Advances in information and communication technologies, the increasing use of electronic devices and networks and the digitalisation of production processes mean that vast quantities of data are generated daily by economic and social activities. This ‘Big Data’ can be transmitted, collected, aggregated and analysed to provide insights into processes and human behaviours. Specifically, Big Data is consistently concerned about the volume (amount) of data (tons of petabytes), the velocity (speed) of data and the variability (number of types) of data. This is considered as highly valuable assets to business organisations and policy makers. Big Data has already shown profound impacts in every aspect of our lives, and it has fundamentally changed the way in which businesses compete and operate. To extract valuable information, data must be processed and analysed in a timely manner, and the results need to be readily available and easily interpreted. Typical Big Data applications include consumers’ behaviour in online environments, precautionary approach in healthcare industry, customer behaviour and preference identification in media business, performance prediction in education and Internet-of-Things (IOT) in smart urban planning. Statisticians, computer scientists, management scholars and engineers have collaborated to solve data science problems. The amount and diversity of available data requires innovative methods in data extraction, analysis, integration and visualisation.
Data Science, the Science of extracting knowledge from data, is an emerging discipline that mainly combines knowledge in Statistics, Mathematics and Computer Science. This is highly connected with empirical analyses. This involves practical applications to real-life situations that are relevant in academic areas such as business intelligence, public health, social research and supply chain management. Data Science provides potential breakthroughs using new algorithms, methodologies and Big Data analytics to uncover underlying knowledge from Big Data efficiently and effectively.
Our vision is to develop a Big Data information hub in the region.
- Undertake relevant, high-quality applied research in Big Data, Artificial Intelligence and Business Analytics,
- Address the growing demand in industry for a fuller understanding of Big Data, and
- Disseminate Big Data related knowledge to academic, practitioners and government units in Hong Kong.
The Centre is housed under School of Decision Sciences. It is managed by an Executive Committee in which the chairperson is the Centre Director appointed by the President. The Centre Director is responsible for reporting the performance of the Centre to the School Board of SDSC. Meanwhile, Prof. Tang Man Lai is the Centre Director, Dr. Ho To Sum George is the Associate Centre Director (Research & Innovation) and Dr. Wu Chun Ho Jack is the Associate Centre Director (Industry Engagement).
- Prof. Tang Man-Lai, Centre Director, Associate Dean of SDSC, Professor/Head, Department of Mathematics, Statistics and Insurance
- Dr. Ho To-Sum George, Associate Centre Director (Research & Innovation), Assistant Professor, Department of Supply Chain and Information Management
- Dr. Wu Chun-Ho Jack, Associate Centre Director (Industry Engagement), Assistant Professor, Department of Supply Chain and Information Management
- Prof. Poon Chung-Keung, Professor, Department of Computing
- Dr. Ng Chi-Hung Stephen, Associate Professor/Head, Department of Supply Chain and Information Management
- Dr. CHOY Siu-Kai, Associate Professor, Department of Mathematics, Statistics and Insurance
- Dr. Liu Hai, Associate Professor, Head of Department of Computing
- Dr. Mo Yiu-Wing Daniel, Associate Professor, Department of Supply Chain and Information Management
- Dr. Wong Siu-Kuen Ricky, Associate Professor, Department of Supply Chain and Information Management
- Dr. Lam Shu-Yan Benson, Assistant Professor, Department of Mathematics, Statistics and Insurance
Technology and Solutions
Big Data analytics have the potential to identify efficiencies in a wide variety of sectors. It can help develop innovative products and services. However, not all organisations can unleash the potential of Big Data. Business analytics use analytical and experimental methodologies to improve decision making with data support. The Centre works on projects that incorporate Big Data into everyday business practices. It covers such methodologies as business process management, predictive analysis, data mining, IOT, systems simulation and optimisation. The applications of these methodologies improve organisation’s decisions in evaluating process performance, making sound decisions and more accurate business forecasting. Organisations that adopt Big Data analytics can increase productivity by 5%-10% more than those that do not. We also understand that Big Data analytics pose a number of challenges, e.g. fraud detection, data ownership principles and the creation of a new digital divide.