What is Interactive Visual Analytics?
Humans are born to see patterns. From infancy we learn to make sense of the patterns visible in our world. As children, we begin to understand the structures and connections of natural things and artificial objects. We learn to explore, play and solve problems with what we see.
Data, big or small, remains a mystery unless we can find patterns in it. The easier it is to see the structures and connections in data, the easier it is to understand its meaning, its story.
Interactive visual analytics (IVA) provides us with a means to see the structures, patterns and connections in our data, whether currently known or yet to be discovered.
Visual analytics was initially proposed as a means to help US intelligence analysts meet the challenge of dealing with the masses of security-related information made available to them following the terrorist attacks on September 11, 2001, on the Pentagon in Washington and the World Trade Center in New York. They literally were lost in a data deluge.
Visual analytics was proposed as a possible solution for these anayts and was defined as “the science of analytical reasoning facilitated by interactive visual interfaces.”
Interactive visual analytics techniques and technologies support analysis, sense-making and decision-making activities by human analysts by providing the following capabilities:
Figure 1. Interactive visual analytics core processes: Data acquisition, visual data analysis and dissemination of results.
IVA is a multidisciplinary field intended to help people understand how to synthesize information from large amounts of data. It draws upon research in a number of areas, including visualization, human computer interaction, machine learning, statistics, and cognitive science. 
Figure 2. Core and associated disciplines within the scope of Interactive Visual Analytics.
Interactive visual analytics proposes to take advantage of the visual intelligence and cognitive capabilities of human analysts using interactive, exploratory, data visualisation tools. These tools will also include statistical and machine learning capabilities that would be guided by the analyst.
Although interactive visual analytics originally was intended to solve data analysis problems in the security and intelligence domains, the tools and techniques that have been developed in the past decade are also of great interest to analysts in many other data-rich domains, e.g., aerospace safety, manufacturing and maintenance, transportation, financial risk analysis and fraud detection, business process analysis, social media analysis, health care and medical research, and environmental health and safety.
Organizations everywhere are encountering a significant challenge: How to derive more value from datasets that continue to increase in complexity and size, ie., Big Data. Traditional analytic approaches are often inadequate to cope with such data sets; important—and sometimes critical—information often remains undiscovered. New methods are consequently needed to enable analysts to handle data on such a large scale and with such complexity, to allow confirmation of expected and discovery of unexpected findings. Many organizations, researchers and analysts have identified VA as a promising approach to their critical data analytic needs.
Raw data has little intrinsic value
Data mining can help find expected patterns, e.g., look for gold and find the gold in the data.
Humans have some very impressive visual and cognitive capabilities, but humans change very slowly, e.g., brain volume has only doubled in approximately 2.5 x 106 years. Technology, however, has been changing very quickly, e.g., Moore’s law shows integrated circuit capacity doubles in approximately 2 years. Consequently, sophisticated VA tools are being developed to help human analysts view and explore their data.
Figure 3. Performance of a multi-initiative Interactive Visual Analytics System is a function of the Analyst and his/her Environment, including other people/analysts and machine agents, and changes over time.
 Thomas, J.J. & Cook, K.A. (Eds.). Illuminating the path: The research and development agenda for visual analytics. IEEE Computer Society, 2005. (See pdf at NVAC)S
 Keim, D.A. & Mansmann, F. & Schneidewind, J. & Ziegler, H., Challenges in Visual Data Analysis, Proceedings of Information Visualization (IV 2006), IEEE, p. 9-16, 2006.