Higher Education Data and Analytics Platform and Data Catalog Solutions

The vendor is required to provide higher education data and analytics platform for include:
•    Provide a scalable, secure, and flexible platform that incorporates a data warehouse, data lake, and other modern technologies used to aggregate, integrate, and prepare data for longitudinal analysis for a variety of use cases. 
•    Support student success, enrollment, finance, operations, and compliance reporting. 
•    Where appropriate, employ artificial intelligence and machine learning to enable for advanced analytics while ensuring privacy. 
•    Integrate seamlessly with the university’ core administrative and student information systems
•    A user experience that empowers both navigators and explorers.
1. Data integration and storage
•    Structured, semi-structured, unstructured data.
•    On-premises, cloud, or hybrid deployment models.
•    Real-time or near-real-time ingestion.
•    Prebuilt integrations with banner, slate, course-dog, canvas and other higher education information systems.
2. Analytics and reporting
•    Prebuilt reporting data models for higher ed (enrollment, retention, finance, etc.).
•    Self-service analytics, visualizations, and dashboards.
•    Support for both navigators and explorers in finding and using data.
•    Data storytelling and natural language query capabilities.
•    Advanced analytics: e.g., forecasting, benchmarking, ai-driven insights.
3. Governance and security
•    Role-based access and audit trails.
•    Support for acts, and other relevant compliance obligations.
•    Data lineage, quality monitoring, and impact analysis.
- Data catalog, dictionary and glossary
•    Provide a centralized metadata repository accessible across the university.
•    Ensure consistency in terminology and data definitions.
•    Increase transparency and trust in institutional data.
1. Data catalog
•    Enterprise-wide searchable inventory and classification of data assets.
•    Metadata discovery, enrichment, and lineage tracking.
•    Integration with analytics and reporting tools.
2. Business glossary
•    Institution-wide definitions of business terms (e.g., student’s current standing etc.).
•    Support for semantic relationships, synonyms, and taxonomies.
3. Data dictionary
•    Technical metadata (data types, constraints, default values, relationships).
•    Owned and maintained by it but available for cross-functional use.
4. Governance features and user support
•    Governance roles: data governance steering committees, data governance councils, data trustees, data stewards, and data custodians.
•    End-user experience: ensuring that both navigators (who need direct access to specific datasets or definitions) and explorers (who browse and discover available data) can effectively engage with the catalog. 
•    Workflow and collaboration: approval processes, stewardship assignments, role-based collaboration, and version control.
•    Transparency and accountability: clear traceability of business definitions, ownership, and usage.