Integrated Data and AI Platform

The Vendor is required to provide the heart of these drivers is the need for a sophisticated, integrated data and artificial intelligence (AI) platform.
¬- Utilizing predictive analytics to identify at-risk students and prescribe personalized intervention strategies to enhance retention and graduation rates.
- Analyzing industry demand data and employment trends to align micro credentials, diplomas, and degrees with the evolving needs of the state workforce.
- Facilitating multi-disciplinary research through centralized data repositories that support complex analysis in Agency four areas of focus: energy innovation, manufacturing, health technology, and social innovation.
- Real-time operational analytics and collaboration data to optimize campus environments and resource allocation.
- Leveraging data-informed insights to refine marketing strategies and demonstrate institutional impact to provincial and national stakeholders.
- Analytics supporting partnership evaluation, ROI measurement, and collaborative opportunities identification.
- Implementing advanced HR analytics to optimize recruitment, support professional development, and improve employee engagement.
- The Integrated Data and AI Platform will function as Agency single source of truth for institutional data, enabling self-serve analytics, AI-informed insights, and advanced decision support across academic, operational, and research domains. 
- The platform must support real-time or near-real-time data integration and provide scalable, flexible architecture capable of hybrid and multi-cloud deployment.
- The solution must accommodate growing data volumes from existing and emerging systems while maintaining security, compliance, and performance standards.
- Agency anticipates serving approximately 1,000+ internal users (faculty, staff, researchers) with varying levels of data literacy, requiring intuitive interfaces and comprehensive governance frameworks.
- Provide a modern, cloud-native, integrated platform to centralize disparate data sources, democratize data access, enable natural language querying, support AI/Machine Learning (ML) capabilities, and support comprehensive data governance.