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In this highly volatile and fast-paced financial market, traders and managers working in banking and financial organizations must struggle to cope with large and complex data from multi-sources, that move throughout the market at increasingly high speed. The cost of making poor business and investment decisions is very high. This places great demands on data analysts, who are responsible for providing process information, to support the activities of traders and managers. Static reports and traditional business intelligence tools simply cannot keep up with a market that is changing on a second-to-second basis. By the time the traders and bankers have read a report and try to make sense of the data, market trends have changed completely. What traders and bankers need is an effective way to monitor the performance of their orders throughout the whole organization and to visualize trade risk across their entire enterprise.

This presentation aims to show how the above data analytics challenges could be addressed using visual analytics approach. It is consists of two parts. The first part aims to identify the key misperceptions of visual analytics among the business community. The presentation will also define the main components of visual analytics. Two real world visual analytics applications will then be shown using interactive demonstration. First, we will look at stock market monitoring as an example. Using treemaps and horizon graph, the demonstration reveals interesting market trends and patterns from the stock market data.

We will then explore the potential of using Parallel Sets, a newly developed visual analytics tool, to understand customer profiles of different product segments and their inter-relationship from a retail banking perspective.


Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics


INFOCOMM Professional Development Forum

City or Country


Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.