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10 tips for getting started with machine learning
Source: Clint Boulton




Machine learning (ML) is fast becoming a litmus test for forward-thinking CIOs. Companies that fail to adopt machine learning for product development or business operations risk falling behind more nimble competitors in the coming decade. That's according to Dan Olley, who as the CTO of Elsevier, the scientific and health information unit of RELX Group, has ratcheted up his organization's adoption of ML technologies in recent years.   

"I fundamentally believe that we are at a tipping point with machine learning and it's going to change the way we interact with the digital world over the next decade," Olley told an audience of his peers last month at the CIO100 Symposium in Colorado Springs, Colo. "We're going to have decisions increasingly made by machines."

It's a reasonable assumption. Growth in computing power, the increasing sophistication of algorithms and training models and a seemingly unlimited source of data have facilitated significant innovations in artificial intelligence (AI). AI, which includes any technology in which a machine can mimic the behavior of the human mind, includes subfields such as ML, in which statistical-based algorithms automate knowledge engineering. Google, Amazon, Baidu and others are pouring more money into AI and ML. Moreover, entrepreneurial activity unleashed by these developments drew three times as much investment in 2016 — between $26 billion and $39 billion — as it did the previous three years, according to McKinsey Global Institute.
The time to adopt AI and ML is now

AI adoption outside of the tech sector is mostly at an early, experimental stage, with few firms deploying it at scale, McKinsey reports. Companies that have not yet adopted AI technology at scale or as a core part of their business are unsure of the returns they can expect on such investments, according to McKinsey. But Olley, whose ML efforts at Elsevier have helped pharmaceutical clients discover drugs and deliver relevant medical information to clinicians, said use cases for ML abound in talent management, sales and marketing, customer support, and other areas.

CIOs had better get up to speed on these emerging technologies if they want to establish a competitive advantage or at least stay ahead of the curve. "It's something that you have to start embarking on now," Olley said.

How do organizations who have never seen AI algorithms embark on data science or ML? Olley and Gartner offer the following tips.
1. Understand where data science fits

You have an idea for leveraging data science and ML at your organization, but how do you go about implementing it? First, you needn’t centralize your data science and ML operations. In fact, it may make sense to embed data science and machine learning into every department, including sales, marketing, HR and finance. Olley suggested CIOs try something that works for him at Elsevier, where he pairs data scientists with software engineers or oncology specialists, who build products in agile squads inspired by the Spotify model.



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