USDA NASS
Modernizing the Systems That Power America’s Agricultural Intelligence
Challenge
The U.S. Department of Agriculture’s National Agricultural Statistics Service relies on complex legacy systems to collect, process, and publish agricultural data critical to national policy, markets, and economic forecasting. Built on Sybase, PowerBuilder, and FoxPro, these environments struggled with scalability, fragmented data, and heavy manual reporting as data volumes and real-time demands grew. NASS required a partner capable of modernizing these systems without disrupting mission-critical statistical production.
Solution
WINTrio transformed NASS’s legacy ecosystem into a secure, cloud-based platform on Azure Government, migrating decades of agricultural data while preserving lineage, integrity, and uninterrupted operations.
- Enterprise Agricultural Data Platform: Designed a cloud-native architecture centralizing datasets for scalable storage, analytics, and secure access across statistical programs.
- Nationwide Survey Modernization: Enabled data collection and processing across 12 regional offices, improving coordination and accelerating reporting cycles.
- Secure Data Governance: Implemented role-based access controls integrated with enterprise identity services, ensuring compliant and auditable data access.
- Real-Time Analytics & Decision Support: Replaced manual reporting with interactive dashboards, enabling policymakers and analysts to access insights on demand.
- Reusable Modernization Framework: Established a microservices-based architecture with standardized services including auth, logging, and data access, accelerating future development and reducing duplication.
Outcomes
- Migrated decades of agricultural datasets into a modern, cloud-based architecture
- Modernized 10+ mission applications supporting survey operations, data management, and dissemination
- Enabled nationwide operations across 12 NASS regional field offices
- Reduced manual reporting and data preparation effort by approximately 50%
- Accelerated agricultural data processing cycles by up to 70%
- Reduced manual commodity classification by 76% through machine learning models
- Achieved approximately 40% enterprise code reuse through platform standardization
- Delivered full modernization with zero disruption to active national survey cycles
