The QS Conference held in Singapore on June 3rd and 4th 2026 brought together higher education leaders to discuss key topics shaping the future of universities, including AI, data, and digital transformation. Throughout the conference, a common theme emerged: AI cannot be successfully implemented without strong data governance and high-quality data.
Marcela Hernandez, Chief Data Officer at UBC, presented "Improving Data Literacy from Compliance to Confidence," a masterclass focused on helping institutions move beyond reporting requirements to build confidence in using data for insight, decision-making, and innovation. The session explored practical approaches to strengthening data understanding across different roles, creating a culture where staff feel empowered to interpret and apply data, and embedding data literacy as a core institutional capability. Marcela’s presentation and panel discussion were well received, particularly as many universities in Asia and the Middle East are investing in data initiatives but are often unsure where to begin.
As part of the conference, Marcela had the opportunity to visit the University of Singapore, where there is a strong data governance strategy. “The National University of Singapore is building a data repository similar to UBC’s University Data Platform (UDaP) to provide standardized, reliable, and secure access to current and historical data across the university,” said Marcela. “Having the opportunity to visit was a valuable experience and a highlight of the conference.”
One of Marcela's key takeaways was the positive feedback she received about UBC's work. Attendees commented on the value of UBC's efforts to build data literacy while establishing data governance. Many also noted that her presentation connected strongly with broader conference discussions by demonstrating how strong data management serves as a key success factor for AI. As Marcela notes, “AI cannot be done without data governance. There were many conversations [at the conference] about AI emphasizing that high-quality data is needed for AI to produce meaningful results.”
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