We are living in a world which faces a rapidly increasing amount of data to be dealt with on a daily basis. In the last decade, the steady improvement of data storage devices and means to create and collect data along the way influenced our way of dealing with information: Most of the time, data is stored without filtering and refinement for later use. Virtually every branch of industry or business, and any political or personal activity nowadays generate vast amounts of data. Making matters worse, the possibilities to collect and store data increase at a faster rate than our ability to use it for making decisions. However, in most applications, raw data has no value in itself; instead we want to extract the information contained in it.
Generally, large scale organizations have large amount of data and information to process. They need some strong procedures and techniques to collect, analyze, process and visualize the data in order to get required results as well as to take the right decision in order to get their long term goals and objectives. Several software and tools relating to big data analytics, visual analytics are being used by companies in order to manage their big data or large data sets. Are regards the big data analytics, it is the process of examining big data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions.
Although the idea of visual analytics is a broad one, this discipline can have some ambiguity. Generally, what makes visual analytics unique is that the information being visualized involves statistical work or data mining, or other types of analytics work. Visual analytics is based on the premise that visualization is used as a tool to help with analytics. For instance, visualization of data that is inherent in a natural system or gets drawn up by human hands could be described as information visualization. On the other hand, a visual interface that simply displays the results of analytics algorithms would be described as visual analytics. A visual analytics system will often use a specific software dashboard to present analytics results visually. For example, the dashboard screens might have various engines involving visual graphs, pie charts or infographics tools, where, after computational algorithms work, the results pop up on the screen.
Figure 1: Visual Data Analytics
Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces. Today, data is produced at an incredible rate and the ability to collect and store the data is increasing at a faster rate than the ability to analyze it. “People use visual analytics tools and techniques to 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; and communicate assessment effectively for action. “
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. The goal of visual analytics is the creation of tools and techniques to enable people to:
- 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.
Visual analytics is a multidisciplinary field that includes the following focus areas:
- Analytical reasoning techniques that let users obtain deep insights that directly support assessment, planning, and decision making;
- Visual representations and interaction techniques that exploit the human eye’s broad bandwidth pathway into the mind to let users see, explore, and understand large amounts of information simultaneously;
- Data representations and transformations that convert all types of conflicting and dynamic data in ways that support visualization and analysis; and
- Techniques to support production, presentation, and dissemination of analytical results to communicate information in the appropriate context to a variety of audiences.
Benefits of Visual Analytics
As we know, the visual analytics has become an essential part of business. Companies and enterprises are using data visualization technologies in order to speed up their business performance and improve their business decisions making process. So, here we discuss some key benefits of visual analytics for business:
- Visual analytics software improves the data exploration, minimizes the overall cost and improves the data analysis.
- Visual analytics (VA) make easier the bulk of complex information for better decisions.
- VA enables enterprises to understand data much more quickly and to make faster, better decisions.
- With sharp improvements in computing and data storage, it helps to businesses to solve relevant issues.
- Having the capabilities of solving large and complex issues it offers more accurate results for more profitable decisions for business.
- It offers the different trends of visualization, so the understandable data presentation modes are guaranteed.
 Paula Järvinen, Kai Puolamäki, Pekka Siltanen & Markus Ylikerälä, “Data analytics visualization applications tools”, Final Report, March 2009
 Keim, Daniel, Gennady Andrienko, Jean-Daniel Fekete, Carsten Görg, Jörn Kohlhammer, and Guy Melançon, “Visual analytics: Definition, process, and challenges”, In Information visualization, pp. 154-175. Springer, Berlin, Heidelberg, 2008.
 “Visual Analytics”, available online at: https://www.techopedia.com/definition/30371/visual-analytics
 “Visual Analytics – Key Attributes, Scope and Advantages”, available online at: http://infinitylimited.co.uk/visual-analytics-key-attributes-scope-and-advantages/