13th AUN Rectors’ Meeting: Data, a Powerful Enabler of Research and Innovation, and the Data Infrastructure of NUS

The 13th AUN Rectors’ Meeting articles have been one of AUN Secretariat’s longest standing series of coverage of a conference. Starting all the way since late September, articles about the 13th AUN Rectors’ Meeting and 37th AUN Board of Trustees have now come to the final week in the early presence of March. For the final coverage of the 13th AUN Rectors’ Meeting, the theme of “Data Strategies and the Achievements of High-Impact University Missions” continues with National University of Singapore. Dr. Taizan Chan, Chief Data Officer of NUS, took to the podium to share about NUS’s approach to data management.

First things first, when it comes to legalities, NUS adheres to Singapore’s Personal Data Protection Act (PDPA), and other relevant data use and privacy laws. The Act covers aspects such as limitation of purpose, consent, notification, access and correction, accuracy, data protection, transfer and retention. Law aside, NUS believes data is a powerful enabler that serves to drive new insights, innovate, and bring forth new knowledge. Then, with proper use of data, it helps to promote more evidence-based decision making, and in research, it also leads to increased efficiency, improved processes, and opens the door for more potential synergies with various fields. 

In order to adhere to the PDPA and utilize data effectively, NUS has created their own data framework that lays the foundation for their data strategies. As such, NUS’s Data Management Policy stresses the following 6 principles:

1. Shared Responsibility through Data Stewardship and Usage
2. Confidentiality through Data Classification
3. Single Source of Truth through Data Collection and Storage
4. Need To Know through Data Sharing and Disclosure
5. Need To Keep through Data Retention and Disposal
6. Security through Data Protection

When it comes to research, NUS also has specific guidelines to ensure security and usability of stored data. Firstly, the aforementioned principles of NUS’s Data Management Policy will determine how data is collected, used, managed, and stored. Secondly, all external data must be acquired ethically and lawfully. The last specific guideline in research data is that securely retained Proprietary Research Data permits a complete retrospective audit for a minimum of 10 years.

The highly effective Data Management Structure of NUS, ALSET 
(Applied Learning Sciences and Education Technology)

Dr. Taizan proceeded with sharing NUS’s data infrastructure, called the ALSET Data Lake. This infrastructure lays out the flow of data that starts from either researchers or operation systems. After this, the data will be collected, managed, and securely stored within NUS’s database through the Data Governance system. The server that handles the data can then analyze the data through its engine and is always ready to embed the results into a readable user interface. With a well-developed data infrastructure, NUS researchers and staff will be able to request data for various uses, such as the following:

Research uses:

• Understanding students’ natural rhythms (chronotypes and programme trajectories)
• Identifying factors influencing heterogeneous outcomes (grades, salary)
• Evaluating the effects of policy changes and classroom innovations
• Supporting longitudinal research designs with balanced samples
• Avoid the cold start problem by providing live, but de-identified, data for student projects and theses

Staff uses:

• Ready data source for training of staff (Intermediate Data Literacy Program)
• Extract custom data sets for department academic programme evaluations (SCALE, NUS Medicine, Residential Colleges)
• Accessible data science platform for analysis of school admissions criteria (NUS Medicine)
• Safely monitor adoption of new campus initiatives without identifying staff and students (Covid-19, Design Your Own Module)
• Archive non-mission critical, but interesting research, data for future uses (Libraries, Graduate Employment Survey)

Examples of NUS data usage for the benefit of education, research, organizational excellence, and enterprise show how much of an impact properly utilized data can have. In education, NUS can identify first-choice-offers as the top influencer of admission offer acceptance and apply appropriate strategies to achieve higher acceptance offers. Once more, when it comes to research, NUS has been using data to identify low-quality journals in each subject area. After the data is analyzed, researchers are then encouraged to avoid publishing in the bottom 10%, and this has resulted in overall less publications in low impact journals and a higher Field Weighted Citation Impact (FWCI).

Organizational excellence is where NUS can analyze different segments of received data for differential engagement. The segmentations can be based on different categories such as demographic and education data, alumni status, estimated income, and etc. Once the data is analyzed, NUS can target engagement with subgroups most appropriate for the category. This allows the marketing of content that is specifically designed for the targeted subgroup for maximum appeal and has resulted in NUS achieving a 3 times higher response rate!

NUS’s data infrastructure used for the purpose of enterprise has greatly improved the collection of data in their efforts towards entrepreneurial education, entrepreneurship support, and technology translation and commercialization. With this, NUS can develop key performance indicators and track the progress of their enterprising strategies. The analysis will assist NUS in ensuring their enterprise’s strategies and target formulations are aligned with those of the University’s own.

Examples of Data from NUS being visualized into easy-to-read charts, graphs, and bars

To pave the way forward, NUS is also offering an extensive range of courses from undergraduate to postgraduate (including Ph.D.) in data science, analytics, and usage. Along with offering education in this field, increasing staff competencies is also a priority in order to foster a culture of data-use within the institution. NUS is undergoing the process of making sure the staff is well equipped with relevant knowledge by taking courses on AI, digital literacy, and digital enablement. The staff also partakes in many activities such as workshops and hackathons where group collaborations are greatly encouraged and provides the experience of tackling real challenges.

And this concludes the long-standing series of the 13th AUN Rectors’ Meeting! We, at AUN, hope the articles have provided valuable insights into the best strategies in University Excellence. If you would like to read all the other articles related to the AUN Rectors’, click here!