Helping Farmers Grow Crops 更多的 Efficiently through Innovation and Sustainability

    Testing more seed variants, producing more seed with less land, and serving customers better while yielding additional company value.


    With roots tracing back more than a century, 拜耳 has always seen innovation as key to its mission of improving farmers’ harvests to balance the needs of humanity with our planet’s limited resources. 拜耳, a world-leading provider of agricultural products, relies on data science at its core, supporting use cases such as maximizing crop yields, improving customer experience, and optimizing supply chain operations. The output of data science is a model. With models built by 拜耳’s 500- plus strong data science community helping to improve more than 100 decisions, the company exemplifies what it means to be model-driven.

    拜耳 has adopted Domino as part of their “science@scale” data science platform to further enhance visibility and collaboration, accelerating the pace of research across hundreds of simultaneous projects and multiple business units. The platform is making a big impact on the business. Coupled with investments in an enterprise-wide data strategy and digital 平台, 拜耳 has realized significant cost savings by reducing cost- of-goods and increasing operational efficiencies.


    拜耳 has a multi-year research pipeline to develop new products, including seeds that maximize crop productivity and provide protection from insect pests and herbicides that are needed to combat yield-robbing weeds in the field. The process is expensive and time-consuming; there is little margin for error.

    “每年, we have one chance in each hemisphere’s growing season to collect data on the seeds we develop,” explained Naveen Singla, Data Science Center of Excellence lead at 拜耳. “We manage an incredible amount of data to help produce high-quality results, but we also know there’s always opportunity to improve how we manage and leverage the data.”

    Domino has made it easier for users across the global enterprise, using different 工具 and with varied backgrounds and skill sets, to work with each other, 利用过去的工作, and collaborate quickly.

    Naveen Singla, Data Science Center of Excellence Lead at 拜耳

    The company applies highly complex models at each stage of the agricultural process, from early breeding to in-field testing, to increase the probability and pace of breakthroughs that will maximize output while conserving environmental resources.

    拜耳 realized early in their data science journey that managing the development, 生产, and ongoing improvements to models requires a different approach than established disciplines surrounding 软件 engineering and data management. 拜耳 started developing an internal, cloud-based data science platform called “science@scale” to ingest data and provide access to widely used data science 工具. While the platform sped up data analysis, the unique characteristics of models required additional collaboration.

    Models are built differently, and they serve a different purpose.

    Unlike 软件 engineering or data management, models (and 拜耳’s business) require a research-based approach comprised of constant exploration, 迭代, 和敏捷性. They’re intended to be probabilistic, not deterministic. The nature of data scientists’ work is experimental and collaborative; models must constantly be tracked, 重新训练, and iterated on to reflect changing data and other factors that lead to model drift.

    拜耳 had the opportunity to augment and amplify its research-based approach for even greater success across its global data science community.

    Models have different ingredients.

    The landscape of data science 工具 and technologies -- i.e. the “ingredients” that go into models -- is very heterogeneous and constantly evolving. A data science platform must provide flexibility, 敏捷性, and scalability to support a dynamic tooling environment and diverse skill sets and preferences. The ability to quickly iterate on retraining models, 验证, and deploying was a “must” for 拜耳.

    The science@scale solution included RStudio, Jupyter, 瓶, etc., catering to data scientists comfortable with modern 软件 programming paradigms. Domino has provided easier access to the big data technology stack to the broader data science community at 拜耳, as well—which has had a positive impact for 拜耳’s diverse research team while also delivering business value.

    “We needed a platform that could abstract away complexities and allow all users to do analysis at scale, utilizing the modern tech stack and getting better insights from data,”Singla说.


    拜耳 leadership recognized an opportunity to enhance science@scale with Domino. Domino is a purpose-built data science platform that supports diverse 工具, automates hardware infrastructure provisioning (so data scientists can run experiments in parallel and at scale), and facilitates rapid 迭代 and deployment of models. The critical features provided by Domino include:

    • Open and flexible ease of use: Domino allows 拜耳’s hundreds of data scientists to focus on driving innovation, using their preferred hardware, 软件, 工具, and languages -- including RStudio, Python, 瓶, and Shiny -- with centralized management. The platform allows team members who are relatively unfamiliar with the big data technology stack to process, 探索, and model data using the latest packages. Data scientists at every level are empowered to control their own environment and hardware.
    • Collaboration: Domino automatically versions not just code, but entire experiments along with the data, 的环境, 讨论线程, 和必要的工件, meaning work is never lost and is always reproducible. “It’s invaluable to be able to compare your current result with one from five experiments ago and see what’s changed,”Singla说. Data scientists across the globe can collaborate and build on past work rather than reinventing the wheel, and data science leadership is confident in the team’s ability to deliver business results efficiently and at scale.
    • Adoption: 拜耳 set up Domino within a robust discovery environment in science@scale, where it facilitates accelerated model development along with model delivery via the Domino API and Shiny apps. 更多的 than 75% of the company’s 500-strong community of data scientists now actively use Domino and adoption continues to expand. 随着团队的成长, expert data scientists create templates in Domino that help ingrain and share best practices for more junior colleagues.
    拜耳’s Data Science Journey: Mission Driven

    拜耳’s large data science community works as a cohesive, high-performing team. They build models that both drive agricultural breakthroughs and optimize efficiencies of everyday business operations.

    Digital innovations across the company, enabled via a combination of investments in data, 平台, 和人民, have led the company to realize value and efficiencies in delivering agricultural products to farmers around the world.

    • Using machine learning via Domino’s platform, 拜耳 can better understand, model and predict the impact that seed genetics, environmental conditions and agronomic practices have on crop yield within its Supply Chain operations. Yield performance depends on the interaction between the crop genotype and environmental factors (such as topography, soil and climate conditions at the location where the crop is planted in the field). Leveraging the platform has resulted in a significant increase in seed 生产 yield simply by planting a product in the best zones within the company’s existing seed 生产 network. This increase can be used either to reduce 生产 acres, or the level of uncertainty within existing 生产 acres.
    • Rather than using static models during online operations, researchers can now adapt models based on updated data flow. 快速迭代, 验证, and delivery using Domino allows them to conduct field operations more efficiently.
    • Domino allows 拜耳’s sales teams to access more detailed information about customers’ specific needs, in order to recommend the best products for their fields. This personalized approach improves customer success and satisfaction.
    • The platform automatically tracks the full testing record for R&D项目, and deploys it as APIs for consumption by downstream systems, allowing new members who join the team to contribute immediately.

    “Domino has made it easier for users across the global enterprise, using different 工具 and with varied backgrounds and skill sets, to work with each other, 利用过去的工作, and collaborate quickly. This ultimately results in more models being delivered and deployed in a shorter window of time, which is empowering 拜耳 to be a model-driven company that’s at the forefront of farming,”Singla说.

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