RMIT Cuts Manual Data Work by Up To 75% Using AWS Data Lake, Delivering Tailored Student Support

With the rise of transformative technologies such as artificial intelligence (AI) and generative AI, RMIT recognized that fragmented data and manual processes were limiting its ability to improve student outcomes and innovate in response to rapid changes in the education sector.

Fragmented systems across academic, enterprise, and research functions required staff to manually process data, including students’ socioeconomic status, attendance records, and course performance metrics, from diverse sources. This time-intensive approach delayed insights critical for identifying students at risk of dropping out, struggling academically, or facing financial or engagement challenges.

To position itself as a leader in data-driven education, the university wanted to address these challenges while enhancing its responsiveness to regulatory changes and meeting the growing expectations for personalized student experiences.  

In 2020, RMIT worked with AWS to implement a centralized data lake solution. The university established a scalable framework that unified 54 datasets from academic, enterprise, and research platforms. AWS Partners Slalom and The Data Foundry implemented the AWS cloud architecture, while Slalom executed integration to Snowflake and other components. The centralized platform now provides secure, accessible, and actionable data for staff across departments. With this transformation, RMIT gained real-time insights into student journeys, improving its ability to respond quickly to regulatory changes and strengthen financial oversight.

The university built its data lake using Amazon Simple Storage Service (Amazon S3), a secure, scalable object storage service for unifying datasets from across the university. By centralizing data on Amazon S3, RMIT established a robust foundation for real-time insights and decision-making.

As part of this implementation, the university developed a student monitoring dashboard that tracks metrics such as attendance, course performance, and socioeconomic status. Staff now identify at-risk students earlier and deliver timely interventions tailored to their needs, significantly improving student retention.

To integrate its fragmented systems, RMIT used a number of AWS services such as Amazon DynamoDB and the AWS Database Migration Service (AWS DMS), which securely migrates databases to AWS with minimal downtime. By automating the ingestion of all student surveys to a single platform, RMIT reduced processing time for coding responses by 75 percent. This freed staff to focus on strategic decision-making based on insights and improving the quality of courses delivered to students.

RMIT developed its capabilities in predictive analytics to analyze diverse data sources and identify students at risk of dropping out or struggling academically by deploying Amazon SageMaker, a service that simplifies building, training, and deploying machine learning models. This proactive approach supports early interventions that improve retention rates and align with individual student needs.

RMIT’s cloud analytics capability supports timely decisions and adaptation to changing needs. In response to new government regulations capping international student admissions, RMIT demonstrated the platform’s adaptability and agility by swiftly creating dashboards that monitor compliance, verify student eligibility, and align admissions processes to avoid penalties.

In a recent initiative, RMIT also worked with AWS on an Experience-Based Accelerator (EBA) project to develop a generative AI assessment tool. This innovative solution, created within three days, streamlined grading processes and reduced assessment time, helping staff optimize their efforts and focus on teaching and strategic initiatives.  

RMIT's data-first strategy has transformed how the university supports student success and improves learning outcomes through advanced analytics. Looking ahead, RMIT plans to expand its data analytics capabilities by exploring new applications of generative AI using Amazon Bedrock, a service for building and scaling generative AI models.

The university also plans to enhance its predictive analytics to deliver more personalized support and insights. It will continue onboarding new datasets to deepen its understanding of student journeys. With plans for further expansion, RMIT aims to use its data lake to scale operations and address the evolving needs of students, staff, and regulatory bodies.

Additionally, the university is fostering a culture of data literacy. It is equipping staff with the skills to translate data-driven insights into impactful decisions that improve student outcomes and support organizational adaptability.

"At RMIT, our partnership with AWS has redefined how we use data to enhance student success. With the scalability and agility of our data lake solution, we can adapt quickly to changing educational needs while staying focused on our core mission—empowering students through personalized support and meaningful insights. Following a proposal from the Federal Government to introduce international student cap legislation in Australia, we were able to harness data on the platform with speed and reliability to assess the potential impact. This helped our leadership to have a data-driven discussion and respond quickly with an appropriate strategy," said Nonna Milmeister, Chief Data & Analytics officer, RMIT University.