An Analytics Data as a Service (DaaS) initiative is being piloted in 2018, with a view to enhancing our ability to provision data to enable the advanced analytics capabilities at UQ. Analytics, supported by a well-governed big data infrastructure and governance practices, will improve UQ’s ability to monitor student success, and overall organisational efficiency and risk, in a more accurate and timely manner.

Sophisticated analytics capability will be critical for organisations to compete and survive into the future. The DaaS initiative is designed to underpin such a capability and will complement the existing Business Intelligence function to service the needs of analytics and data science teams within UQ, and provide a data and governance foundation for more advanced Machine Learning (ML) and Artificial Intelligence (AI) capabilities in the future.

A basic taxonomy of analytics is emerging in higher education that comprises two main categories: learning analytics and institutional analytics. The former is more concerned with various aspects of the student experience; the latter focuses on the business side of higher education. The Analytics Data as a Service pilot projects will involve servicing the data needs of both a learning analytics project with the Faculty of Medicine and ITaLI, and institutional analytics project with P&F and Queensland Centre for Population Research (QCPR).

Analytics Data as a Service Benefits

By supporting analytics functions across UQ, a new Data as a Service capability will complement the existing Business Intelligence function to enhance the achievement many of UQ’s strategic initiatives, including these key strategic objectives:

  • Improve student retention rates through a pro-active supportive approach
    • Learning analytics can enable identification of students at risk of failure or other retention-related risks
  • Support innovative teaching practices to deliver better learning outcomes for students
    • Benchmarking teaching innovations against traditional or alternative approaches will allow the success of new practices to be measured
  • Utilise our information systems to personalise and enhance the quality of our students’ learning
    • Analytics can be used to drive individualised human or digital interactions with students based on their data
  • Support academic staff in the use of appropriate and proven technology-enriched educational approaches
    • Analytics can inform teaching activities, e.g., analysing progressive assessment or quizzes to identify concepts or topics that students are struggling with
  • Continue to improve the capability and reliability of our IT infrastructure
    • Log analytics is an important discipline to pre-emptively combat security threats, monitor system health, report on resource utilisation trends, and so on.
  • Consolidate investment in enterprise systems and support processes
    • Providing a central governed repository of data for analytics teams will reduce the prevalence of poorly governed “shadow” repositories across the organisation
  • Review and improve our practices, systems and business processes to ensure they utilise resources efficiently and facilitate a modern, inclusive, and globally focused organisation
    • DaaS will help centralise and standardise data provisioning in support of advanced analytics to help UQ effectively compete in the global higher education market.