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, Dr. Lam Hoi Yan Cathy is the Centre Director. Dr. Lam Shu Yan Benson and Dr. Hou Yun Aileen are the Associate Centre Directors.
- Dr. LAM Hoi Yan Cathy, Centre Director, Assistant Professor, Department of Supply Chain and Information Management
- Dr. LAM Shu Yan Benson, Associate Centre Director, Associate Professor, Department of Mathematics, Statistics and Insurance
- Dr. HOU Yun Aileen, Associate Centre Director, Assistant Professor, Department of Computing
- Prof. CHOY Siu Kai, Associate Dean (Research), School of Decision Sciences, Head and Professor, Department of Mathematics, Statistics and Insurance
- Dr. MO Yiu Wing Daniel, Associate Dean (Teaching and Learning), School of Decision Sciences, Associate Professor, Department of Supply Chain and Information Management
- Dr. LIU Hai, Associate Professor and Head, Department of Computing
- Dr. NG Chi Hung Stephen, Associate Professor and Head, Department of Supply Chain and Information Management
- Dr. HO To Sum George, Associate Professor, Associate Director of Centre for Teaching and Learning (E-learning), Department of Supply Chain and Information Management
- Dr. WU Chun Ho Jack, Associate Professor, Department of Supply Chain and Information Management
- Dr. TANG Valerie, Lecturer, Department of Supply Chain and Information Management
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.
Data mining is the process of patterns discovery of the extensive data set under the semi-automatics or automatics using the combination of statistics, database system, and machine learning. It can apply in information processing with big data and computer decision support system which opened a world of possibilities for business. The predictive result of data can enhance the organizational business strategies design, for example, predictive market research in customer behavior and personalized loyalty campaigns that improve market segmentation. The combination of data mining and big data can create a considerable classification model for further predictive analysis and make better business decisions. As the data keep actively analyzing under data mining, the pattern result can always be up-to-date and highly reliable for organizational decision making.
As a branch of Computer Science, Artificial Intelligence (AI) focuses on creating machines that exhibit human intelligence. As illustrated in spectacular achievements such as driverless car systems on public roads, AI has gained much attention in recent years. These achievements are enabled by the availability of Big Data and the advancement in computing technology. To date, AI has applications in face and speech recognition, chatbot, natural language translation, etc. These have great impacts on our society. It is believed that we are at the initial stage of this surge in AI advancement, and that applications of AI will be part of our daily lives.
Cloud computing serves as the high-powered data center on the internet for computing and data storage without direct user management. The cloud application platform can share resources and distribute over various locations and achieve economies of scale and coherence. Since big data is defined as high informational assets, big data analysis with cloud computing can be processed and remote real-time and state-of-the-art infrastructure. The benefits of reducing the cost of data analytics, enhance business continuity, and disaster recovery can improve business operation and decision making. The cloud platform enables people to utilize and analyze data cost-effectively under limited resources. The store of essential data in the cloud can also allow organizations to access relevant big data insights at anytime and anywhere.