Connect with potential future students in a more targeted and personalised manner
Facilitate ambitious research projects by integrating data from in-house and third party sources
Safely and securely share data with other research institutions
As both education facilities and research institutions, universities and other higher education institutions need to balance two goals: on the one hand, they need to attract students and offer quality education. On the other, significant pressure is put on these institutions to obtain research grants for conducting interesting research and sharing meaningful results. Expert data management is a crucial part of accomplishing both goals. From an education perspective, a well-integrated data architecture can help institutions connect with potential students and offer better education to their current ones. From a research perspective, expert data management solutions enable higher education institutions to easily access and integrate data from various third-party sources and share their own data with other institutions in turn.
The kind of data management that education and research institutions can benefit from often require specialised solutions. Our Datastreams Platform is our state-of-the-art data streaming and collaboration solution. It allows organisations to collect, clean, stream and share data in a secure and compliant manner. This enables education institutions to:
- Utilise data-driven marketing strategies to connect with potential future students in a more targeted and personalised manner.
- Perform learning analytics on an integrated dataset of university data to assess and improve student experience and identify ways to increase performance.
- Craft personalised learning experiences and offer better student support by safely and securely streaming student data to learning platforms and CRM systems.
- Facilitate ambitious research projects by collecting and integrating data from a variety of in-house and third-party sources.
- Leverage the power of event-stream processing in machine learning to incrementally process huge datasets or continuously optimise on-line models.
- Encourage follow-up and replication research by easily and securely sharing research data with other research institutions.
