Data visualization
Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.
Overview[edit | edit source]
In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions. It is a quick, easy way to convey concepts in a universal manner.
History[edit | edit source]
The concept of using pictures to understand data has been around for centuries, from maps and graphs in the 17th century to the invention of the pie chart in the early 1800s. Today's data visualization tools go beyond the standard charts and graphs used in Microsoft Excel spreadsheets, displaying data in more sophisticated ways such as infographics, dials and gauges, geographic maps, sparklines, heat maps, and detailed bar, pie and fever charts.
Importance[edit | edit source]
Data visualization is a crucial part of any business intelligence strategy. It can also help to identify areas that need attention or improvement, clarify which factors influence customer behavior, understand which products to place where, predict sales volumes, and much more.
Types of Data Visualization[edit | edit source]
There are several types of data visualization: charts, tables, graphs, maps, infographics, and dashboards. Each of these has its own importance and can be used in certain situations to represent data in the best possible way.
Tools[edit | edit source]
There are many tools available for data visualization. Some of the popular ones include Tableau, QlikView, PowerBI, and D3.js. These tools provide an easy way to use, create, and share interactive dashboards, which contain selected visuals to represent the data.
Challenges[edit | edit source]
Despite its many benefits, data visualization also presents challenges. The most common one is dealing with too much data. It can be hard to know which data is relevant, and how to best represent it. Also, data visualization tools can be complex and require advanced training to use effectively.
See also[edit | edit source]
References[edit | edit source]
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Contributors: Prab R. Tumpati, MD
