Vendor needs to provide enterprise data platform. SaaS enterprise data platform capabilities. Data ingestion, processing, modeling, governance, quality, and consumption. Support for structured and semi–structured data. Integration with ERP, operational systems, GIS, and external data feeds. Operational monitoring and administration capabilities. Replacement of transactional systems of record. Custom application development unrelated to data platform enablement. On–premises–only solutions. Respondents must clearly state any assumptions that materially differ from those listed above. Cloud–hosted SaaS delivery model. Alignment with FedRAMP Moderate security requirements. Enterprise identity and access management (SSO, RBAC, row–level security). Multiple isolated environments: Dev, Test/QA, Pre–Prod, Prod. Hybrid transition period where legacy and new platforms may operate in parallel. Lack of failover and disaster recoverySolution Architecture. High–level logical and physical architecture. SaaS deployment and tenancy model. Workload and environment isolation approach. Scalability, high availability, and disaster recovery capabilities. Data Modeling and Semantic Layer. Data modeling philosophy and supported patterns. Semantic layer capabilities and governance. Support for multiple semantic models over shared data. Business logic versioning and performance considerations. DATA GOVERNANCE, METADATA, AND LINEAGE. Business and technical metadata management. End–to–end lineage from source to consumption. Stewardship workflows and approvals. Native capabilities versus integrations with external tools. AI and Advanced Analytics. AI–assisted data quality, classification, or automation features. Support for ML integration, notebooks, or model lifecycle management. Geographic Information System (GIS) Capabilities: Ability to ingest, store, and manage spatial and geospatial data alongside non–spatial enterprise data. Support for spatial data types, spatial indexing, and geospatial query and analysis functions. Approach for relating GIS data to enterprise data models, including support for large–scale joins, aggregations, and performance optimization. Integration architecture with third–party GIS tools or services, including supported standards and any functional limitations. Governance, security, and access controls for spatial data, including role–based and rowlevel enforcement. Support for internal analytics, reporting, and controlled external publication of GIS–enabled data where applicable. All question must be submitted no later than February 27, 2026.