10 tips for getting started with machine learning

10 tips for getting started with machine learningMachine 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 US$26 billion and US$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.

“We've built our data science teams into our product management teams and business units but we bring them together as a chapter and have one person lead that,” Olley said. “We do put the data scientists as close to the problem as we possibly can because we think that's the way to scale across the organization better.”

2. Get started

You needn't have a five-point plan for building a data science enterprise nor a framework to construct a polished ML product. Gartner says you should foster small experiments in different business areas with particular AI technologies for learning purposes, not ROI. "If you haven't yet I thoroughly recommend that you get started," Olley said. "Your competitors are."

3. Treat your data as if it's money

With data serving as the fuel for any AI/ML efforts, CIOs must treat their data like it's money by managing it, protecting it and obsessing over it. "Your CFO wouldn't just let the accounts be spread all over the company," Olley said. "Nor would he or she say, 'I think we've got about this much in revenue this year.'"

4. Stop looking for purple squirrels

Data scientists tend to be people who have high aptitude in math and statistics and are skilled at finding insights in data, not necessarily software engineers that can write algorithms and craft products. This is easier said than done as companies often seek unicorn-like candidates who are master statisticians, ninja software engineers and masters in an industry domain, such as health care or financial services, Olley said. "I heard one person describe it as, 'I want a software engineer with a Ph.D. in mathematics who is also a trained clinician and if they have a specialty in oncology that would be really useful,'" Olley said, wryly adding that he knows "those three people."