An ideal visualization should not only communicate clearly, but stimulate viewer engagement and attention.
Data discovery is a user-driven process of searching for patterns or specific items in a data set. Data discovery applications use visual tools such as geographical maps, pivot-tables, and heat-maps to make the process of finding patterns or specific items rapid and intuitive. Data discovery may leverage statistical and data mining techniques to accomplish these goals.
Data discovery is a type of business intelligence in that they both provide the end-user with an application that visualizes data. Traditional BI covered dashboards, static and parameterized reports, and pivot tables. Visualization of data in traditional BI incorporated standard charting, KPIs, and limited graphical representation and interactivity. BI is undergoing transformation in capabilities it offers, with a focus on end-user data analysis and discovery, access to larger volumes of data and an ability to create high fidelity presentations of information.
Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects. The goal is to communicate information clearly and efficiently to users. It is one of the steps in data analysis or data science. According to Friedman (2008) the “main goal of data visualization is to communicate information clearly and effectively through graphical means. It doesn’t mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex data set by communicating its key-aspects in a more intuitive way. Yet designers often fail to achieve a balance between form and function, creating gorgeous data visualizations which fail to serve their main purpose — to communicate information.
Not limited to the communication of an information, a well-crafted data visualization is also a way to a better understanding of the data (in a data-driven research perspective), as it helps uncover trends, realize insights, explore sources, and tell stories.
Data visualization is closely related to information graphics, information visualization, scientific visualization, exploratory data analysis and statistical graphics. In the new millennium, data visualization has become an active area of research, teaching and development.
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