Data visualization refers to the techniques used to communicate data or information graphically, by encoding it as visual objects (e.g., points, lines or bars) contained in graphics. The goal is to communicate information clearly and efficiently to users. It is one of the steps in spatial data analysis.
The "main goal of data visualization is to communicate information clearly and effectively through graphical means, usually as maps, graphic impressions, charts, etc. 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"
Data visualization is viewed by many disciplines as a modern equivalent of visual communication. It is not owned by any one field, but rather finds interpretation across many (e.g. it is viewed as a modern branch of descriptive statistics by some, but also as a grounded theory development tool by others). It involves the creation and study of the visual representation of data, meaning "information that has been abstracted in some schematic form, including attributes or variables for the units of information".
A primary goal of data visualization is to communicate information clearly and efficiently to users via the graphics selected, such as tables and charts. Effective visualization helps users in analyzing and reasoning about data and evidence. It makes complex data more accessible, understandable and usable.
Users may have particular analytical tasks, such as making comparisons or understanding causality, and the design principle of the graphic (i.e., showing comparisons or showing causality) follows the task. Tables are generally used where users will look-up a specific measure of a variable, while charts of various types are used to show patterns or relationships in the data for one or more variables.
Data visualization is both an art and a science. The rate at which data is generated has increased, driven by an increasingly information-based economy. Data created by internet activity and an expanding number of sensors in the environment, such as satellites and traffic cameras, are referred to as “Big Data”. Processing, analyzing and communicating this data present a variety of ethical and analytical challenges for data visualization.