Data visualization

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Data visualization or data visualisation is the creation and 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]


A data visualization from social media

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 form 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 only 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 scientific and information visualization.[4] Brian Willison has demonstrated that data visualization has 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 data visualization methods. It includes six types of data visualization methods: data, information, concept, strategy, metaphor and compound.[6]

Data visualization scope[edit]

There are different approaches on the scope of data visualization. One common focus is on information presentation, such as Friedman (2008) presented it. In 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:[7]

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]

For different types of visualizations and their connection to infographics, see infographics.

Related fields[edit]

Data acquisition[edit]

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[edit]

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:

Data governance[edit]

Data governance encompasses the people, processes and technology required to create a consistent, enterprise view of an organisation's data in order to:

Data management[edit]

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[edit]

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"[8] and "the science of extracting useful information from large data sets or databases."[9] 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".[10]

Data transforms[edit]

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[edit]

SoftwareTypeTargeted UsersLicense
AmiraGUI/Code Data VisualizationScientistsProprietary
AvizoGUI/Code Data VisualizationEngineers, ScientistsProprietary
Cave5DVirtual Reality Data VisualizationScientistsOpen Source
D3.jsLibraryProgrammersOpen Source
Data DeskGUI Data VisualizationStatisticianProprietary
DatawatchGUI Data VisualizationBusiness UsersProprietary
DAVIXOperating System with data toolsSecurity ConsultantVarious
Dundas Data Visualization, Inc.GUI Data VisualizationBusiness ManagersProprietary
ELKIData mining visualizationsScientists, TeachersOpen Source
Eye-SysGUI/Code Data VisualizationEngineers, ScientistsProprietary
Ferret Data Visualization and AnalysisGridded Datasets VisualizationOceanographers, MeteorologistsOpen Source
GephiGUI Data VisualizationStatisticiansOpen Source
GGobiGUI Data VisualizationStatisticiansOpen Source
ggplot2Data visualization package for RProgrammersOpen Source
GrapheurGUI Data VisualizationBusiness Users, Project Managers, CoachesProprietary
High-DGUI Data VisualizationEngineers, ScientistsProprietary
IBM OpenDXGUI/Code Data VisualizationEngineers, ScientistsOpen Source
IDL (programming language)Programming LanguageProgrammersOpen Source
ImproviseLibrary and GUI Data VisualizationProgrammers/designers (building), Analysts (browsing)Open Source
InfragisticsData Visualization Controls for iOS, Android, Windows Phone, .NET, jQuerySoftware Development (UI/UX), Mobile Application Developers, Web DevelopersProprietary
InstantatlasGIS Data VisualizationAnalysts, Researchers, Statisticians, GIS ProfessionalsProprietary
Keyzo IT Solutions Ltd.Data Visualization SoftwareSoftware DevelopmentProprietary
MeVisLabGUI/Code Data VisualizationEngineers, ScientistsProprietary
MindViewMind Map Graphic VisualizationBusiness Users, Project ManagersProprietary
MondrianGUI Data VisualizationStatisticianOpen Source
Panopticon SoftwareEnterprise application, SDK, Rapid Development Kit (RDK)Business UsersProprietary
Panorama SoftwareGUI Data VisualizationBusiness UsersProprietary
ParaViewGUI/Code Data VisualizationEngineers, ScientistsBSD
Processing (programming language)Programming LanguageProgrammersGPL
protovisLibrary / ToolkitProgrammersBSD
qunbGUI Data VisualizationNon-Expert Business UsersProprietary
R (programming language)Programming LanguageScientists, Researchers, Statisticians, ProgrammersGPL
SAS InstituteGUI Data VisualizationBusiness Users, AnalystsProprietary
Science of Science Tool (Sci2)GUI/Code Data Visualization, Network Analysis, Data MiningScientists, Researchers, Programmers, StudentsOpen Source
Smile (software)GUI/Code Data VisualizationEngineers, ScientistsProprietary
SpotfireGUI Data VisualizationBusiness UsersProprietary
StatSoftCompany of GUI/Code Data Visualization SoftwareEngineers, ScientistsProprietary
Tableau SoftwareGUI Data VisualizationBusiness UsersProprietary
The Hive Group: HiveOnDemandGUI Data VisualizationBusiness Users, Academic UsersProprietary
The Hive Group: HoneycombGUI Data VisualizationBusiness Users, EngineersProprietary
TinkerPlotsGUI Data VisualizationStudentsProprietary
Tom Sawyer SoftwareData Visualization and Social Network Analysis ApplicationsBusiness Users, Engineers, ScientistsProprietary
Trade Space VisualizerGUI/Code Data VisualizationEngineers, ScientistsProprietary
TreeMapGUI Data VisualizationBusiness ManagersProprietary
TrendalyzerData VisualizationTeachersProprietary
TulipGUI Data VisualizationResearchers, EngineersOpen Source
Vis5DGUI Data VisualizationScientistsOpen Source
VisADJava/Jython LibraryProgrammersOpen Source
VisifireLibraryProgrammersWas Open Source, now Proprietary
VisItGUI/Code Data VisualizationEngineers, ScientistsBSD
VTKC++ LibraryProgrammersOpen Source
YoixProgramming LanguageProgrammersOpen Source

Data presentation architecture[edit]

Data presentation architecture (DPA) is a skill-set that seeks to identify, locate, manipulate, format and present data in such a way as to optimally communicate meaning and proffer knowledge.

Historically, the term data presentation architecture is attributed to Kelly Lautt:[11] "Data Presentation Architecture (DPA) is a rarely applied skill set critical for the success and value of Business Intelligence. Data presentation architecture weds the science of numbers, data and statistics in discovering valuable information from data and making it usable, relevant and actionable with the arts of data visualization, communications, organizational psychology and change management in order to provide business intelligence solutions with the data scope, delivery timing, format and visualizations that will most effectively support and drive operational, tactical and strategic behaviour toward understood business (or organizational) goals. DPA is neither an IT nor a business skill set but exists as a separate field of expertise. Often confused with data visualization, data presentation architecture is a much broader skill set that includes determining what data on what schedule and in what exact format is to be presented, not just the best way to present data that has already been chosen (which is data visualization). Data visualization skills are one element of DPA."


DPA has two main objectives:


With the above objectives in mind, the actual work of data presentation architecture consists of:

Related fields[edit]

DPA work has some commonalities with several other fields, including:

See also[edit]


  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",, April 19, 2011.
  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. ^ Lengler, Ralph; Lengler, Ralph. "Periodic Table of Visualization Methods". Retrieved 15 March 2013. 
  7. ^ "Data Visualization: Modern Approaches". in: Graphics, August 2nd, 2007
  8. ^ 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. 
  9. ^ D. Hand, H. Mannila, P. Smyth (2001). Principles of Data Mining. MIT Press, Cambridge, MA. ISBN 0-262-08290-X. 
  10. ^ Ellen Monk, Bret Wagner (2006). Concepts in Enterprise Resource Planning, Second Edition. Thomson Course Technology, Boston, MA. ISBN 0-619-21663-8. 
  11. ^ The first formal, recorded, public usages of the term data presentation architecture were at the three formal Microsoft Office 2007 Launch events in Dec, Jan and Feb of 2007-08 in Edmonton, Calgary and Vancouver (Canada) in a presentation by Kelly Lautt describing a business intelligence system designed to improve service quality in a pulp and paper company. The term was further used and recorded in public usage on December 16, 2009 in a Microsoft Canada presentation on the value of merging Business Intelligence with corporate collaboration processes.

Further reading[edit]

External links[edit]