Advanced Computing in the Age of AI|Wednesday, November 25, 2020
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Enterprises Embrace Machine Learning 

Machine learning technology is poised to move from niche data analytics applications to mainstream enterprise big data campaigns over the next two years, a recent vendor survey suggests.

SoftServe, a software and application development specialist based in Austin, Texas, reports that 62 percent of the medium and large organizations it polled in April said they expect to roll out machine learning tools for business analytics by 2018. That majority said real-time data analysis was the most promising big data opportunity.

The survey authors argue that artificial intelligence-based technologies like machine learning are moving beyond the "hype cycle" as enterprise look to automate analytics capabilities ranging from business intelligence to security. (In the latter case, the Defense Advanced Research Projects Agency is sponsoring an "all-machine hacking tournament" in conjunction with next month's DEF CON hacking convention in Las Vegas. The goal is to demonstrate that that cyber defenses can be automated as more infrastructure is networked via an Internet of Things.)

The survey found that the financial services sector is among the early adopters of big data analytics and emerging approaches such as machine learning. About two-thirds of financial services companies said analytics was a "necessity" to stay competitive while 68 percent said they expect to implement machine-learning tools within the next two years.

Among the incentives for early adoption is growing pressure on financial institutions "to close the gap between the experiences they provide and what consumers have come to expect," the survey authors noted. Big data is increasingly seen as a way to increase client demand for a faster and more accurate service, the added.

For the IT sector, big data is widely viewed as a way to reduce operating costs such as software licensing and commodity hardware savings.

Meanwhile, tools like machine learning also are perceived as helping to break down data siloes while improving the quality of business intelligence data used in decision-making. The survey cited estimates that poor quality data can cost businesses as much as $14 million a year. "A big data transformation is able to overcome this challenge by systematically integrating these silos – and turning bad data into good information," the survey asserts.

"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," Serge Haziyev, SoftServe's vice president of technology services, noted in a statement.

The survey of 300 executives in the U.K. and U.S. also found that the retail sector is most concerned about data governance issues.

About the author: George Leopold

George Leopold has written about science and technology for more than 30 years, focusing on electronics and aerospace technology. He previously served as executive editor of Electronic Engineering Times. Leopold is the author of "Calculated Risk: The Supersonic Life and Times of Gus Grissom" (Purdue University Press, 2016).

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