Machine learning is set to go mainstream in the next two years, according to a new survey by software and application development vendor SoftServe Inc.

The new report, based on an April poll of 300 U.S. and U.K.-based medium and large enterprises, shows that 62 percent of firms expect to roll out machine learning-based tolls for business analytics within the next two years. The majority of those companies said the most promising opportunity for machine learning lays in real-time data analysis.

According to SoftServe, the survey is evidence that machine learning is moving past the “hype cycle”, with enterprises looking to automate analytics processes in areas like business intelligence and cyber security. In the latter area, further evidence of machine learning’s progress comes from the Defense Advanced Research Projects Agency (DARPA), which is sponsoring an “all-machine” hackathon at the DEF CON hacking conference in Las Vegas next month. The goal of that event is to show that it’s now possible to automate cyber defenses in the same way that network infrastructure is also being automated.

SoftServe’s survey says the financial sector is the most prominent early adopter of machine learning technologies, with about two thirds of companies in the industry saying that analytics capabilities are necessary in order to stay competitive. In addition, 68 percent of the financial firms surveyed said they expect to introduce machine learning-based tools by 2018. Financial institutions say there’s big pressure to “close the gap between the experiences they provide and what consumers have come to expect”, and many of them believe that Big Data technologies are one of the most effective ways to meet customer demand for faster and more accurate services.

Meanwhile in the IT sector, machine learning technologies are seen as a useful way to reduce operating costs on things like software licensing and commodity hardware. Machine learning tools are also viewed as being able to help break down data silos, whilst simultaneously helping to improve the quality of business intelligence data. SoftServe notes that poor quality data costs businesses up to $14 million a year, but says it’s possible to “overcome this challenge by systematically integrating these silos” and transform bad data into useful information.

“Businesses that take the plunge and implement machine learning techniques realize the benefits early on – it’s big a step forward because it delivers prescriptive insights enabling businesses to not only understand what customers are doing, but why,” said Serge Haziyev, SoftServe’s vice president of technology services, in a statement.

Photo Credit: tpn.team via Compfight cc