The vendor is required to provide to modernize its data infrastructure to improve accessibility, integrity, and analytical capabilities.
- This initiative is part of a broader strategic effort encompassing data governance, technological modernization, and personnel enhancements.
- Agency to integrate structured, semi-structured and unstructured data into a single platform, with the ability of real-time and batch data processing, advanced analytics, machine learning directly on raw data without requiring data movement, with role-based access control, encryption and data lineage tracking, making data secure and regulatory compliant.
• Technology modernization:
o Migration from legacy data warehouses to a modern, scalable platform.
o Implementation of advanced data processing, integration, and governance tools.
o Adoption of cloud-based or hybrid infrastructure to enhance performance and accessibility.
• Data governance and integrity:
o Establishing standardized data definitions and structures.
o Implementing data validation mechanisms to ensure consistency and accuracy.
o Enabling seamless data integration across agency institutions.
• Analytics and reporting capabilities:
o Deploying real-time and historical reporting and self-service analytics tools.
o Supporting ai-driven insights and predictive analytics for institutional and strategic decision making.
o Enhancing security, compliance, and privacy controls.
o Ensuring intuitive and user-friendly data access for various stakeholders.
o Reducing technical barriers for non-technical users.
• Training and support:
o Provide training and support of the platform
o Provide training and support for the integration and transformation of the data.
o Provide training and support for effective data utilization, analytics and reporting.
- Design
a. Data management
1. Data storage and governance
• Support for structured and unstructured data storage
• Compliance with data governance frameworks.
• Data versioning and lineage tracking for auditability.
• Data quality management (e.g., validation, cleansing, enrichment).
• Metadata management capabilities (e.g., tagging, cataloging).
• Ability to leverage distributed computing for large datasets.
• Integration with cloud-native service
• Scalability to accommodate increasing data volumes while maintaining performance.
2. Data security and compliance
• Encryption of data at rest and in transit.
• Role-based access control (RBAC) and fine-grained permissions.
• Auditing and logging of user actions for security and compliance purposes.
• Built-in compliance with industry standards.
• Secure data access and transmission protocols.
• Ability to anonymize or mask sensitive data.
• Integration with enterprise IAM solutions
• Multi-factor authentication (MFA) and single sign-on (SSO) support.
b. Data integration
3. Integration with existing systems
• Ability to integrate with a variety of data sources.
• Support for real-time, batch, and hybrid data integration.
• Pre-built connectors for higher education platforms.
• Pre-built connectors for higher education data sources.
• Ability to create connectors to other necessary data sources in various formats
• Data synchronization management and troubleshooting capabilities.
• Compatibility with existing it architecture and technologies.
• Open standards support.
4. ETL/ELT processes
• Support for both ETL (extract, transform, load) and ELT (extract, load, transform) workflows.
• Data transformation, cleansing, and mapping capabilities.
• Automation and orchestration of data pipelines.
• Monitoring, error-handling, and alerting for failed jobs.
• Tools for scheduling, managing, and monitoring data pipelines.
• Support for event-driven processing or real-time streaming
• Built-in data transformation engines
c. Analytics and reporting
5. Data analysis
• Real-time and historical data analysis and visualization.
• Self-service analytics tools for business users (e.g., dashboards, reports).
• Support for bi tools or incorporation of bi tools within the solution.
• Advanced analytics capabilities (e.g., machine learning, artificial intelligence, predictive analytics, data mining).
6. Data visualization
• Customizable dashboards with interactive visualizations.
• Support for key metrics, KPIs, and trend analysis.
• Drill-down capabilities and ad-hoc reporting.
• Ability to publish dashboards and visualizations to both public and private audiences.
7. Data sharing and collaboration
• Ability to share reports and insights securely across departments or external partners.
• Collaboration features (e.g., commenting, annotations on visualizations).
• Export options (e.g., csv, excel, pdf).
d. Performance, scalability, and maintenance
8. System monitoring, availability, and performance
• High availability and disaster recovery architecture.
• Distributed architecture with failover mechanisms.
• Automatic load balancing and optimized query processing.
• Data caching and optimization for faster query response times.
• Real-time monitoring of data integration and analytics processes.
• Automated alerts for system failures, performance degradation, or data inconsistencies.
• Centralized logging for debugging and diagnostics.
• Easy-to-manage software updates and patching.
• Support for version control and rollback capabilities.
9. Data backup and recovery
• Automated backups with retention policies.
• Granular backup options (e.g., full, incremental, differential).
• Disaster recovery capabilities and recovery point objectives (RPO) and recovery time objectives (RTO).
e. Future roadmap and innovation
10. AI and analytics innovation
• Continuous ai and analytics innovation, including predictive modeling, generative ai, and natural language querying.
• Mechanisms to monitor for bias and fairness in ai models, particularly in admissions and student support.
11. Modular feature activation
• Ability to activate and deactivate features without disrupting workflows.
12. User-driven enhancements
• Active engagement in agency -driven enhancements, incorporating user feedback into product updates.
f. Support and maintenance, software licensing, change management
13. Support and maintenance
• 24/7 customer support (if required).
• SLA (service level agreement) guarantees for uptime and issue resolution.
• Training materials, documentation, and user guides.
14. Software licensing and cost
• Clear pricing structure (e.g., subscription, per-user, per-data-volume) for campuses and agency system collectively.
• Licensing models and associated costs for long-term maintenance and updates for campuses and agency system collectively.
15. Change management and upgrades
• Support for smooth upgrades and minimal disruption to service.
• Vendor-driven product roadmaps and future features.
16. Clear resource needs for implementation and maintenance of system
• Clearly defined, personnel, financial and technical commitment for each phase of project implementation and for ongoing maintenance of system for both campuses and agency system administration
17. Severance and retention
• Ability to retain all data and migrate to new system as warranted by business needs.
- Contract Period/Term: 3 years
- Questions/Inquires Deadline: May 28, 2025