How Visual Analytics Enhance Human Decision-Making

Subject: Tech & Engineering
Pages: 1
Words: 732
Reading time:
3 min
Study level: Master


Visual analytics are tools for data analysis and representation that provide support for an individual’s analytical reasoning ability. Visual analytics is a complex multidisciplinary field that includes different components and techniques. Visual analytics is often connected with the data visualization process, even though visual analytics offers a more comprehensive approach to data representation and primarily focuses on improving the decision-making process. The main question of the research is to define how the application of visual analytics influences analytical processes in human decision-making. We chose this topic because we wanted to closely investigate the influence of visual analytics on analytical processes and emphasize the importance of visual analytics for better decision-making results.

Research method

Firstly, to start the investigation of the topic, the research will conduct a literature review to provide the definition of visual analytics and its key differences from visual representation. Next, in order to collect data on the influence of visual analytics on the human decision-making process, the research method will include an observation of the survey. The survey participants will choose one of two possible decisions depending on data represented in different forms. By combining the observation results with the existing body of knowledge on the stages of human decision-making, the research will define the ways in which visual analytics influence the process (Wu, 2019). Lastly, by examining common fields of visual analytics application, the research will explain how society benefits from the application of visual analytics.

It is important to analyze the visualization in scientific sectors like physics. The fact is that the methodology of this approach is the same for all areas, from information to science. At present, there is no clear consensus on the boundaries between these areas, but in a broad sense, these three areas can be distinguished as follows:

  • Scientific visualization is associated with data that has a natural structure (e.g., MRI data, wind currents).
  • Visual analytics, in particular, is concerned with pairing interactive visual representations with sub-analytical processes so that high-level, complex activities (reasoning, decision-making) can be efficiently performed.

In order to more objectively and understandably consider the principle of visual analytics, it is necessary to give an example of physics, where the described method is most common. The main purpose of this in physics research is to illustrate processes in such a way that the researcher can see them (Wu, 2019). Visualization of nanostructures is an example of such a transformation. Turning numbers and formulas into an animated picture is one of the best ways to help scientists understand the underlying processes in the objects they study. Providing the ability to change input data (for example, conditions or the number of original objects) gives the analyst the ability to solve their problems without showing the numerical results of the calculations and without wasting time understanding what went wrong. All the necessary information and processes are visible, and the calculations are performed in the background. The collision of two fullerenes is a good example of such a problem. The created software had the ability to change the angles and speed of collision of fullerenes. The analyst can change the parameters to get the image he wants to get (Wu, 2019). Another extremely important visualization object is the various fields. In all cases, the main question was about regions with low or high values, as well as finding equivalent zones. In these studies, individuals used both shapes and optical attributes to show the meanings and directions of the fields. Analysis of the parameter field of a superconductor is a good example of such visualization. The superconductor is modeled in accordance with the Ginzburg-Landau theory (Wu, 2019). The developed software shows the current flows, and the color depends on the field values. This application allowed physicists to see and understand the directions and values of the field.

Expected research outcome

The desired results of the research will illustrate the influence of visual analytics for better decision-making outcomes and provide the foundation for further development of visual analytics and its application in new fields. The expected outcome needs to specifically identify the stage of the decision-making process influenced by the application of visual analytics. Lastly, to demonstrate and support results and conclusions, the research will explain how society benefits from visual analytics.


Wu, Y. (2019). Neutronics of advanced nuclear systems. Springer Singapore.