What do corn portfolios and football lineups have in common? Product development is a lot like assembling a winning football team. We’ve got lots of prospects in the pipeline, but deciding on the best ones to drive genetic gains requires a balance of data, advanced analytics and expertise.
Every NFL team employs an analytics pro who dissects statistics to build a promising squad for the coming season and, in much the same way, we analyze reams of data in our quest to deliver products every season that meet the needs of growers and our licensees.
Fortunately, there’s a wealth of information at our finger tips today. In addition to game day stats, coaching staffs can now track player performance with GPS monitors embedded in shoulder pads. Those metrics are used to better structure team practices, help prevent injuries and manage the team’s resiliency over the long playing season.
Similarly, plant breeders today can access trial results plus a range of diverse data —from soil types and weather patterns to cultural practices and genotypic information. This information helps us evaluate hybrids coming through the pipeline and make better decisions throughout the breeding process – from hybrid creation, trial placement, quality control and product advancement.
Being able to gather such a large quantity of data is great, but it doesn’t mean much if it has flaws. For instance, we’ve seen how crucial the placement of field trials can be. The location of these research sites have to be representative of the environment our target markets experience, so we can ensure our data is relevant. Obviously it’s pretty gratifying to sample fields that are highly productive or uniform, but we know not all farmers have that type of soil. Ensuring we have quality statistics is paramount to fueling the analytics process.
Once we have data and we’re confident that it’s accurate, what’s the best way to dissect it? Collaboratively, of course. Just as a head coach or general manager relies on the expertise of scouts, trainers, assistants and analysts to help him or her assemble a great team and analyze what cannot be measured, we can’t make good product decisions without a variety of perspectives – the combination of data and expertise result in improved performance.
When it’s time to review trial results every fall, our team strongly resembles that of an NFL “war room” during draft week. We rely on people with different roles, like our product scientists, who bring reams of data and expert observations, plus seed production researchers, who ensure our products will be profitable to licensees. Altogether they ensure decisions are grounded in science. The key account leads, serve as scouts for the organization. They have great relationships with their customers and understand their needs, plus they have an excellent understanding of the players—the experimental hybrids—and how they can fit into a licensee’s portfolio. Like a seasoned sports scout, they have a diversity of information and know how to apply it.
The team knows success can be measured in multiple ways – not just yield, but other factors such as standability, plant height, root strength, or pest resistance all play an important role in defining success. These days, we’re able to rely on machine learning and predictive analytics that allow us not to focus on what a hybrid did last season, but to predict how it will perform tomorrow.
In our field, you can collect a lot of great data and develop metrics to refine the decision making process, but ultimately the right team of people have to finalize those decisions to deliver the best possible product for our customers. Over time, the availability of data and skill sets of our “players” have increased, and that allows us to develop better solutions for agriculture. My data suggests that will only continue to improve.