Enterprise Cloud Data Modernization Platform

The Vendor is required to provide data management and analytics platform to replace fragmented legacy architecture (primarily SQL server-based) with a scalable, high-performance ecosystem that will serve as the organization’s single source of truth (SSOT).
- To enable secure, seamless movement, transformation, and governance of enterprise data across diverse sources and environments, while supporting advanced analytics and future AI/ML initiatives.
- The modernized data platform will support enterprise data integration, governance, business intelligence, and future artificial intelligence and machine learning initiatives while ensuring compliance with healthcare regulatory requirements and data security standards.
- Requirements
•    Enterprise cloud data platform (e.g., snowflake, data bricks, Microsoft fabric, or equivalent);
•    Data ingestion and integration framework].
- Enterprise cloud data platform core capabilities:
1. Administration, availability and manageability
•    Offering available on multiple cloud providers
•    Unified console for monitoring, auditing, and administration.
•    Features include:
o    Disaster recovery and high availability
o    Backup/restore/snapshots
o    Continuous data protection (CDP)
o    Historical query (time travel and flashback)
o    Shallow cloning
o    Intrusion detection and prevention
o    Vulnerability management
2. Business analysis capability
•    Full integration with Microsoft power bi and tableau, providing seamless connectivity for data visualization, reporting, and analytics. 
•    Advanced analytics capabilities for business users and data analysts. 
•    The capability to execute external binary code or runtimes (e.g., java based applications) within the platform environment, including support for integrating third-party or externally published software without requiring extensive re-engineering. 
•    A secure enterprise data marketplace with curated datasets, governance, and metadata visibility to enable rapid analytics without heavy data preparation.
3. Analytics and user productivity
•    Support for building and hosting lightweight, interactive data apps using modern frameworks (e.g., python).
•    Governed access to enterprise data sources for dashboards and workflows without extensive front-end development. 
•    Advanced feature: generative AI-powered analytics (e.g., natural language to SQL, query discovery).
4. Data governance and security
•    Unified framework for technical, business, and operational metadata management.
•    End-to-end data lineage to ensure transparency, compliance, and trust.
•    Data masking, encryption and retention policies
•    Built in privacy controls (e.g. Ml-powered auto-classification)
5. Cloud financial governance
•    Ability to track cost allocation and enforce cost policies.
•    Granular cost forecasting and budgeting by business unit.
•    Automated resource optimization and cost governance (govern types and numbers of resources used).
•    Tools for modeling costs and recommending cost optimization strategies.
6. AI SQL and integrated AI/Ml
•    Built-in AI capabilities for query optimization and advanced analytics.
•    Integration with pre-trained AI/ML models (e.g., NLP, forecasting, anomaly detection).
•    Advanced feature: secure, governed environment for training and deploying custom AI/ML models within the platform, minimizing data movement and ensuring compliance.
- Data ingestion and integration framework core capabilities:
•    The platform (or complimentary tool) must include the following capabilities to hydrate the data platform from heterogeneous source systems and support hybrid deployment models.
•    The solution must support replication between heterogeneous database management systems (DBMS) platforms, ensuring data consistency and low-latency synchronization.
•    The solution must provide secure, reliable connectivity to the following data sources for ingestion and integration:
o    Microsoft SQL server (transactional and legacy data warehouse)
o    Flat files
o    Microsoft excel
o    Microsoft data verse
o    Microsoft SharePoint
o    Microsoft dynamics 365
o    Salesforce
o    Rest APIs
o    Pdfs
o    Cloud storage (data lake)
o    Public and private endpoints
o    Streaming platforms
•    The solution must support multiple ingestion patterns to accommodate diverse data integration needs, including:
o    Bulk and batch processing – high-performance historical and scheduled data loads. 
o    Replication and synchronization – automated change data capture (CDC) to maintain source-to-cloud parity.
o    Streaming ingestion – real-time or near real-time ingestion with latency under 15 minutes for operational requirements. 
o    Optional: data virtualization – ability to query across disparate sources without physically moving data, where appropriate.
•    The solution must support a hybrid deployment model that enables secure connectivity and seamless data movement between on-premises infrastructure and cloud environments, ensuring compliance with enterprise security and residency requirements.
•    Hardware requirement, whether on-premises, hosted, etc.: no on-premises hardware required (cloud-native).