Data Platform Implementation Partner

Opportunity overview

Award of Data platform implementation partner. The University is seeking to design, build and implement a modern, enterprise-grade data platform to replace the existing on-premises Oracle data warehouse. The new platform will provide a scalable, secure and future-ready foundation capable of supporting the full ecosystem of data ingestion, transformation, semantic modelling, operational reporting, strategic insights, and AI-enabled analytical capabilities. The platform will form a critical foundation for decision making across the University, supporting academic operations, student lifecycle management, finance, HR, research and other domains. It will empower approximately 4,000 internal users while indirectly supporting services that impact more than 20,000 students. The solution will be developed using Microsoft Azure and Microsoft Fabric as the strategic technology stack. It must deliver high levels of scalability, improved data accessibility, and automation, alongside advanced analytics capabilities. The platform will also be designed to support future integration of AI agents and LLM-based natural language querying, enabling more intuitive and democratised access to insights. As part of its broader analytical capability, the platform should provide the foundations to enable advanced learning analytics and support data modelling use cases aligned to student recruitment, retention and engagement. There is an expectation that the delivery will include at least one agreed proof of concept to demonstrate the platform's advanced analytics capabilities. The specific use case will be defined collaboratively, but is expected to align to one or more of the following innovation areas: • Predictive analytics (e.g. student number forecasting in collaboration with Strategic Planning) • Advanced learning analytics capabilities • Data modelling solutions to support student recruitment and engagement The final scope and prioritisation of this proof of concept will be agreed during the delivery phase to ensure alignment with institutional priorities and data readiness. A key objective is to democratise data access and capability across the University. The platform will provide governed self-service capabilities, allowing teams beyond the Information Management function to access curated data models and develop their own data modelling solutions where appropriate, while maintaining strong data governance and quality standards. The scope of work includes the design, implementation and migration of approximately 1,200 ODI mappings, over 35 database schemas, and 3,000 tables. It also includes comprehensive documentation, training, and a structured knowledge transfer programme to ensure long-term sustainability. The supplier will ensure that the final solution is production-ready, secure, fully documented, and successfully transitioned into business-as-usual support structures, while enabling ongoing evolution of analytical capabilities over time. The University of Portsmouth requires an experienced supplier to deliver a complete cloud‑based data platform to modernise its existing data estate and enable future analytical growth. The contract will cover end‑to‑end delivery: architecture design, build, migration, operational readiness and early‑life support. The University plans to retire its legacy Oracle data warehouse and build a new data platform, migrating key datasets that remain in active use. This will improve performance, ensure long-term sustainability, and open new capabilities such as AI-driven querying and predictive analytics. The platform will support both strategic and operational reporting, including self-service tools, automated data updates, and advanced modelling aligned with the University's goals. Beyond the development of the core data platform, the University is interested in working with the successful supplier to explore innovative applications of data. This may take place alongside the implementation or once the platform is established, and could include areas such as learning analytics, forecasting, and other advanced insights.

Awarded to

Amounts belong to published awards or contracts. Where several suppliers share one value, it is labelled as shared and is not a supplier-specific allocation.

Classification

No classification was supplied.

Lots

• Lot 1Value not stated

Documents and notices

• Contract details notice on Find a TenderOpen source ↗