Informing design of visual analytics systems for intelligence analysis: understanding users, user tasks, and tool usage

• Main Author: Kang, Youn Ah
• Published: Georgia Institute of Technology 2012
• Subjects: Sensemaking; Visual analytics; User studies; Intelligence service Computer network resources; Reasoning
• Online Access: http://hdl.handle.net/1853/44847

Visual analytics, defined as "the science of analytical reasoning facilitated by interactive visual interfaces," emerged several years ago as a new research field. While it has seen rapid growth for its first five years of existence, the main focus of visual analytics research has been on developing new techniques and systems rather than identifying how people conduct analysis and how visual analytics tools can help the process and the product of sensemaking. The intelligence analysis community in particular has not been fully examined in visual analytics research even though intelligence analysts are one of the major target users for which visual analytics systems are built. The lack of understanding about how analysts work and how they can benefit from visual analytics systems has created a gap between tools being developed and real world practices. This dissertation is motivated by the observation that existing models of sensemaking/intelligence analysis do not adequately characterize the analysis process and that many visual analytics tools do not truly meet user needs and are not being used effectively by intelligence analysts. I argue that visual analytics research needs to adopt successful HCI practices to better support user tasks and add utility to current work practices. As the first step, my research aims (1) to understand work processes and practices of intelligence analysts and (2) to evaluate a visual analytics system in order to identify where and how visual analytics tools can assist. By characterizing the analysis process and identifying leverage points for future visual analytics tools through empirical studies, I suggest a set of design guidelines and implications that can be used for both designing and evaluating future visual analytics systems.