What is visual analytics? What are its application areas? How to choose a suitable software solution for analysis of business data? These are only some of the questions that will be answered in the following articles providing an extensive overview of the field of visual analytics. This blog series is based on literature review and recent research. Let’s get started.
Background
There is a steady growth in the creation and consumption of data. Practically every industry or business generates and deals with data on a daily basis. With the rapid advance of technology, it is now easier and cheaper than ever to collect and keep this data on hard drives and cloud storages. It is often stored unfiltered and unrefined and in such a raw form the data adds little to no value in most cases. Therefore, it is important to extract the information contained in it. The problem is, however, that the quantity of data grows at a faster rate than the humans’ ability to process it and use it for making decisions.[1] As the amount, velocity, and complexity of data continuously increase, it becomes more and more difficult to use it effectively. Standard tools for data analysis are no longer able to meet the new requirements[2] and this leads to a growing need of developing solutions with improved performance.[3] As a result of the challenge that the processing and comprehension of big data represents, the interest in visual analytics has increased in the last years and it has been identified as an effective way to extract and understand the information hidden in large-scale datasets.[4] This emerging field of study aims at turning the excessive load of information into an opportunity to improve the decision-making process and quickly gain insight by combining the strengths of human intelligence and computer data processing while using visualization as a medium.[1]
Definition
After the September 11 attacks , the US government focused its efforts on evaluating existing technologies for protecting the country against terrorism and responding to attacks.[5] In 2004 the US Department of Homeland Security entrusted the National Visualization and Analytics Center (NVAC) with the responsibility to develop new technologies for protection. In the research and development agenda published by NVAC, which was concentrated on preventing terrorist attacks, protecting borders, and improving emergency response,[6] the term visual analytics was coined.[7] Originally, visual analytics was defined as “the science of analytical reasoning facilitated by interactive visual interfaces”.[8] In the recent years, the application fields of visual analytics have been extended and it is no longer solely focused on homeland security. Furthermore, it is described as a multidisciplinary field[7] and based on the ongoing development, a more suited definition emerged.[1]
Visual analytics “combines automated analysis techniques with interactive visualizations for an effective understanding, reasoning and decision making on the basis of very large and complex data sets”.
This definition underlines the interdisciplinary nature of the field and better reflects its current state.
Goals
There are four main goals of visual analytics. Following the definition, it can be stated that visual analytics is the creation of tools and techniques to enable people to:[9]
- Synthesize information and derive insight from massive, dynamic, ambiguous, and often conflicting data.
- Detect the expected and discover the unexpected.
- Provide timely, defensible, and understandable assessments.
- Communicate assessment effectively for action.
In order for these goals to be successfully accomplished, a process that allows the generation of knowledge from data is required. This process will be discussed in the third article of this blog series.
Visual analytics versus information visualization
While the terms visual analytics and information visualization are highly connected, they do not have the same meaning. The purpose of information visualization is to represent data and provide the user with the possibility to explore it.[10] Furthermore, information visualization does not have to deal with tasks related to data analysis. Additionally, most of the research in this field is focused on creating views and interaction techniques for data and little effort is made on how to turn the interaction with data into intelligence.[1]
On the other hand, visual analytics does more than simply representing data. It combines visualization, automated data analysis, and human factors.[7] Visual analytics provides tools for analysis and visualization and allows users to make sense of large amounts of data.[11] Compared to information visualization, visual analytics gives much higher priority to the analysis of data and this can be outlined as the most considerable difference between both fields.[1] Last but not least, information visualization is a product whereas visual analytics is a process.[11]
In addition to information visualization, there are other disciplines related to the field of visual analytics. You will find out more in the next article.
[1] Keim, D., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J. and Melancon, G. (2008) ‘Visual Analytics: Definition, Process, and Challenges’, in Kerren, A., Stasko, J.T., Fekete, J.-D., and North, C. (eds.) Information Visualization. Berlin, Heidelberg: Springer-Verlag, pp. 154–175.
[2] Lemieux, V.L., Gormly, B. and Rowledge, L. (2014) ‘Meeting Big Data challenges with visual analytics’, Records Management Journal, 24(2), pp. 122–141.
[3] Zhang, L., Stoffel, A., Behrisch, M., Mittelstadt, S., Schreck, T., Pompl, R., Weber, S., Last, H. and Keim, D. (2012) ‘Visual Analytics for the Big Data Era — A Comparative Review of State-of-the-Art Commercial Systems’, 2012 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 173–182.
[4] Choo, J. and Park, H. (2013) ‘Customizing Computational Methods for Visual Analytics with Big Data’, IEEE Computer Graphics and Applications, 33(4), pp. 22–28.
[5] Thomas, J.J. and Cook, K.A. (2006) ‘A Visual Analytics Agenda’, IEEE Computer Graphics and Applications, 26(1), pp. 10–13.
[6] Robertson, G., Ebert, D., Eick, S., Keim, D. and Joy, K. (2009) ‘Scale and complexity in visual analytics’, Information Visualization, 8(4), pp. 247–253.
[7] Keim, D., Mansmann, F., Stoffel, A. and Ziegler, H. (2009) ‘Visual Analytics’, in Özsu, M.T. and Liu, L. (eds.) Encyclopedia of Database Systems. New York: Springer US, pp. 3341–3346.
[8] Thomas, J.J. and Cook, K.A. (2005) Illuminating the Path: The Research and Development Agenda for Visual Analytics. Los Alamitos, CA: IEEE Computer Society.
[9] Keim, D., Kohlhammer, J., Ellis, G. and Mansmann, F. (2010) Mastering the Information Age: Solving Problems with Visual Analytics. Goslar: Eurographics Association.
[10] Shrinivasan, Y.B. (2010) Supporting the Sensemaking Process in Visual Analytics. Eindhoven.
[11] Lemieux, V.L., Gormly, B. and Rowledge, L. (2014) ‘Meeting Big Data challenges with visual analytics’, Records Management Journal, 24(2), pp. 122–141.