The Decision Analytics Outstanding Award 2025 Seminar was successfully held on 6 June 2025, featuring two distinguished speakers whom shared their insights on the latest trends in data analytics and its applications.
Date: 6th June 2025 (Friday)
Time: 2:30 PM – 4:00 PM
Venue: Room A403
Speakers:
Name: Dr Yue Wang, Associate Professor of Department of Mathematics and Information Technology, EdUHK
Topic: Advancing Product Ideation with Data Analytics and AI
Abstract: In the competitive landscape of modern business, product ideation plays a crucial role in driving innovation by identifying and addressing new customer needs. Traditionally, methodologies such as focus groups, interviews, and lead user analysis have been employed, yet these approaches often prove expensive and time-consuming, limiting their practicality in fast-paced environments. In this talk, I will present our research, which uses data analytics and artificial intelligence to reshape product ideation. Our work began with tapping into the wealth of data found in online product reviews, treating this valuable source of consumer insight as an outline detection problem. Building on this, we explored the potential of large language models (LLMs) to generate customer needs and insights automatically. These AI-driven models, with their ability to process large amounts of text data and identify subtle patterns, offer new opportunities for innovation and adapting to market demands.
Name: Mr Willson Deng, CEO of Arcstone
Topic: Rationalizing Uncertainty in a Uncertain World
Abstract: Making the perfect decision requires an infinite amount of data. Collecting an infinite amount of data requires an infinite ability to acquire and structure it as well. This is a truly impossible task. However, bringing “enough” data into an actionable context and applying decision science allows us to maximize our chances of getting an optimal result. As such we see the future of decision science being the intersection of data, statistics and AI. The future of decision making will be the interaction between AI recommendations/triggers and human actions for short term and long-term decision making respectively to attempt to rationalize and make better decisions against infinite uncertainty.
