Visual Analysis: Beyond Social Network Analysis

Recent market dynamics continue to show an endless growth of data at a pace approaching 50% per year with the volume of digital content projected to grow to 2.7 zettabytes (ZB) by the end of 2012. With over 90% of the growth in information involving unstructured and semi-structured types of data, the demand for new types of analytic tools that help better understand hidden information in Big Data continues to increase.

As it has been noted on other posts of this blog, graph-based visual analysis is a highly effective method for capturing and understanding relationships between data that are not quantitative in nature. This method and technology has been used in diverse fields such as intelligence and law enforcement to customer sentiment and network topology analysis to uncover hidden insights in growing data that was not possible when relying only on traditional analytics.

This interest in visual analysis is due in part to the significant amount of data that are from new sources that are automated – capturing attributes such as time and geospatial locations on a routine basis. The richness of this new information creates an opportunity for understanding not only the traditional topological relationships between entities (such as social networks), but also for better understanding the behavior of networks in terms of change and pace of change, along with how specific changes are related to different types of data. As a result, we have the opportunity to better understand key trends and anomalies better than at any time in the past.

The combination of rich data collection, advanced analytics operating across both structured and unstructured data, and efficiently storing and analyzing information in quantities unimagined just a few years back, have created a new era of data analysis in general and visual analysis in particular. We can now look at the networks representing relationships between data as not just static topologies, but rather as “dynamic networks” with their own behavioral pattern in terms of change, sequence of change, and uncertainties of change, combined with the ability to integrate information from complex event processing engines and other “event driven” information sources. These new developments promise to bring about a new dawn of information use, enabling smarter, timelier decision-making in various fields of human endeavor.

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