Improving Analytics Capabilities Through Crowdsourcing

Syngenta developed an award-winning suite of analytics tools by tapping into expertise outside the organization — including talent available through open-innovation platforms.

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How does a company operating outside the major technology talent centers gain access to the most innovative data scientists that money can buy? Assuming you can’t recruit the right data analysts to join your team full time, how do you tap into contractors with the knowledge and creativity you need outside your technical core? In a nutshell, this was the predicament Syngenta AG faced in 2008.

Syngenta, an agrochemical and seed company based in Basel, Switzerland, was formed in 2000 by the merger of the agribusiness units of Novartis and AstraZeneca. Among its more than 28,000 employees are more than 5,000 highly trained experts in biology, genetics, and organic chemistry, many of whom hold doctorates in their field. As a company, Syngenta’s mission is to develop innovative crop solutions that enable farmers to grow basic food staples such as soybeans, corn, and wheat to feed the world’s growing population as efficiently as possible. That means pushing the envelope on genetics.

For centuries, plant breeding has been a labor-intensive process that depended largely on trial and error. Farmers tested different seeds and cultivation techniques in an effort to find plants with the best yields and most desirable characteristics. Luck played a decisive role, as breeders relied heavily on intuition and guesswork to decide which varieties to cross-pollinate. To find the most successful variety of corn, for example, a breeder might have pollinated hundreds or even thousands of plants by hand to see what happened.

Syngenta had been involved in a large-scale version of trial-and-error research and development (R&D), conducting field tests on hundreds of thousands of plants each year in more than 150 locations around the world. But given that the results of experiments are often shaped by quirks and idiosyncrasies, it was sometimes difficult to draw meaningful conclusions. Did one plant grow taller than other plants because of a genetic trait, or was it because it received more water and more sunlight? With traditional research methods, the only way to find out was to invest significant amounts of time and money conducting large numbers of additional tests, which becomes an expensive proposition. Indeed, it takes seven years, on average, to move a new plant variety from the early testing stage to a full commercial product.



1. Although Alpheus Bingham, one of the authors of this article, does not work for Syngenta, he served as an early-stage advisor to Syngenta’s project, so the authors opted to use the first person plural when describing Syngenta’s experience.

2. More details about Syngenta’s use of analytics can be found in J. Byrum, C. Davis, G. Doonan, T. Doubler, D. Foster, B. Luzzi, R. Mowers, C. Zinselmeier, J. Kloeber, D. Culhane, and S. Mack, “Advanced Analytics for Agricultural Product Development,” Interfaces 46, no. 1 (January-February 2016): 5-17.

3. For more information about the Edelman Award, see

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