{"slug":"cnh-analyzes-geospatial-data-to-improve-crop-outputs","url":"https://findausecase.com/use-cases/cnh-analyzes-geospatial-data-to-improve-crop-outputs","title":"CNH analyzes geospatial data to improve crop outputs","description":"CNH used the Databricks Data + AI Platform to drastically reduce the time it takes to analyze massive volumes of geospatial data on weather patterns and crop outputs, using Delta Lake, Unity Catalog, and built-in H3 indexing to scale geospatial aggregations that previously were not possible to analyze.","company":"CNH","industry":"Manufacturing","aiCapabilities":["AI Model Development & MLOps"],"technology":["Delta Lake","Unity Catalog"],"deployment":"Unknown","problemStatement":"CNH needed to analyze agricultural geospatial data at scale — with tens of thousands of polygons ingested daily — to identify how weather patterns impact crop outputs, but it was difficult to add even basic geographic insights into reports or dig deeper into the data.","solutionApproach":"CNH used Delta Lake to ingest and store data with ACID transactions and scalable performance, Unity Catalog for fine-grained access controls and governance, collaborative notebooks for cross-team work, and the built-in H3 indexing system and Databricks's spatial library to analyze geospatial aggregations.","businessValue":"CNH significantly reduced the time it took to run geospatial aggregations — what previously took a full day can now be done in just two hours — reducing storage and compute costs, allowing daily refreshes, and letting anyone on the team add geographic information to reports without a specialized geospatial background.","evidence":{"band":"high"},"sourceUrl":"https://www.databricks.com/customers/cnh","dates":{"publishedAt":"2026-09-08T09:04:03.638Z","publishedAtSource":"pipeline","updatedAt":"2026-09-08T09:04:03.638Z"},"license":"Open for reading and citing with a link to https://findausecase.com/use-cases/cnh-analyzes-geospatial-data-to-improve-crop-outputs. Bulk republication requires permission."}