positive signs in a lot of these "traditional" industries, he says, as even a lot of the energy companies are seriously looking into the benefits of data science. "There's a lot of good dialogue we're entertaining," he said.
He added the best way to speed up more big data implementation is to "just learn from the best use cases." He recommended creating a platform about best practices and sharing it across industries to raise awareness of big data science.
"Big data is right at the 'trough of disillusionment,'" Nair said. "People are becoming more practical with big data. The hype is over and now is the implementation of the hype."