5 trends driving Big Data in 2017

A few years ago, Big Data was a headline-grabbing phrase that could pull in almost any technologist. But now, Big Data is less of an anomaly and simply the way business gets done.

That’s because, when done well, Big Data works and offers businesses concrete return on investment. Using Big Data in sales, marketing, supply chain, manufacturing and R&D can net businesses 20% to 30% gains, according to a recent Boston Consulting Group (BCG) report.

In fact, BCG points out that most of the world’s most valuable companies have shoved aside traditional players by using data-driven models. Six of the 10 most valued companies today are built on data — Apple, Alphabet, Microsoft, Amazon, Facebook and Alibaba — compared with only one in 2006.

But how companies are using data is changing, marking the advancement of tools and the investment from executive leadership of forecasting more parts of the business. To touch on the changing Big Data market, here are five major trends:

Forward-thinking companies are begging to adopt a DevOps-like model to Big Data projects, called DataOps.

“DataOps is an approach to building a self-service data platform for delivering insights-driven business decisions across an organization,” said Ashish Thusoo, CEO and co-founder at Qubole.

By using automation, companies are making their data teams more effective, de-coupling them from daily demands to make room for agility. Ultimately, the streamlining will lead businesses to faster time to insight in an environment where teams can work in a more efficient and effective way.

Sure, all that data is great. But the perpetual challenge for businesses is what do you do with it? How can you turn Big Data into something that improves the business?

“Enterprises across all industries have accepted that their business life lines and advantages lie within the data they can accumulate,” said Alex Lesser, vice president at PSSC Labs. “Making sense of that data and providing the necessary time sensitive information to the relevant stakeholders is now critical.”

But doing so is not easy. As a result, several startups have emerged to help support these initiatives. The common thread between these companies? The need to quickly gather and assess data in as close to real-time as possible.

Startups are emerging because advancements in technology and storage has made their tools possible. “The technologies these companies are using to perform this analysis did not exist two years ago,” said Lesser.

As organizations move forward with their modern data architecture and data lake initiatives, many quickly realize the need for governance.