Leap by Howard Yu

Author:Howard Yu
Language: eng
Format: epub
Publisher: PublicAffairs
Published: 2018-06-11T16:00:00+00:00

STEPPING OUT FROM THE SHADOW OF THE TECH GIANTS

To Kitamura at Recruit, the company was particularly well positioned at the intersection between offline and online activities. With the successive debuts of various digital platforms, the potential to glean insights from its valuable data became all the more apparent. “Say a customer is seated at a restaurant; such an offline action should trigger information online—[be it] pulling the customer profile to understand her food preference, or tallying up kitchen inventory for the waiter to make spontaneous menu recommendations,” said Kitamura. To Asano, the future is to blend offline and online data to the point where the digital divide vanishes. “If we can grasp that moment of this second wave of digitalization, removing the final boundary between online and offline ahead of Google, Facebook, or even IBM, we can dominate the sphere across all SMEs [small and medium-size enterprises] in Japan, and that will be our winning card.”

The lack of in-house data scientists, however, had hampered Recruit’s progress. That’s the same challenge that Syngenta faced, as we observed in the last chapter. And so, Recruit became the first Japanese company to collaborate with Kaggle—the world’s largest community of about 300,000 data experts—to hold a two-and-a-half–month data prediction competition. In 2015, the company invested in DataRobot Inc., a provider of platforms for general machine learning. These platforms used massive parallel processing to train and analyze thousands of models in open-source languages, including Python, Spark, and H2O. The resolute commitment to data science eventually came in late November, when the board announced an artificial intelligence (AI) research laboratory in Silicon Valley, headed by Dr. Alon Halevy, a heavily cited AI researcher cherry-picked from Google. Like many of his contemporaries, Halevy espoused open software to expedite innovation. Under his watch, all AI development relied on open-source components. He also took a page from Recruit’s existing playbook by co-locating his researchers with existing businesses. Data scientists wouldn’t just write code, but embrace unplanned conversations with customers and sales—an approach that might look odd in Silicon Valley, but a long-established business norm at Japan’s headquarters.

“We have a very interesting collection of datasets, but we don’t have the appropriate tools. What you see at Recruit is that they are excellent at providing services, but they have not really focused on technology in a concentrated way,” Halevy said to me. “It was and still is ‘let us make the service better.’ However, there is now this visceral sentiment that we have to leverage data to make a real difference.” In that regard, Asano couldn’t agree more: “We need Recruit to take high-quality data and turn it into value. We are unique in the sense that we can take a holistic view of our customers.” Being thoroughly pedestrian and small business–oriented, the smiling Kitamura raised his voice in excitement, “We can now see exactly the kind of business opportunity for us to invent new services, in the very sector where we choose to compete.”

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