Data Lake / Data Warehouse Modernization · Real-time Analytics
Syngenta needed a better way to support AI-ready R&D data products for crop protection research because research data across dozens of countries and systems took months to access, which slowed scientific collaboration and product development.
Reported impact
Syngenta improved AI-ready R&D data products for crop protection research with measurable results including 200 protecting crops today to feed billions tomorrow, 70% data products with more published every week, and 50% faster time to value, while giving teams a faster path from governed data to production decisions.
Syngenta uses Databricks Data + AI Platform to support AI-ready R&D data products for crop protection research. The implementation replaced a legacy state where research data across dozens of countries and systems took months to access, which slowed scientific collaboration and product development. Molecular research data and drone and satellite imagery move through production data pipelines and land in Delta Lake, giving teams one trusted data foundation. Unity Catalog governs access, lineage, and compliance across sensitive datasets. Databricks SQL, MLflow, and Genie operationalize models, agents, and analytics that deliver R&D data products and scientist self-service insights.
Molecular research data and drone and satellite imagery land on a shared platform, pipeline services clean and standardize them, governance controls enforce trusted access, and AI, model-serving, and analytics services deliver R&D data products and scientist self-service insights.
As a global agrochemical and seed company serving the agriculture industry, Syngenta’s mission is to secure the global food supply by protecting crops from weeds, fungal diseases and insect pests. With 6,000-plus researc
Syngenta is a global science company that designs, manufactures and sells crop protection products (e.g., herbicides, fungicides, insecticides and biologicals) and high-performing seeds to farmers worldwide. Their offeri
Unfortunately, the diversity of data only added to the complexity. Researchers were working with everything from lab results and drone imagery to external contractor reports and commercial data. Bringing those sources to
think big, start small, scale fast,
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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