CIOs wish for simpler ways to wrangle data and experiment with business models – but change remains hard to scale. Also, it may be time to stop chasing “alignment.”
Big data and IoT promise big changes to agriculture
The State University of New York (SUNY) College of Agriculture and Technology in upstate Cobleskill, NY, is home to some 2,500 students, a USDA-certified meat processing facility, the largest cold-water hatchery in the Northeast and a recently acquired 150-acre farm. And soon, it will be the breeding ground for an effort that promises to bring big data analytics to the agriculture industry.
SUNY Cobleskill is building what it calls the Broadband Rural Agriculture Cloud, or BRAG Cloud for short. The goal is to create a common directory or clearing house for agriculture and farm food information, something that does not exist today, says James Dutcher, CIO for SUNY Cobleskill.
Such a clearinghouse will help drive the concept of precision agriculture, where farmers and food producers try to highly optimize their use of resources – be it fertilizer or water – applying only what is necessary for best results. No more, no less.
Individual farms are already practicing precision agriculture and in the process, each is creating “their big blobs of big data,” Dutcher says. “If you take a closer look at the enormity of the data sets they’re creating, it’s big data on top of big data on top of big data.”
As an example, consider the various sources of data farmers have at their disposal. Driverless tractors (which, by the way, John Deere has been selling for years, putting it way ahead of Google) use geographic information systems (GIS) to collect reams of crop field analysis data with every pass. Add in data from drones flying over farms to perform analysis and even the genetics behind the seeds in any given crop and you’ve got more big data sets. Farm animals, whether for dairy or meat, also routinely wear sensors that generate enormous datasets. And then there are various external data sources, from the USDA, local state data and so on.
Add it all up and it amounts to what Dutcher calls “agriculture being at the exponential intersection of IT, with big data and the Internet of Things predicting and building the future.” The BRAG Cloud is intended to help those in the agriculture industry make sense of all that data and put it to good use.
It will consist of two main parts. First is a wireless broadband mesh network that will connect the SUNY Cobleskill campus with the 150-acre farm it recently acquired, which will function as an experimental test bed. Due to launch in the fall, the network will support speeds exceeding 1 Gbps over multiple miles, enabling the movement of large data sets from the farm back to campus for storage and analysis.
Secondly, SUNY Cobleskill will partner with the Cornell University’s Cornell Cooperative Extension to foster the skills needed to perform the kind of big data analysis that will drive success in precision agriculture and other disciplines.
“It requires a highly-skilled workforce to do this sort of work,” Dutcher says. “We’ll be building the physical infrastructure to capture all that data and will be graduating students to work with agricultural farm food producers in collecting data, analyzing it, and reporting back on how to take advantage of that intersection of Ag and IT.”
Plans also call for partnerships with the likes of NYSERNet, which provides next-generation Internet services to New York State’s education and research community, and Amazon, “to help us flex our storage and infrastructure as needed,” Dutcher says.
Ultimately, the plan is to make BRAG Cloud available to any research organization in the state and, eventually, nationally. If a university is conducting research on how to best grow a particular type of apple, for example, it could use BRAG Cloud to see what information already exists on the topic, to help identify historical trends or validate their research and add that research to the collection. Or perhaps a grower having an issue with a particular type of apple can input data and use the database to find whether anyone else has experience with the issue and get some advice on how to handle the problem.
“That would help the orchard optimize its production and make more money in the end,” Dutcher says.
While the business model hasn’t yet been fully fleshed out, he expects growers will be willing to pay for access to that kind of data, perhaps on a subscription basis. “Then the money will be used to sustain and grow the business and portions will go back to funding research, and to the university,” he says.
The challenge is, unlike many Internet startups, it may take quite some time for the project to yield useful information. “It happens over many life cycles of growing seasons,” Dutcher says. “We're talking timelines of five, ten years if not longer for these efforts to become fruitful, excuse the pun.”
He says it brings to mind the Chinese proverb: The best time to plant a tree was 20 years ago. The second best time is now.