Consulting Research Solutions

Machine learning success stories: An inside look

6 machine learning success stories: An inside look

Fewer technologies are hotter than () and () . Leading organizations are already harnessing the technology, which mimics the behavior of the human mind, to woo customers and bolster business operations.

SwissCognitiveAnd the trend will only gain more traction in the years ahead, as and will be a top five investment priority for more than 30 percent of CIOs by 2020, according to Gartner. Initial fears over and being used to displace jobs appears to be dissipating, with more than 67 percent of business executives surveyed by PwC saying that will help humans and machines work better together . Recognizing the opportunity to move the needle for their businesses, some CIOs are experimenting with, building and even patenting new and technologies.

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Putnam Investments, a provider of mutual funds, 401(k) plans, IRAs and other retirement plans, views and as essential for driving improved coverage of stocks by the financial services firm’s research analysts, CIO Sumedh Mehta tells The analysts work closely with Putnam data scientists to create theses that help glean insights from large amounts of data, Mehta says. Putnam is also working on algorithms that will recommend the most important sales prospects.

“It’s a hugely disruptive and transformational power and the whole business driver for it is efficiency and productivity,” says Mehta of and .

Mehta, who relies on a combination of software engineers, data scientists, analytics and vendors, has created a data science center of excellence, which is essentially ground zero for and efforts that support business stakeholders. He says his “enlightened” business partners have embraced these approaches to achieve better automation.

The and work is part of Putnam’s broader digital transformation, which entails modernizing IT infrastructure with and creating a single platform on which to run the business.

Key advice: Organizations should take their time and set expectations appropriately, understanding that the first few ideas will lead to new questions rather than answers. “There is no such thing as a eureka moment when it comes to ,” Mehta says. “It’s not the case that suddenly your algorithm will yield insight you didn’t already know about.”  […]


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