What is Data Visualization?
Data visualization is the creation and study of the visual representation of data. Data visualization is sometimes referred to as visual communication or descriptive statistics, and includes the techniques to present data in a visual way so as to clearly communicate information and stimulate viewer engagement and attention.
As data generation has increased, so have the ways for people to communicate, analyze, show and share these large quantities of information. The main objective of data visualization is to understand the significance of data and to communicate this information clearly and efficiently.
Analyzing and reasoning about data through visualizations makes complex data more accessible, understandable and usable. For many people, seeing analytical results presented visually makes interpretation easier and makes it easier to see patterns trends and correlations.
Some of the main reasons for using data visualization are:
- to explore sources
- to tell stories
- to predict sales volumes
- to identify areas that need attention or improvement
- to understand what factors influence customers’ behavior
- to know which products to place where
- to discover how to increase revenues or reduce expenses
- spreadsheets are hard to visualize
- patterns and trends can be spotted quickly and easily
- saves time and energy
The main advantages of communicating and analyzing information through visual means are that it is faster for people to grasp meaning, the data is easily interpreted, it is easier for decision makers to see things that were not obvious, and it is simple to share ideas.
Nowadays, advanced interactive data visualizations allow the user to not only put vast amounts of data into a pictorial or graphical format, but using computers and mobile devices allows users to drill down into charts and graphs for more details — changing what data is seen and how it is processed.
Overall, good data visualization should simplify data. It should make analytical tasks, such as making comparisons or understanding causality, providing insights into a data set, exposing and recognizing patterns or relationships easier and more effective. The best uses of data visualization will have a balance between form and function. This meaning the visual representation of data should not only be visually attractive, but also informative. Inversely, visual objects contained in graphics do not need to be boring or overly simple to convey useful information.