Professor Longbing Cao, Eureka Prize Winner 2019 for Excellence in Data Science. Image: UTS
Professor Longbing Cao of the Advanced Analytics Institute is the winner of the 2019 Eureka Prize for Excellence in Data Science for developing cutting-edge theories and systems to analyse complex real-world enterprise data and behaviours that significantly helps businesses make smarter decisions.
He is highly respected for his work in both pure and applied research in areas including financial systems for payment accuracy, debt collection, prevention, prediction and intervention, which are producing significant socio-economic benefits to Australia.
At UTS, our data science research focuses on developing novel theories and actionable solutions to analyse various complexities and discover diversified intelligences for enterprises. My unique approach is bridging the gap between theoretical breakthroughs and real–world impactful business transformation.
While data science and analytics are now established research disciplines, when he joined UTS 15 years ago ‘data science’ and ‘big data’ were barely used – except by Professor Cao! He has been promoting Data Science and Analytics research, education and business engagement from his time as a chief technology officer focused on business intelligence, and then in to academia.
In 2007, he developed the first Australian Data Science and Knowledge Discovery Lab at UTS, followed in 2011 by the Advanced Analytics Institute (AAi) at UTS as the founding director. He established world-first research degrees at UTS in the Master of Analytics (Research) and PhD in Analytics; now most universities offer similar courses.
In the past decade, Prof Cao’s team has been widely engaging with industry and government to extract significant value from data sets in areas including capital markets, financial services, social warfare, taxation, immigration, banking, insurance, health and medical services, transport, and education. The Data Science Lab led by Prof Cao has invented and applied leading-edge data science and advanced analytics to address real-life, real-time, and enterprise-level decision-making challenges and business transformation opportunities in increasingly complex big data, behaviours, and business environments.
Increases in computing power and data storage capacity mean businesses are now generating and storing massive volumes of data, yet this can remain a largely untapped resource despite its vast economic and social value.
Our research outputs of transforming bigger data to smarter decision have been used to drive new-generation business intelligence, capability and productivity lifting, and business transformation.
His team has been focusing on studying data science fundamental issues, general issues in artificial intelligence, knowledge discovery and machine learning, and better practice and decision-making for enterprise data science innovation, an area that needs greater attention and investment, he says.
We are an alternative to vendors such as SAS, Deloitte and IBM which offer off the shelf solutions. We bring a level of data science thinking and innovation to our clients as we have a richer depth and width of know-how and know-why insights and innovations to develop actionable solutions to their problems to the maximum.
Reflecting on 15 years of fast-paced change, Professor Cao says that social and ethical thinking and responsibility are key areas that require greater attention.
The next generation of data science has the capacity to transform education - and the science, technology, economy, and society. On one hand, enterprise and institutions need to largely foster and enhance data science thinking and best practice for translational research, education and economy.
On the other, as we learn to understand individuals’ intent and behaviours and to make personalized predictions and interventions, we also need to consider privacy and ethics preservation, when our data is acquired, accessed, used, transformed and stored. More urgent challenge and national priority is to educate K-12 students for them to be empowered by data science thinking and knowledge as well as the mindset of preserving data ethics, privacy and security.