Stories that Data Tells: Rich Insights from Sparse Data
Dr Shonali Krishnaswamy, Director and Senior Scientist, Institute for Infocomm Research, Singapore
This talk will focus on three real-world cases studies/demonstrations that show the opportunities for drawing rich insights from “not so” rich data.
The first case study will be on the topic of Predictive Audit where we show how Data Analytics has led to transformational impact in a large financial institution by enabling Audit to preventive/pre-emptive of future risks and irregularities, rather than reactive/responsive to past risks and irregularities.
The second case study will focus on “Tell Tale Trajectories”— showing how “not so” rich and sometimes rather sparse data such as public transport cards and mobile cell tower connections can be leveraged to draw deep insights about users, their preferences, life-styles and behavior.
The third case study will focus on predicting machine faults and failures and show how to overcome the challenges of making accurate predictions when there are very few relevant data points/exemplars to learn from.
The talk will also briefly examine the emerging world of big data partnerships in sectors such as finance, insurance, telco, and health/wellness, and discuss future needs for data analytics to be truly scalable and overcome the current bottlenecks that face the application of data science in industry.
Dr Shonali Krishnaswamy is a Director and Senior Scientist at the Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR) in Singapore.
She currently directs and leads a number of analytics-focused R&D labs with strategic industry partners including DBS, Standard Chartered Bank and AIA. She is also focused on developing “Data Partnerships” to determine the value of “Outside-In” data to organisations and to enable data monetisation.
Prior to this, Shonali was the Head of Data Analytics at the Institute for Infocomm Research, Singapore and Associate Professor in the Faculty of Information Technology, Monash University, Australia.
Her research interests are in the areas of Mobile, Ubiquitous, Distributed Data Mining. She is increasingly interested in the intersection of Machine Learning, Parallel and Distributed Computing, and Artificial Intelligence.