How auto giants are using big data: A conversation with Ford

How auto giants are using big data: A conversation with Ford

How auto giants are using big data: A conversation with Ford

Autonomous driving research and the race to develop driverless vehicles has, at its core, a key: Big data. These technological advances, which are dependent on the machine learning branch of AI, rely on the data collected by car companies—data from real miles driven, such as with Tesla's Autopilot, data from simulations of autonomous driving, and data from test situations, such as Uber's driverless fleet in Pittsburgh.

Big data, said Michael Cavaretta, director of analytics infrastructure at Ford Motor Company, means data that is "too big to easily handle within your computational resources." It's about looking at datasets with "high velocity, high volume and high variety," he said.

And as computers have become more powerful and storage is cheaper, grappling with this data has become more difficult.

But this data is essential to machine learning—which operates through inputting data and learning via feedback loops. "Data and machine learning go together like peanut butter and jelly," said Cavaretta. "So much better with each other."

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Cavaretta previously led an analytics group within product development, specifically in research and advanced engineering, supporting different functions within Ford. There were several of these groups, such as an analytics group in manufacturing, one in marketing and sales, and so on. "We would do our best to be internal consultants," he said, "and work with our internal customers to deliver the best value."

There has been a sea change recently, he said, at Ford. When the company brought on a new chief data and analytics officer, Paul Ballew, the aim was to centralize Ford's analytics groups. The new data operations organization has a singular focus on understanding Ford's internal data, third-party data, potential partnerships, and vehicle data, said Cavaretta. It's about "having an enterprise view and an enterprise strategy, with regard to Ford's data, and then the analytics to put on top of it," he said.

The group realized there was an opportunity to be part of a bigger picture. "We thought, 'It would be great if we had more communication and looked beyond the immediate needs within a particular silo,'" said Cavaretta. The group presented the executive board with a proposal for a new role—and that's when Ballew was hired, and the Global Data Insight and Analytics group was formed, with a machine learning division.

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When Ballew came in, he saw the importance of having an enterprise view of both the data and analytics side, said Cavaretta. So Ford has focused on making sure the right roles are filled: "For data engineers, data scientists, and people who can understand both sides," said Cavaretta. "Now, we're here.

 



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