On September 28th and 29th, "Data Driven Prescriptive Analytics" was held in National University of Singapore (Innovation 4.0). The workshop was hosted by Institute of Operations Research and Analytics and a number of well-known Professors from NUS Business School and University of Science and Technology of China (USTC) were invited to attend the event.
In the morning of September 29th, the opening ceremony of the workshop was held in the innovation 4.0, officiated by Prof Teo Chung Piaw, Executive Director of Institute of Operations Research and Analytics . At the opening , Prof Teo Chung Piaw of National University of Singapore and YU Yugang, Dean of School of Management, USTC jointly unveiled the 3rd workshop between USTC and NUS.
From the morning of 28th to evening 29th Sept , Prof. TEO Chung Piaw and Hanqin Zhang from the National University of Singapore chair the sessions which introduced the application of big data in operations research, resource allocation, fresh retail and other fields. Prof. YU Yugang and Prof. XIE Jingui from USTC introduced the application of big data in the field of logistics and optimization of medical resources distribution. Some scholars analyzed the examples of big data in the optimization of decision-making applications.
Data Driven Prescriptive Analytics
In a world where flood of data available to businesses regarding their operations these days. The need capabilities to analyze historical data, doing it right and becoming a data driven organization.
Many large organizations, especially those in traditional industry sectors such as telecommunications, financial services and manufacturing from Singapore or china companies and governments, are beginning to realize the importance of design thinking combined with big data for true customer centricity. By embedding design thinking into big data use cases, organizations can unlock new opportunities, build empathy for users and pave the way to experiences that are truly human-centered and create an emotional connection.
In this new paradigm shift across businesses, success lies in simplifying complex and using statistical methods, deterministic and stochastic optimization methods to solve them. These logics will help to utilize business data more effectively by deriving insights of trends and irregularities from data and applying them for forward-looking predictions which will help improve the user experience, business intelligence and analytics environment of their organizations. This is realized through building predictive models with appropriate analytical and design thinking techniques.