Data visualization

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 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 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 data visualization methods [1]. It details 6 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. 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

See also

• Data Presentation Architecture
• Scientific visualization

References

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