Data—trillions and trillions of bytes of it—has been called the new fuel of agronomics.
And for 20 years Greg Finch has been in the middle of it all, doing what he enjoys, building platforms and creating new architectures that help turn the ones and zeroes into meaningful, searchable, stackable answers. His work bridges the gap between the company's most significant digital product development and connection points to our customers—JDLink™ and Operations Center.
That's why as Finch thinks about John Deere's Smart Industrial Operating Model, he can't help but smile.
"You know, we are sitting right in the center of Smart Industrial," Finch said. "It's the idea of bringing intelligence to our machines and working with these massive amounts of data that we collect, deriving intelligence and making our product smarter and more efficient, and better for the customer. I love that."
It doesn't take long for Finch to present his case.
"Back in 2008, when we had fewer sensors on our equipment and not enough computing power and storage space to handle that data, we were getting information back to our customers only once per day and in 30-minute aggregates," he said.
More than a decade ago those bytes were handled through satellites or slow and unreliable cell connections before they found their way back to Deere. When cellular connectivity improved and cloud-based computing took hold, the world sped up.
"As we added more sensors to our equipment and increased data collection rates the information just poured in. During peak times like planting and harvest, we process tens of millions of sensor readings every second," he said.
In the world of agronomic datasets no company gathers more information on how its products are performing than John Deere. In total for 2020 more than 72.8 trillion unique sensor values were captured. Finch said it all moves much more quickly now.
What’s exciting to me is we can now make these machines start to wake up to the rest of the world around them and become even smarter through that.
"That information is captured every 200 milliseconds," Finch said. "Every second we are getting and making sense of data from millions of acres all around the world. It takes a couple of minutes to make it useful to our customers in the Ops Center. The goal we keep working toward is having that be more real-time. That's why the data platform and our overall cloud computing infrastructure is so important."
Today, Finch is working with the data engineering team at Deere's Intelligent Solutions Group (ISG-Urbandale) in Iowa, building applications that add value to all the data that is streaming in.
Finch has been recognized for having the "courageous and creative technical, operational and cultural thought leadership" necessary to bring cloud adoption to Deere. But does having all that information resonate outside of Deere?
"We just hired three new people on my team recently, and all three of them came because of the mission," he said. "And they"re super excited about what we're doing. The vision, the strategy of the company now is Smart Industrial, that we are going to be using all of this information that we've collected over the years, and more to come, to make our machines smarter, more productive, more capable and our customers more successful.";
Finch then looks beyond.
"We've spent the last 20 years getting to this point of our machines being more self-aware. We know what's going on inside the machine really well. But the machine doesn't necessarily know a whole lot about what's going on in the environment around it," he said.
It's here where he pauses, letting his mind sort out what's next and what's possible based on the data.
"See & Spray™ is a good example of what is possible as a machine begins to look outside of itself—as it starts to perceive the environment that it is working in," Finch said. "Imagine all of the other things we can do as the machines become smarter about their surroundings, not just the weeds, but the plants they're tending to or the soils or the weather or the fleet they are working with. What's exciting to me is we can now make these machines start to wake up to the rest of the world around them and become even smarter through that. That's where I think the big opportunity is."
"That's where I think the big opportunity is."