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[[Image:Data visualisation.jpg|thumb|360px|The research process from [[data]] to [[visualization]].<ref>[http://nvac.pnl.gov/research.stm National Visualization and Analytics Center]. Retrieved 1 Juli 2008.</ref>]]'''Data visualization''' is the study of the visual representation of [[data]], defined as information which has been abstracted in some schematic form, including attributes or variables for the units of information.<ref name = "MF08"> [[Michael Friendly]] (2008). [http://www.math.yorku.ca/SCS/Gallery/milestone/milestone.pdf "Milestones in the history of thematic cartography, statistical graphics, and data visualization"].</ref>
== Overview ==
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]]
* [[Statistical graphics]]
== History ==
Since the 1980s data visualization is an evolving concept whose boundaries are continually expending and, as such, is best defined in terms of loose generalizations. It referes to the more technologically advanced techniques, which allow visual interpretation of data through the representation, modelling and display of solids, surfaces, properties and animations, involving the use of graphics, image processing, computer vision and user interfaces. It encompasses a much broader range of techniques then specific techniques as solid modelling.<ref>Paul Reilly, S. P. Q. Rahtz (eds.) 1992. ''Archaeology and the Information Age: A Global Perspective''. p.92.</ref>
== Data visualization subjects ==
According to Michael Friendly (2008) two main parts of data visualization are:<ref name = "MF08"/>
* [[statistical graphics]], and
* [[Thematic map|thematic cartography]].
The "Data Visualization: Modern Approaches" (2007) article gives an overview of seven subjects of data visualisation:<ref>[http://www.smashingmagazine.com/2007/08/02/data-visualization-modern-approaches/ "Data Visualization: Modern Approaches"]. in: ''Graphics'', August 2nd, 2007 </ref>
* [[Mindmap]]s
* Displaying [[News]]
* Displaying [[Data]]
* Displaying [[connections]]
* Displaying [[website]]s
* [[Articles]] & [[Resources]]
* Tools and Services
These subjects are all close related to [[graphic design]].
<!-- This is hardly a reliable source and this list should maybe be moved to Information graphics -->
Frits H. Post (2002) gives an quiet other overview of data visualization subjects. He listed:<ref> Frits H. Post, Gregory M. Nielson and Georges-Pierre Bonneau (2002). [http://visualisation.tudelft.nl/publications/post2003b.pdf ''Data Visualization: The State of the Art''].</ref>
* Visualization Algorithms and Techniques
* Volume Visualization
* Information Visualization
* Multiresolution Methods
* Modelling Techniques and
* Interaction Techniques and Architectures
== Related fields ==
=== Data acquisition ===
[[Data acquisition]] is the sampling of the real world to generate data that can be manipulated by a computer. Sometimes abbreviated '''DAQ''' or '''DAS''', data acquisition typically involves acquisition of signals and waveforms and processing the signals to obtain desired information. The components of data acquisition systems include appropriate sensors that convert any measurement parameter to an electrical signal, which is acquired by data acquisition hardware.
=== Data analysis ===
[[Data analysis]] is the process of looking at and summarizing [[data]] with the intent to extract useful [[information]] and develop conclusions. Data analysis is closely related to [[data mining]], but data mining tends to focus on larger data sets, with less emphasis on making [[inference]], and often uses data that was originally collected for a different purpose. In [[statistics|statistical applications]], some people divide data analysis into [[descriptive statistics]], [[exploratory data analysis]] and [[confirmatory data analysis]], where the EDA focuses on discovering new features in the data, and CDA on confirming or falsifying existing hypotheses.
Types of data analysis are:
* [[Exploratory data analysis]] (EDA): an approach to analyzing data for the purpose of formulating [[hypothesis|hypotheses]] worth testing, complementing the tools of conventional [[statistics]] for testing hypotheses. It was so named by [[John Tukey]].
* [[Qualitative data analysis]] (QDA) or [[qualitative research]] is the analysis of non-numerical data, for example words, photographs, observations, etc..
=== Data governance ===
[[Data governance]] encompasses the people, processes and technology required to create a consistent, enterprise view of an organisation's data in order to:
* Increase consistency & confidence in decision making
* Decrease the risk of regulatory fines
* Improve data security
* Maximize the income generation potential of data
* Designate accountability for information quality
=== Data management ===
[[Data management]] comprises all the academic disciplines related to managing [[data]] as a valuable resource. The official definition provided by [[DAMA]] is that "Data Resource Management is the development and execution of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise." This definition is fairly broad and encompasses a number of professions which may not have direct technical contact with lower-level aspects of data management, such as [[relational database]] management.
=== Data mining ===
[[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=[[AI Magazine]] |date=Fall 1992 |pages=pp. 213–228 |id={{ISSN|0738-4602}}}}</ref> and "the science of extracting useful information from large [[data set]]s or [[database]]s."<ref>{{cite book |author=D. Hand, H. Mannila, P. Smyth |title=Principles of Data Mining |publisher=MIT Press, Cambridge, MA |year=2001 |id=ISBN 0-262-08290-X}}</ref> Data mining in relation to [[enterprise resource planning]] is the statistical and logical analysis of large sets of transaction data, looking for patterns that can aid decision making.<ref> {{cite book |author=Ellen Monk, Bret Wagner |title=Concepts in Enterprise Resource Planning, Second Edition |publisher=Thomson Course Technology, Boston, MA |year=2006 |id=ISBN 0-619-21663-8}}</ref>
== See also ==
<!-- The first 13 items listed here are Wikipedia articles that mention the term data visualization.-->
Organizations
*[[Interactive Data Visualization, Inc. ]]
*[[Dundas Data Visualization, Inc.]]
*[[National Oceanographic Data Center]]
Software programs/ visualization applications/graphics toolkit
*[[Eye-Sys ]]
*[[Ferret Data Visualization and Analysis]]
*[[GGobi]]
*[[IBM OpenDX]]
*[[OpenLink AJAX Toolkit]]
*[[ParaView]]
*[[Smile (software)]]
*[[SpeedTree ]]
*[[StatSoft]]
*[[VTK]]
== References ==
{{reflist}}
== Further reading ==
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* Chandrajit Bajaj, Bala Krishnamurthy (1999). [http://www.amazon.com/Data-Visualization-Techniques-Trends-Software/dp/0471963569 ''Data Visualization Techniques''].
* William S. Cleveland (1993). [http://www.amazon.com/Visualizing-Data-William-S-Cleveland/dp/0963488406 ''Visualizing Data'']. Hobart Press.
* William S. Cleveland (1994). [http://www.amazon.com/Elements-Graphing-Data-William-Cleveland/dp/0963488414 ''The Elements of Graphing Data'']. Hobart Press.
* Alexander N. Gorban, Balázs Kégl and Andrey Zinovyev (2007). [http://www.amazon.com/Principal-Manifolds-Visualization-Computational-Engineering/dp/3540737499 ''Principal Manifolds for Data Visualization and Dimension Reduction''.
* John P. Lee and Georges G. Grinstein (eds.) (1994). [http://portal.acm.org/toc.cfm?id=646122&type=proceeding&coll=GUIDE&dl=GUIDE&CFID=35087769&CFTOKEN=59542343 ''Database Issues for Data Visualization: IEEE Visualization '93 Workshop, San Diego''].
* Peter R. Keller and Mary Keller (1993). [http://www.amazon.com/Visual-Cues-Practical-Data-Visualization/dp/0818631023 ''Visual Cues: Practical Data Visualization''].
* Frits H. Post, Gregory M. Nielson and Georges-Pierre Bonneau (2002). [http://visualisation.tudelft.nl/publications/post2003b.pdf ''Data Visualization: The State of the Art''].
==External links==
{{Commonscat|Data visualization}}
* [http://www.math.yorku.ca/SCS/Gallery/milestone/ Reader "Milestones in the History of Thematic Cartography, Statistical Graphics and Data Visualization"]
* [http://prefuse.org/ Prefuse] is a set of software tools for creating rich interactive data visualizations.
{{Visualization}}
[[Category:Visualization (graphic)]]
[[Category:Data analysis]]
[[Category:Data collection]]
[[Category:Data management]]
[[Category:Data mining]]
[[nl:Datavisualisatie]]
[[Category:Information technology governance]]
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