Data visualization

A data visualization of Wikipedia as part of the World Wide Web, demonstrating hyperlinks

Data visualization is the 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".^[1]

According to Friedman (2008) the "main goal of data visualization is to communicate information clearly and effectively through graphical means. 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 design and function, creating gorgeous data visualizations which fail to serve their main purpose — to communicate information".^[2] Indeed, Fernanda Viegas and Martin M. Wattenberg have suggested that an ideal visualization should not merely communicate clearly, but stimulate viewer engagement and attention^[3]

Data visualization is closely related to information graphics, information visualization, scientific visualization, and statistical graphics. In the new millennium, data visualization has become an active area of research, teaching, and development. According to Post et al. (2002), it has united the field of scientific and information visualization.^[4] As demonstrated by Brian Willison, data visualization has been also been linked to enhancing agile software development and customer engagement.^[5]

KPI Library has developed the “Periodic Table of Visualization Methods”, an interactive chart displaying various different data visualization methods [1]. It details 6 types of data visualization methods: data, information, concept, strategy, metaphor and compound.^{[citation needed]}

Data visualization scope

There are different approaches on the scope of data visualization. One common focus is on information presentation such as Friedman (2008) presented it. On this way Friendly (2008) presumes two main parts of data visualization: statistical graphics, and thematic cartography.^[1] In this line the "Data Visualization: Modern Approaches" (2007) article gives an overview of seven subjects of data visualization:^[6]

• Mindmaps
• Displaying news
• Displaying data
• Displaying connections
• Displaying websites
• Articles & resources
• Tools and services

All these subjects are closely related to graphic design and information representation.

On the other hand, from a computer science perspective, Frits H. Post (2002) categorized the field into a number of sub-fields:^[4]

• 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 studying 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 statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis, and inferential statistics (or 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 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 that 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 analysts, but is increasingly being used in the sciences to extract information from the enormous data sets 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"^[7] and "the science of extracting useful information from large data sets or databases."^[8] In relation to enterprise resource planning, according to Monk (2006), data mining is "the statistical and logical analysis of large sets of transaction data, looking for patterns that can aid decision making".^[9]

Data transforms

Data transforms is the process of Automation and Transformation, of both real-time and offline data from one format to another. There are standards and protocols that provide the specifications and rules, and it usually occurs in the process pipeline of aggregation or consolidation or interoperability. The primary use cases are in integration systems organizations, and compliance personnels.

Data visualization software

Software	Type	Targeted Users	License
Avizo	GUI/Code Data Visualisation	Engineers and Scientists	Proprietary
Cave5D	Virtual Reality Data Visualization	Scientists	Open Source
Data Desk	GUI Data Visualisation	Statisician	Proprietary
DAVIX	Operating System with data tools	Security Consultant	Various
Dundas Data Visualization, Inc.	GUI Data Visualisation	Business Managers	Proprietary
ELKI	Data mining visualizations	Scientists and Teachers	Open Source
Eye-Sys	GUI/Code Data Visualisation	Engineers and Scientists	Proprietary
Ferret Data Visualization and Analysis	Gridded Datasets Visualisation	Oceanographers and meteorologists	Open Source
Trendalyzer	Data Visualisation	Teachers	Proprietary
GGobi	GUI Data Visualisation	Statisician	Open Source
Grapheur	GUI Data Visualisation	Business Users	Proprietary
ggplot2	Data visualization package for R	Programmers	Open Source
Mondrian	GUI Data Visualisation	Statisician	Open Source
IBM OpenDX	GUI/Code Data Visualisation	Engineers and Scientists	Open Source
IDL (programming language)	Code Data Visualisation	Programmer	Many
IDL (programming language)	Programming Language	Programmer	Open Source
InetSoft	Company	Many	Proprietary
Instantatlas	GIS Data Visualisation	Analysts, researchers, statisticians and GIS professionals	Proprietary
MeVisLab	GUI/Code Data Visualisation	Engineers and Scientists	Proprietary
OpenLink AJAX Toolkit	Library / Toolkit	Programmers	GPL
ParaView	GUI/Code Data Visualisation	Engineers and Scientists	BSD
Processing (programming language)	Programming Language	Programmers	GPL
protovis	Library / Toolkit	Programmers	BSD
Smile (software)	GUI/Code Data Visualisation	Engineers and Scientists	Proprietary
Spotfire	GUI Data Visualisation	Business Users	Proprietary
StatSoft	Company of GUI/Code Data Visualisation Software	Engineers and Scientists	Proprietary
Tableau Software	GUI Data Visualisation	Business Users	Proprietary
TinkerPlots	GUI Data Visualisation	Students	Proprietary
Tom Sawyer Software	Data Visualization	Business Users, Engineers, and Scientists	Proprietary
Trade Space Visualizer	GUI/Code Data Visualisation	Engineers and Scientists	Proprietary
Visifire	Library	Programmers	Was Open Source, now Proprietary
Vis5D	GUI Data Visualization	Scientists	Open Source
VisAD	Java/Jython Library	Programmers	Open Source
VisIt	GUI/Code Data Visualisation	Engineers and Scientists	Open Source
VTK	C++ Library	Programmers	Open Source
Yoix	Programming Language	Programmers	Open Source

See also

• Data Presentation Architecture
• Scientific visualization

References

1. ^ ^{a b} Michael Friendly (2008). "Milestones in the history of thematic cartography, statistical graphics, and data visualization".
2. ^ Vitaly Friedman (2008) "Data Visualization and Infographics" in: Graphics, Monday Inspiration, January 14th, 2008.
3. ^ Fernanda Viegas and Martin Wattenberg, "How To Make Data Look Sexy", CNN.com, April 19, 2011. http://articles.cnn.com/2011-04-19/opinion/sexy.data_1_visualization-21st-century-engagement?_s=PM:OPINION
4. ^ ^{a b} Frits H. Post, Gregory M. Nielson and Georges-Pierre Bonneau (2002). Data Visualization: The State of the Art. Research paper TU delft, 2002..
5. ^ Brian Willison, "Visualization Driven Rapid Prototyping", Parsons Institute for Information Mapping, 2008
6. ^ "Data Visualization: Modern Approaches". in: Graphics, August 2nd, 2007
7. ^ W. Frawley and G. Piatetsky-Shapiro and C. Matheus (Fall 1992). "Knowledge Discovery in Databases: An Overview". AI Magazine: pp. 213–228. ISSN 0738-4602. 
8. ^ D. Hand, H. Mannila, P. Smyth (2001). Principles of Data Mining. MIT Press, Cambridge, MA. ISBN 0-262-08290-X. 
9. ^ Ellen Monk, Bret Wagner (2006). Concepts in Enterprise Resource Planning, Second Edition. Thomson Course Technology, Boston, MA. ISBN 0-619-21663-8. 

Further reading

• Chandrajit Bajaj, Bala Krishnamurthy (1999). Data Visualization Techniques.
• William S. Cleveland (1993). Visualizing Data. Hobart Press.
• William S. Cleveland (1994). The Elements of Graphing Data. Hobart Press.
• Alexander N. Gorban, Balázs Kégl, Donald Wunsch, and Andrei Zinovyev (2008). Principal Manifolds for Data Visualization and Dimension Reduction. LNCSE 58. Springer.
• John P. Lee and Georges G. Grinstein (eds.) (1994). Database Issues for Data Visualization: IEEE Visualization '93 Workshop, San Diego.
• Peter R. Keller and Mary Keller (1993). Visual Cues: Practical Data Visualization.
• Frits H. Post, Gregory M. Nielson and Georges-Pierre Bonneau (2002). Data Visualization: The State of the Art.
• Stewart Liff and Pamela A. Posey, Seeing is Believing: How the New Art of Visual Management Can Boost Performance Throughout Your Organization, AMACOM, New York (2007), ISBN 978-0814400357

External links

• Milestones in the History of Thematic Cartography, Statistical Graphics, and Data Visualization, An illustrated chronology of innovations by Michael Friendly and Daniel J. Denis.
• Peer-reviewed definition of Data Visualization with commentaries