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".

Overview

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".

Indeed, Fernanda Viegas and Martin M. Wattenberg have suggested that an ideal visualization should not only communicate clearly, but stimulate viewer engagement and attention.

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. Brian Willison has demonstrated that data visualization has also been linked to enhancing agile software development and customer engagement.

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.

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. In this way Friendly (2008) presumes two main parts of data visualization: statistical graphics, and thematic cartography. In this line the "Data Visualization: Modern Approaches" (2007) article gives an overview of seven subjects of data visualization:

• 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:

• Visualization algorithms and techniques
• Volume visualization
• Information visualization
• Multiresolution methods
• Modelling techniques and
• Interaction techniques and architectures

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

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" and "the science of extracting useful information from large data sets or databases." 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".

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
ANTz	Realtime 3D Data Visualization	Analysts, Scientists, Programmers, VR	Public Domain
Amira	GUI/Code Data Visualisation	Scientists	Proprietary
Avizo	GUI/Code Data Visualisation	Engineers and Scientists	Proprietary
Cave5D	Virtual Reality Data Visualization	Scientists	Open Source
curios.IT	Interactive 3D Data Visualization	Business Managers	Proprietary
Data Desk	GUI Data Visualisation	Statistician	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
FusionCharts	Component	Programmers	Proprietary
Geoscape	Geographic Data Visualisation	Business Users	Proprietary
TreeMap	GUI Data Visualisation	Business Managers	Proprietary
Trendalyzer	Data Visualisation	Teachers	Proprietary
Tulip	GUI Data Visualization	Researchers and Engineers	Open Source
Gephi	GUI Data Visualisation	Statistician	Open Source
GGobi	GUI Data Visualisation	Statistician	Open Source
Grapheur	GUI Data Visualisation	Business Users, Project Managers, Coaches	Proprietary
ggplot2	Data visualization package for R	Programmers	Open Source
Mondrian	GUI Data Visualisation	Statistician	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	GUI Data Visualization	Business Users, Developers, Academics	Proprietary
Infogr.am	Online Infographic tool	Journalists, Bloggers, Education, Business Users	Proprietary
Instantatlas	GIS Data Visualisation	Analysts, researchers, statisticians and GIS professionals	Proprietary
MeVisLab	GUI/Code Data Visualisation	Engineers and Scientists	Proprietary
Kumu	Web-Based Relationship Visualization	Social Impact, Business, Government & Policy	Proprietary
Panopticon Software	Enterprise application, SDK, Rapid Development Kit (RDK)	Capital Markets, Telecommunications, Energy, Government	Proprietary
Panorama Software	GUI Data Visualisation	Business Users	Proprietary
PanXpan	GUI Data Visualisation	Business Users	Proprietary
ParaView	GUI/Code Data Visualisation	Engineers and Scientists	BSD
Processing (programming language)	Programming Language	Programmers	GPL
ProfilePlot	GUI Data Visualisation	Engineers and Scientists	Proprietary
protovis	Library / Toolkit	Programmers	BSD
qunb	GUI Data Visualisation	Non-Expert Business Users	Proprietary
SAS Institute	GUI Data Visualisation	Business Users, Analysts	Proprietary
ScienceGL	Components / Solutions	OEM, Scientists, Engineers, Analysts	Proprietary
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
PowerPanels	GUI Data Visualisation	Business Users	Proprietary
The Hive Group: Honeycomb	GUI Data Visualisation	Energy, Financial Services, Manufacturers, Government, Military	Proprietary
The Hive Group: HiveOnDemand	GUI Data Visualisation	Business Users, Academic Users	Proprietary
TinkerPlots	GUI Data Visualisation	Students	Proprietary
Tom Sawyer Software	Data Visualization and Social Network Analysis Applications	Capital Markets, Telecommunications, Energy, Government; 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	BSD
VTK	C++ Library	Programmers	Open Source
Weave	Web-based data visualization	Many	Open Source
Yoix	Programming Language	Programmers	Open Source
Visual.ly	Company	Creative Tools: Data curation and visualization	Proprietary
Holsys One: Show the algorithms inside a data	GUI Data Visualisation	Engineers and Scientists	Proprietary

Data presentation architecture

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: "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."

Objectives

DPA has two main objectives:

• To use data to provide knowledge in the most effective manner possible (provide relevant, timely and complete data to each audience member in a clear and understandable manner that conveys important meaning, is actionable and can affect understanding, behavior and decisions)
• To use data to provide knowledge in the most efficient manner possible (minimize noise, complexity, and unnecessary data or detail given each audience's needs and roles)

Scope

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

• Defining important meaning (relevant knowledge) that is needed by each audience member in each context
• Finding the right data (subject area, historical reach, breadth, level of detail, etc.)
• Determining the required periodicity of data updates (the currency of the data)
• Determining the right timing for data presentation (when and how often the user needs to see the data)
• Utilizing appropriate analysis, grouping, visualization, and other presentation formats
• Creating effective delivery mechanisms for each audience member depending on their role, tasks, locations and access to technology

Related fields

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

• Business analysis in determining business goals, collecting requirements, mapping processes.
• Solution architecture in determining the optimal detailed solution, including the scope of data to include, given the business goals
• Business process improvement in that its goal is to improve and streamline actions and decisions in furtherance of business goals
• Statistical analysis or data analysis in that it creates information and knowledge out of data
• Data visualization in that it uses well-established theories of visualization to add or highlight meaning or importance in data presentation.
• Information architecture, but information architecture's focus is on unstructured data and therefore excludes both analysis (in the statistical/data sense) and direct transformation of the actual content (data, for DPA) into new entities and combinations.
• Graphic or user design: As the term DPA is used, it falls just short of design in that it does not consider such detail as colour palates, styling, branding and other aesthetic concerns, unless these design elements are specifically required or beneficial for communication of meaning, impact, severity or other information of business value. For example:
  • choosing to provide a specific colour in graphical elements that represent data of specific meaning or concern is part of the DPA skill-set
  • choosing locations for various data presentation elements on a presentation page (such as in a company portal, in a report or on a web page) in order to convey hierarchy, priority, importance or a rational progression for the user is part of the DPA skill-set.

See also

• Information architecture
• Information design
• Infographic
• Scientific visualization
• Software visualization
• business analysis
• business intelligence
• data analysis
• statistical analysis
• data warehouse
• data profiling
• analytics
• corporate performance management
• information architecture
• Information graphics
• Information visualization
• statistical graphics
• interaction techniques
• interaction design
• visual analytics
• balanced scorecard

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 978-0-8144-0035-7
• Stephen Few (2009) Fundamental Differences in Analytical Tools - Exploratory, Custom, or Customizable.

External links

• Yau, Nathan (2011), Visualize this, the flowing data guide to design visualization and statistics, Wiley, p. 384, ISBN 978-0470944882 
• 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
• We Love Infographics
• The data visualization academy [1] http://bigdataviz.dk/