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]

In February 2014, University of Toronto professor Nadia Amoroso demonstrated how Data Visualization techniques increase the understanding of Big Data sets in order to communicate a story at the University of Waterloo Stratford Campus Inspiration Day.[7]

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:[8]

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.

Activities of data visualization users[edit]

Low-level user analytic activities while interacting with an instance of data visualization are presented in the following table:[9] (pro forma abstracts in fourth column are templates that capture the essence of the task[10][11])

Pro Forma
1Retrieve ValueGiven a set of specific cases, find attributes of those cases.What are the values of attributes {X, Y, Z, ...} in the data cases {A, B, C, ...}?- What is the mileage per gallon of the Audi TT?

- How long is the movie Gone with the Wind?

2FilterGiven some concrete conditions on attribute values, find data cases satisfying those conditions.Which data cases satisfy conditions {A, B, C...}?- What Kellogg's cereals have high fiber?

- What comedies have won awards?

- Which funds underperformed the SP-500?

3Compute Derived ValueGiven a set of data cases, compute an aggregate numeric representation of those data cases.What is the value of aggregation function F over a given set S of data cases?- What is the average calorie content of Post cereals?

- What is the gross income of all stores combined?

- How many manufacturers of cars are there?

4Find ExtremumFind data cases possessing an extreme value of an attribute over its range within the data set.What are the top/bottom N data cases with respect to attribute A?- What is the car with the highest MPG?

- What director/film has won the most awards?

- What Robin Williams film has the most recent release date?

5SortGiven a set of data cases, rank them according to some ordinal metric.What is the sorted order of a set S of data cases according to their value of attribute A?- Order the cars by weight.

- Rank the cereals by calories.

6Determine RangeGiven a set of data cases and an attribute of interest, find the span of values within the set.What is the range of values of attribute A in a set S of data cases?- What is the range of film lengths?

- What is the range of car horsepowers?

- What actresses are in the data set?

7Characterize DistributionGiven a set of data cases and a quantitative attribute of interest, characterize the distribution of that attribute’s values over the set.What is the distribution of values of attribute A in a set S of data cases?- What is the distribution of carbohydrates in cereals?

- What is the age distribution of shoppers?

8Find AnomaliesIdentify any anomalies within a given set of data cases with respect to a given relationship or expectation, e.g. statistical outliers.Which data cases in a set S of data cases have unexpected/exceptional values?- Are there exceptions to the relationship between horsepower and acceleration?

- Are there any outliers in protein?

9ClusterGiven a set of data cases, find clusters of similar attribute values.Which data cases in a set S of data cases are similar in value for attributes {X, Y, Z, …}?- Are there groups of cereals w/ similar fat/calories/sugar?

- Is there a cluster of typical film lengths?

10CorrelateGiven a set of data cases and two attributes, determine useful relationships between the values of those attributes.What is the correlation between attributes X and Y over a given set S of data cases?- Is there a correlation between carbohydrates and fat?

- Is there a correlation between country of origin and MPG?

- Do different genders have a preferred payment method?

- Is there a trend of increasing film length over the years?

This taxonomy of user activities can be used in two occasions: while discovering user requirements for particular data visualization project, and during evaluation of a data visualization technique. The taxonomy can also be organized by three poles of activities: retrieving values, finding data points, and arranging data points. This organization is displayed in the following diagram:

Analytic activities of data visualization users

Examples of diagrams used for data visualization[edit]



Bar chart mode 01.svgBar Chart
  • length
  • color
  • width
  • color
  • time (flow)
  • size
  • color
Gantt ChartGantt Chart
  • color
  • time (flow)
Scatter PlotScatter Plot
  • position x
  • position y
  • color

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"[12] and "the science of extracting useful information from large data sets or databases."[13] 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".[14]

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]

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:[15] "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. ^ "Inspiration Day at the University of Waterloo Stratford, Campus". Retrieved April 10, 2014. 
  8. ^ "Data Visualization: Modern Approaches". in: Graphics, August 2nd, 2007
  9. ^ Robert Amar, James Eagan, and John Stasko (2005) "Low-Level Components of Analytic Activity in Information Visualization"
  10. ^ William Newman (1994) "A Preliminary Analysis of the Products of HCI Research, Using Pro Forma Abstracts"
  11. ^ Mary Shaw (2002) "What Makes Good Research in Software Engineering?"
  12. ^ 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. 
  13. ^ D. Hand, H. Mannila, P. Smyth (2001). Principles of Data Mining. MIT Press, Cambridge, MA. ISBN 0-262-08290-X. 
  14. ^ Ellen Monk, Bret Wagner (2006). Concepts in Enterprise Resource Planning, Second Edition. Thomson Course Technology, Boston, MA. ISBN 0-619-21663-8. 
  15. ^ 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]