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The main goal of data visualization is its ability to visualize data, communicating information clearly and effectivelty. 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 tend to discard the balance between design and function, creating gorgeous data visualizations which fail to serve its main purpose — communicate information.<ref> [http://www.smashingmagazine.com/2008/01/14/monday-inspiration-data-visualization-and-infographics/ "Data Visualization and Infographics"] in: ''Graphics'', Monday Inspiration, January 14th, 2008.</ref>
Datavisualisation is close related to [[Information graphics]], [[Information visualization]], [[Scientific visualization]] and [[Statistical graphics]]. According to Frits Post (2003) data visualization is currently a very active and vital area of research, teaching and development. The term unites the established field of scientific visualization and the more recent field of information visualization.<ref name= "FHP02"/>
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[[Data mining]] is the process of [[sorting]] through large amounts of data and picking out relevant information. It is usually used by [[business intelligence]] organizations, and [[financial analyst]]s, but is increasingly being used in the sciences to extract information from the enormous [[data set]]s generated by modern experimental and observational methods.
It has been described as "the nontrivial extraction of implicit, previously unknown, and potentially useful [[information]] from [[data]]"<ref>{{cite journal |author=W. Frawley and G. Piatetsky-Shapiro and C. Matheus |title=Knowledge Discovery in Databases: An Overview |journal=
== See also ==
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