Your aging parents, your children’s pet, and the cornfield that you see along the highway. What do all of these have in common? Chances are good that each has been a patient requiring a “medicine” formulated by biotechnology. Whether that medicine is for your father’s high blood pressure, your family dog’s vaccination or to enable a plant to grow stronger, each “patient” benefits from sophisticated biotechnology, made possible by translating data into actionable outcomes. Agriculture has a rich history as perhaps the oldest industry of all, yet there is nothing antique about how effective use of data is essential to product success in plant biotechnology. Being able to collaborate both internally and externally in the context of pursuing a proactive data sciences strategy will determine who will ultimately be the leader.
Let’s take a step back and look at the larger picture. As water and land become scarce, the world’s population is predicted to grow from seven to nine billion people by 2050. This includes a rising middle class who wants protein–made possible by animals consuming corn and soybeans. Data sciences will play a key role in enabling plant biotechnology companies to help feed the growing world. Dow AgroSciences employees feel enormous responsibility as one of a handful of companies that can significantly address this challenge with plant technology.
Today’s agriculture industry is propelled by high-throughput, rapidly evolving biological data generation platforms such as genomics, proteomics, metabolomics, and phenomics, as well as sensors and imaging platforms. A tremendous amount of data on crops, weeds, insects, soils, topography and weather is generated daily around the world. These data are fed into analytical platforms to provide the best “prescription” possible for farmers to use in improving crop productivity. Effectively storing, integrating and analyzing these heterogeneous and often unstructured data sources are a challenge and a source of potential competitive advantage for those who do it well. Companies providing technology in this environment must utilize best practices in a collaborative manner to support ultimate farmer success.
"The reality is that there is very little lead time to know what is next in emerging data generation technologies"
In the field of plant biotechnology, success relies heavily on rapid development of seed varieties, including new discoveries of agricultural chemicals and genetic traits that help the plants protect themselves. For example, at Dow AgroSciences we use predictive modeling as the engine behind our research to speed discovery and to minimize time-to-market for our discoveries as well as maximize genetic gain and reduce cycle time in plant breeding. A well designed and collaborative information management approach can make or break a modern agriculture biotechnology firm.
We work with some unusual variables. The plant biotechnology pipeline involves a lot of relatively low-precision field testing situations with major yearly variability on environmental conditions. Rightsizing each step of the process to the crop, region, trait and business situation is a massive challenge that can be tackled by data sciences. In our industry, ineffective use of expensive field testing resources can hinder progress. This complexity highlights the importance of using data sciences in a collaborative manner. Relying on “best guesses” doesn’t cut it in today’s modern agriculture. Improving outcomes using data is also important when considering the bigger picture as we develop sustainable agricultural solutions that leverage the power of science to balance the needs of boosting agricultural productivity while preserving the environment.
Solid strategy to effectively use data is at the heart of driving business decisions and is integral to all long-term business strategic planning and a high performing R&D organization. Key decisions can’t be made in a silo as they need to be supported by data that effectively flows across the organization, enabling holistic decisions instead of point decisions. This implies a need to change how one thinks about data across all functions within a company as well as across the whole value chain. We cannot afford to have our data architecture, management or analytic strategies to be considered an afterthought. Tremendous cultural change is occurring in plant biotechnology companies to focus on data sciences and to ensure each function understands that all data can and will be used for future decisions, beyond the immediate needs for which they were created.
The reality is that there is very little lead time to know what is next in emerging data generation technologies. The best strategy is to seek to influence the next round. Successful companies lead from the front whenever possible, as well as look for unique differentiators even within commoditized technologies. For example, our company works to respond quickly to new instrumentation technology with IT and data analysis software that scales with the increased throughput of these technologies, and is flexible enough to fit with the existing “home grown” systems.
Modern enterprise architecture frameworks are another part of our strategy as we develop supportable, connectable and extensible systems. We seek to avoid outdated database systems and processes that are bureaucratic, rigid, slow or overspecialized to solve a certain relevant problem, but not designed to easily integrate with other systems. We continuously aim to identify and foster foundational technologies while implementing project specific capabilities to meet localized needs.
Of course security must be top of mind in this data-rich environment. Proactively and carefully adapting IT security requirements is essential. Security systems must adapt to the needs of data integration as well as with our ability to collaborate externally. We are continuously working towards having IT security systems that protect intellectual property while allowing for collaboration and innovation.
The plant biotechnology industry needs to continue leveraging the advances in data sciences to streamline access to knowledge, collaborate, and create value for society faster. After all, our “patients” are counting on us!