In today’s technology-driven world, data is produced at an enormous rate, while the capacity to collect and store data is limited, the ability to analyze these data volumes increases at much lower rates. This gap leads to new challenges in the investigation process, almost all the analysts, decision-makers, engineers, or emergency response teams depend on information hidden in the data. The emerging field of visual analytics focuses on handling these massive, heterogeneous, and dynamic volumes of information by integrating human judgment utilizing visual representations and interaction techniques in the analysis process. Furthermore, it is the combination of related research areas including visualization, data mining, and statistics that turns visual analytics into a promising field of research.
Visual Analytics is basically a field of study that focuses on visual artwork such as color, line, texture, scale, Etc. in order to describe the given information that focuses on analytical reasoning facilitated by interactive visual interfaces. It uses various techniques and vision algorithms to analyze the data and interpret the results for decision making. People in diversified fields use visual analytics for data exploration and data analysis. Visual analytics makes the bulk of complex information easier and enables enterprises to understand data much more quickly and to make faster, better decisions. It is an art as well as the science of analytical reasoning supported to interface the visual data.
This can be understood from a simple example. A Thinka user might start with an initial question or task in his/her mind, find relevant data for the same, and then prepare it for its analysis.
Step 1: The cycle of visual analysis starts with a task or business questions to be answered. For example, a call center manager would ask the following questions:
· How many calls are received monthly?
· Where do the calls come from?
· What are the top call types?
· Who answers the most/least calls?
Step 2: The next step is to identify the key source of data and prioritize which ones will be most impactful by audience size. Further, the data needs to be organized and a detailed analysis of the same needs to be done.
Step 3: After getting data, it is important to explore the data by adding measures and dimensions to the view for effective visualization. At any time the creator can change the type of visualization.
Also, during analysis, he/she might realize that there is a need for additional data, so he/she might go back to get more data, and choose a new visual mapping.
Step 4: After the data is analyzed, it is important to represent the data in such that it should be able to communicate the insights in an optimal, engaging way.
This would help the creator to develop a better understanding of the initiated task and would give a better insight to find the solution to the given question.
Visual Analytics is a non-linear process that helps reveal the patterns in the given data with help of specific tools and techniques focusing to discover the unexpected, synthesize information, and further, get an impactful insight from massive data.