Data visualization is the study of the visual representation of data, meaning "information which 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]
Data visualization is closely related to Information graphics, Information visualization, Scientific visualization and Statistical graphics. In the new millennium data visualization has become active area of research, teaching and development. According to Post et al (2002) it has united the field of scientific and information visualization".[3]
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[edit] 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:[4]
- Mindmaps
- Displaying news
- Displaying data
- Displaying connections
- Displaying websites
- Articles & resources
- Tools and services
All these subjects are all close 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: [3]
- Visualization algorithms and techniques
- Volume visualization
- Information visualization
- Multiresolution methods
- Modelling techniques and
- Interaction techniques and architectures
[edit] Related fields
[edit] 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.
[edit] 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 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 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..
[edit] 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
[edit] 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.
[edit] 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"[5] and "the science of extracting useful information from large data sets or databases."[6] 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".[7]
[edit] See also
- Scientific visualization
- Software
- Data Desk
- DAVIX
- Eye-Sys
- Ferret Data Visualization and Analysis
- GGobi
- IBM OpenDX
- IDL (programming language)
- Instantatlas
- OpenLink AJAX Toolkit
- ParaView
- Processing (programming language)
- Smile (software)
- StatSoft
- Visifire
- VisIt
- VTK
- Yoix
[edit] References
- ^ a b Michael Friendly (2008). "Milestones in the history of thematic cartography, statistical graphics, and data visualization".
- ^ Vitaly Friedman (2008) "Data Visualization and Infographics" in: Graphics, Monday Inspiration, January 14th, 2008.
- ^ 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..
- ^ "Data Visualization: Modern Approaches". in: Graphics, August 2nd, 2007
- ^ 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.
- ^ D. Hand, H. Mannila, P. Smyth (2001). Principles of Data Mining. MIT Press, Cambridge, MA. ISBN 0-262-08290-X.
- ^ Ellen Monk, Bret Wagner (2006). Concepts in Enterprise Resource Planning, Second Edition. Thomson Course Technology, Boston, MA. ISBN 0-619-21663-8.
[edit] 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 and Andrey Zinovyev (2007). 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.
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