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{{Short description|Visual representation of data}}
{{Redirect|Dataviz|the software company|DataViz}}
[[File:Minard.png|thumb|upright=
{{Data Visualization}}
{{InfoMaps}}
'''Data and information visualization''' ('''data viz/vis''' or '''info viz/vis''')
'''Data visualization''' is concerned with
'''Information visualization'''
[[Emerging technologies]] like [[virtual reality|virtual]], [[augmented reality|augmented]] and [[mixed reality]] have the potential to make information visualization more immersive, intuitive, interactive and easily manipulable and thus enhance the user's [[visual perception]] and [[cognition]].<ref>{{Citation |title=Visualizing Big Data with augmented and virtual reality: challenges and research agenda. |last1=Olshannikova |first1=Ekaterina |last2=Ometov |first2=Aleksandr |last3=Koucheryavy |first3=Yevgeny |last4=Ollson |first4=Thomas |journal=[[Journal of Big Data]] |volume=2 |issue=22 |year=2015 |article-number=22 |doi=10.1186/s40537-015-0031-2|doi-access=free }}</ref> In data and information visualization, the goal is to graphically present and explore abstract, non-physical and non-spatial data collected from [[database]]s, [[information system]]s, [[file system]]s, [[document]]s, [[Marketing information system|business data]],
Effective data visualization is properly sourced, contextualized, simple and uncluttered. The underlying data is accurate and up-to-date to
== Overview ==
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[[File:Internet map 1024.jpg|thumb|240px|Partial map of the Internet early 2005 represented as a graph; each line represents two [[IP addresses]], and some delay between those two nodes.]]
The field of data and information visualization has emerged "from research in [[human–computer interaction]], [[computer science]], [[graphics]], [[visual design]], [[psychology]], [[photography]] and [[business methods]]. It is increasingly applied as a critical component in scientific research, [[digital libraries]], [[data mining]], financial data analysis, market studies, manufacturing [[production control]], and [[drug discovery]]".<ref name = "BBB03">Benjamin B. Bederson and [[Ben Shneiderman]] (2003). [http://www.cs.umd.edu/hcil/pubs/books/craft.shtml ''The Craft of Information Visualization: Readings and Reflections''], Morgan Kaufmann {{ISBN|1-55860-915-6}}.</ref>
Data and information visualization presumes that "visual representations and interaction techniques take advantage of the human eye's broad bandwidth pathway into the mind to allow users to see, explore, and understand large amounts of information at once. Information visualization focused on the creation of approaches for conveying abstract information in intuitive ways."<ref>James J. Thomas and Kristin A. Cook (Ed.) (2005). [http://nvac.pnl.gov/agenda.stm ''Illuminating the Path: The R&D Agenda for Visual Analytics''] {{webarchive|url=https://web.archive.org/web/20080929155753/http://nvac.pnl.gov/agenda.stm |date=2008-09-29 }}. National Visualization and Analytics Center. p.30</ref>
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Data analysis is an indispensable part of all applied research and problem solving in industry. The most fundamental data analysis approaches are visualization (histograms, scatter plots, surface plots, tree maps, parallel coordinate plots, etc.), [[statistics]] ([[hypothesis test]], [[regression analysis|regression]], [[Principal component analysis|PCA]], etc.), [[data mining]] ([[Association rule learning|association mining]], etc.), and [[machine learning]] methods ([[cluster analysis|clustering]], [[Statistical classification|classification]], [[decision trees]], etc.). Among these approaches, information visualization, or visual data analysis, is the most reliant on the cognitive skills of human analysts, and allows the discovery of unstructured actionable insights that are limited only by human imagination and creativity. The analyst does not have to learn any sophisticated methods to be able to interpret the visualizations of the data. Information visualization is also a hypothesis generation scheme, which can be, and is typically followed by more analytical or formal analysis, such as statistical hypothesis testing.
To communicate information clearly and efficiently, data visualization uses [[statistical graphics]], [[plot (graphics)|plots]], [[Infographic|information graphics]] and other tools. Numerical data may be encoded using dots, lines, or bars, to visually communicate a quantitative message.<ref name="ReferenceA">{{cite web|url=http://www.perceptualedge.com/articles/ie/the_right_graph.pdf|title=Stephen Few-Perceptual Edge-Selecting the Right Graph for Your Message-2004|access-date=2014-09-08|archive-url=https://web.archive.org/web/20141005080924/http://www.perceptualedge.com/articles/ie/the_right_graph.pdf|archive-date=2014-10-05|url-status=live}}</ref> Effective visualization helps users analyze and reason about data and evidence.<ref>{{Cite web|url=https://www.tableau.com/learn/articles/interactive-map-and-data-visualization-examples|title = 10 Examples of Interactive Map Data Visualizations| work=Tableau }}</ref> It makes complex data more accessible, understandable, and usable, but can also be reductive.<ref>{{Cite book|url=https://www.aup.nl/en/book/9789463722902
|title=Data Visualization in Society|date=2020-04-16|publisher=Amsterdam University Press|isbn=978-90-485-4313-7|editor-last=Engebretsen|editor-first=Martin |language=en|doi=10.5117/9789463722902_ch02|editor-last2=Helen|editor-first2=Kennedy}}</ref> Users may have particular analytical tasks, such as making comparisons or understanding [[causality]], and the design principle of the graphic (i.e., showing comparisons or showing causality) follows the task. Tables are generally used where users will look up a specific measurement, while charts of various types are used to show patterns or relationships in the data for one or more variables.
Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines, or bars) contained in graphics. The goal is to communicate information clearly and efficiently to users. It is one of the steps in [[data analysis]] or [[data science]]. According to Vitaly 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".<ref>Vitaly Friedman (2008) [http://www.smashingmagazine.com/2008/01/14/monday-inspiration-data-visualization-and-infographics/ "Data Visualization and Infographics"] {{Webarchive|url=https://web.archive.org/web/20080722172600/http://www.smashingmagazine.com/2008/01/14/monday-inspiration-data-visualization-and-infographics/ |date=2008-07-22 }} in: ''Graphics'', Monday Inspiration, January 14, 2008.</ref>
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Indeed, [[Fernanda Viegas]] and [[Martin M. Wattenberg]] suggested that an ideal visualization should not only communicate clearly, but stimulate viewer engagement and attention.<ref>{{Cite news |first1= Fernanda |last1=Viegas|first2=Martin |last2=Wattenberg |title= How To Make Data Look Sexy |work= CNN |date= April 19, 2011 |url= https://edition.cnn.com/2011/OPINION/04/19/sexy.data/ |url-status= live |archive-date= May 6, 2011 |archive-url= https://web.archive.org/web/20110506065701/http://articles.cnn.com/2011-04-19/opinion/sexy.data_1_visualization-21st-century-engagement?_s=PM%3AOPINION |access-date= May 7, 2017 }}</ref>
Data visualization is closely related to [[information graphics]],
In the commercial environment data visualization is often referred to as [[Dashboard (business)|dashboards]]. [[Infographic]]s are another very common form of data visualization.
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===Characteristics of effective graphical displays===
{{quote box|width = 300px|quote=The greatest value of a picture is when it forces us to notice what we never expected to see.
|source=[[John Tukey]]<ref name="Tukey1977">{{cite book
| last = Tukey
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The [[Congressional Budget Office]] summarized several best practices for graphical displays in a June 2014 presentation. These included: a) Knowing your audience; b) Designing graphics that can stand alone outside the report's context; and c) Designing graphics that communicate the key messages in the report.<ref>{{cite web|url=https://www.cbo.gov/publication/45224|title=Telling Visual Stories About Data - Congressional Budget Office|website=www.cbo.gov|access-date=2014-11-27|archive-url=https://web.archive.org/web/20141204135630/https://www.cbo.gov/publication/45224|archive-date=2014-12-04|url-status=live}}</ref>
Useful criteria for a data or information visualization include:<ref name=IEEExplore_2007>{{cite book |last1=Kosara |first1=Robert |title=2007 11th International Conference Information Visualization (IV '07) |chapter=Visualization Criticism - The Missing Link Between Information Visualization and Art |date=16 July 2007 |pages=631–636 |doi=10.1109/IV.2007.130 |isbn=978-0-7695-2900-4 |issn=1550-6037}}</ref>
# It is based on (non-visual) data - that is, a data/info viz is not image processing and collage;
# It creates an image - specifically that the image plays the primary role in communicating meaning and is not an illustration accompanying the data in text form; and
# The result is readable.
Readability means that it is possible for a viewer to understand the underlying data, such as by making comparisons between proportionally sized visual elements to compare their respective data values; or using a legend to decode a map, like identifying coloured regions on a climate map to read temperature at that ___location. For greatest efficiency and simplicity of design and user experience, this readability is enhanced through the use of bijective mapping in that design of the image elements - where the mapping of representational element to data variable is unique.<ref name=StudiesComputIntell_2009>{{cite book |last1=Ziemkiewicz |first1=C. |last2=Kosara |first2=R. |title=Advances in Information and Intelligent Systems |chapter=Embedding Information Visualization within Visual Representation |series=Studies in Computational Intelligence |date=2009 |volume=251 |pages=307–326 |doi=10.1007/978-3-642-04141-9_15 |publisher=Springer |___location=Berlin, Heidelberg |isbn=978-3-642-04140-2 }}</ref>
Kosara (2007)<ref name=IEEExplore_2007/> also identifies the need for a visualisation to be "recognisable as a visualisation and not appear to be something else". He also states that recognisability and readability may not always be required in all types of visualisation e.g. "informative art" (which would still meet all three above criteria but might not look like a visualisation) or "artistic visualisation" (which similarly is still based on non-visual data to create an image, but may not be readable or recognisable).
===Quantitative messages===
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===Visual perception and data visualization===
[[File:Grafana dashboard for MusicBrainz Hetzner Yamaoka server screenshot.webp|thumb|Example of data visualization ([[website monitoring]] for [[MusicBrainz]] wirh [[Grafana]]).]]
A human can distinguish differences in line length, shape, orientation, distances, and color (hue) readily without significant processing effort; these are referred to as "[[Pre-attentive processing|pre-attentive attributes]]". For example, it may require significant time and effort ("attentive processing") to identify the number of times the digit "5" appears in a series of numbers; but if that digit is different in size, orientation, or color, instances of the digit can be noted quickly through pre-attentive processing.<ref name="perceptualedge.com">{{cite web|url=http://www.perceptualedge.com/articles/ie/visual_perception.pdf|title=Steven Few-Tapping the Power of Visual Perception-September 2004|access-date=2014-10-08|archive-url=https://web.archive.org/web/20141005080935/http://www.perceptualedge.com/articles/ie/visual_perception.pdf|archive-date=2014-10-05|url-status=live}}</ref>
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== History ==
{{see also|
There is no comprehensive history of data visualization. There are no accounts that span the entire development of visual thinking and visual representation of data, and which collate the contributions of disparate disciplines.<ref name="Springer-Verlag">{{cite book|last1=Friendly|first1=Michael|date=2008 |chapter=A Brief History of Data Visualization|title=Handbook of Data Visualization|pages=15–56|publisher=Springer-Verlag |doi=10.1007/978-3-540-33037-0_2|isbn=9783540330370|s2cid=62626937 }}</ref> Michael Friendly and Daniel Denis of [[York University]] are engaged in a project that attempts to provide a comprehensive history of visualization. Data visualization is not a modern development. Since prehistory, stellar data, or information such as ___location of stars were visualized on the walls of caves (such as those found in [[Lascaux|Lascaux Cave]] in Southern France) since the [[Pleistocene]] era.<ref name="WhitehouseIce00">{{cite web |url=http://news.bbc.co.uk/2/hi/science/nature/871930.stm |title=Ice Age star map discovered |author=Whitehouse, D. |work=BBC News |date=9 August 2000 |access-date=20 January 2018 |archive-url=https://web.archive.org/web/20180106064810/http://news.bbc.co.uk/2/hi/science/nature/871930.stm |archive-date=6 January 2018 |url-status=live}}</ref> Physical artefacts such as Mesopotamian [[History of ancient numeral systems#Clay token|clay tokens]] (5500 BC), Inca [[quipu]]s (2600 BC) and Marshall Islands [[Marshall Islands stick chart|stick charts]] (n.d.) can also be considered as visualizing quantitative information.<ref name="Dragicevic 2012">{{cite web|url=http://www.dataphys.org/list|title=List of Physical Visualizations and Related Artefacts |date=2012 |access-date=2018-01-12 |last1=Dragicevic |first1=Pierre |last2=Jansen |first2=Yvonne |archive-url=https://web.archive.org/web/20180113194900/http://dataphys.org/list/ |archive-date=2018-01-13 |url-status=live}}</ref><ref>{{cite journal|url=https://hal.inria.fr/hal-01120152/document |first1=Yvonne |last1=Jansen |first2=Pierre |last2=Dragicevic |first3=Petra |last3=Isenberg|author3-link= Petra Isenberg |first4=Jason |last4=Alexander |first5=Abhijit |last5=Karnik |first6=Johan |last6=Kildal |first7=Sriram |last7=Subramanian |first8=Kasper |last8=Hornbæk |author8-link=Kasper Hornbæk |date=2015 |title=Opportunities and challenges for data physicalization |journal=Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems |pages=3227–3236 |access-date=2018-01-12 |archive-url=https://web.archive.org/web/20180113093035/https://hal.inria.fr/hal-01120152/document |archive-date=2018-01-13 |url-status=live}}</ref>
The first documented data visualization can be tracked back to 1160 B.C. with the [[Turin Papyrus Map]] which accurately illustrates the distribution of geological resources and provides information about quarrying of those resources.<ref name="Friendly 2001">{{cite web|url=http://www.datavis.ca/milestones/ |title=Milestones in the history of thematic cartography, statistical graphics, and data visualization |date=2001 |last=Friendly |first=Michael |archive-url=https://web.archive.org/web/20140414221920/http://www.datavis.ca/milestones/ |archive-date=2014-04-14 |url-status=dead}}</ref> Such maps can be categorized as [[thematic map|thematic cartography]], which is a type of data visualization that presents and communicates specific data and information through a geographical illustration designed to show a particular theme connected with a specific geographic area. Earliest documented forms of data visualization were various thematic maps from different cultures and ideograms and hieroglyphs that provided and allowed interpretation of information illustrated. For example, [[Linear B]] tablets of [[Mycenae]] provided a visualization of information regarding Late Bronze Age era trades in the Mediterranean. The idea of coordinates was used by ancient Egyptian surveyors in laying out towns, earthly and heavenly positions were located by something akin to latitude and longitude at least by 200 BC, and the map projection of a spherical Earth into latitude and longitude by [[Claudius Ptolemy]] [{{circa|85}}–{{circa|165}}] in Alexandria would serve as reference standards until the 14th century.<ref name="Friendly 2001"/>
[[File:Mouvement des planètes au cours du temps.png|thumb|upright=1.2|Planetary movements]]
[[File:Playfair TimeSeries.png|thumb|upright=1.2|Playfair TimeSeries, 1786]]
[[File:50 years of datavisulization berengueres own work.png|thumb|upright=1.5|Selected milestones and inventions]]
[[File:ProductSpaceLocalization.png|thumb|upright=.7|[[The Product Space|Product Space Localization]], intended to show the [[List of countries by economic complexity|Economic Complexity]] of a given economy]]
[[File:Benin English.png|thumb|Tree map of Benin exports (2009) by product category, The Observatory of Economic Complexity]]
The invention of paper and parchment allowed further development of visualizations. One graph from the 10th or possibly 11th century is an illustration of planetary movements, used in an appendix of a textbook in monastery schools.<ref name="FUNKHOUSER">{{cite journal|last1=Funkhouser |first1=Howard Gray |title=A Note on a Tenth Century Graph |journal=Osiris |date=January 1936 |volume=1 |pages=260–262 |jstor=301609 |doi=10.1086/368425 |s2cid=144492131}}</ref> The graph apparently was meant to represent a plot of the inclinations of the planetary orbits as a function of the time. For this purpose, the zone of the zodiac was represented on a plane with a horizontal line divided into thirty parts as the time or longitudinal axis. The vertical axis designates the width of the zodiac. The horizontal scale appears to have been chosen for each planet individually for the periods cannot be reconciled. The accompanying text refers only to the amplitudes. The curves are apparently not related in time.
By the 16th century, techniques and instruments for precise observation and measurement of physical quantities, and geographic and celestial position were well-developed (for example, a "wall quadrant" constructed by [[Tycho Brahe]] [1546–1601], covering an entire wall in his observatory). Particularly important were the development of triangulation and other methods to determine mapping locations accurately.<ref name="Springer-Verlag"/> Very early, the measure of time led scholars to develop innovative way of visualizing the data (e.g. Lorenz Codomann in 1596, Johannes Temporarius in 1596<ref>{{Cite web|date=2020-12-09|title=Data visualization: definition, examples, tools, advice [guide 2020]|url=https://www.intotheminds.com/blog/en/data-visualization/|access-date=2020-12-09|website=Market research consulting|language=en-BE}}</ref>).
Mathematicians [[René Descartes]] and [[Pierre de Fermat]] developed analytic geometry and two-dimensional coordinate system which heavily influenced the practical methods of displaying and calculating values. Fermat and [[Blaise Pascal]]'s work on statistics and probability theory laid the groundwork for what we now conceptualize as data.<ref name="Springer-Verlag"/> These developments helped [[William Playfair]], who saw potential for graphical communication of quantitative data, to generate and develop [[List of graphical methods|graphical methods]] of statistics.<ref name=":0" /> In 1786, Playfair published the first presentation graphics.
In the second half of the 20th century, [[Jacques Bertin]] used quantitative graphs to represent information "intuitively, clearly, accurately, and efficiently".<ref name=":0" /> John Tukey and Edward Tufte pushed the bounds of data visualization; Tukey with his new statistical approach of exploratory data analysis and Tufte with his book "The Visual Display of Quantitative Information" paved the way for refining data visualization techniques for more than statisticians. With the progression of technology came the progression of data visualization; starting with hand-drawn visualizations and evolving into more technical applications – including interactive designs leading to [[software visualization]].<ref>{{Cite web|url=http://www.datavis.ca/papers/hbook.pdf |title=A Brief History of Data Visualization |date=2006 |access-date=2015-11-22 |website=York University |publisher=Springer-Verlag |last=Friendly |first=Michael |archive-url=https://web.archive.org/web/20160508232649/http://www.datavis.ca/papers/hbook.pdf |archive-date=2016-05-08 |url-status=live}}</ref>
The modern study of visualization started with [[computer graphics]], which "has from its beginning been used to study scientific problems. However, in its early days the lack of graphics power often limited its usefulness. The recent emphasis on visualization started in 1987 with the special issue of Computer Graphics on Visualization in ''[[Computational science|Scientific Computing]]''. Since then there have been several conferences and workshops, co-sponsored by the [[IEEE Computer Society]] and [[ACM SIGGRAPH]]".<ref>G. Scott Owen (1999). [http://www.siggraph.org/education/materials/HyperVis/visgoals/visgoal3.htm History of Visualization] {{Webarchive|url=https://web.archive.org/web/20121008032217/http://www.siggraph.org/education/materials/HyperVis/visgoals/visgoal3.htm |date=2012-10-08 }}. Accessed Jan 19, 2010.</ref> They have been devoted to the general topics of data visualization, information visualization and [[scientific visualization]], and more specific areas such as [[volume visualization]].
Programs like [[SAS (software)|SAS]], [[SOFA Statistics|SOFA]], [[R (programming language)|R]], [[Minitab]], Cornerstone and more allow for data visualization in the field of statistics. Other data visualization applications, more focused and unique to individuals, programming languages such as [[D3.js|D3]], [[Python (programming language)|Python]] (through matplotlib, seaborn) and [[JavaScript]] and Java(through JavaFX) help to make the visualization of quantitative data a possibility. Private schools have also developed programs to meet the demand for learning data visualization and associated programming libraries, including free programs like [[The Data Incubator]] or paid programs like [[General Assembly]].<ref>{{cite news
|title=NY gets new boot camp for data scientists: It's free but harder to get into than Harvard |newspaper=Venture Beat |access-date=2016-02-21 |url=https://venturebeat.com/2014/04/15/ny-gets-new-bootcamp-for-data-scientists-its-free-but-harder-to-get-into-than-harvard/ |archive-url=https://web.archive.org/web/20160215235820/http://venturebeat.com/2014/04/15/ny-gets-new-bootcamp-for-data-scientists-its-free-but-harder-to-get-into-than-harvard/ |archive-date=2016-02-15 |url-status=live}}</ref>
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*A ''graph'' is primarily used to show relationships among data and portrays values encoded as ''visual objects'' (e.g., lines, bars, or points). Numerical values are displayed within an area delineated by one or more ''axes''. These axes provide ''scales'' (quantitative and categorical) used to label and assign values to the visual objects. Many graphs are also referred to as ''charts''.<ref>{{cite web|url=http://www.perceptualedge.com/articles/ie/the_right_graph.pdf|title=Steven Few-Selecting the Right Graph for Your Message-September 2004|access-date=2014-09-08|archive-url=https://web.archive.org/web/20141005080924/http://www.perceptualedge.com/articles/ie/the_right_graph.pdf|archive-date=2014-10-05|url-status=live}}</ref>
Eppler and Lengler have 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.<ref>{{cite web|last1=Lengler|first1=Ralph|author-link1=Ralph Lengler|last2=Eppler|first2=Martin. J|author-link2=Martin J. Eppler|title=Periodic Table of Visualization Methods|url=http://www.visual-literacy.org/periodic_table/periodic_table.html|access-date=15 March 2013|publisher=www.visual-literacy.org|archive-url=https://web.archive.org/web/20130316073116/http://www.visual-literacy.org/periodic_table/periodic_table.html|archive-date=16 March 2013|url-status=live}}</ref> In "Visualization Analysis and Design" [[Tamara Munzner]] writes "Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively." Munzner
==Techniques==
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{| class=wikitable cellpadding="10"
! width="
! width="70" style="text-align:left;" | Name
! width="
! width="
|-
| [[File:Tips-day-barchart.pdf|thumb|Bar chart of tips by day of week]]
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* [[Grand Tour (data visualisation)|Grand tour]]
* [[Graph drawing]]
* [[HyperbolicTree]]
* [[Multidimensional scaling]]
* [[Parallel coordinates]]
* [[Problem solving environment]]
==Interactivity==
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<!-- This is hardly a reliable source and this list should maybe be moved to Information graphics -->
*
* [[Interaction techniques]] and architectures
* Modelling techniques
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== Data presentation architecture ==
{{unreferenced section|date=March 2022}}
[[File:Kencf0618FacebookNetwork.jpg|right|thumb|A data visualization from [[social media]]]]
'''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 knowledge. Historically, ''data presentation architecture'' is attributed to Kelly Lautt:{{efn|The first formal, recorded, public usages of the term data presentation architecture were at Microsoft Office 2007 launch events in Dec, Jan and Feb of 2007–08 in Edmonton, Calgary and Vancouver, 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 recorded in public usage on December 16, 2009 in a Microsoft Canada presentation on the value of merging Business Intelligence with corporate collaboration processes.}} "Data Presentation Architecture (DPA) is a rarely applied skill set critical for the success and value of [[Business intelligence|Business Intelligence]]. Data presentation architecture weds the science of numbers, data and statistics in [[information discovery|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. Data visualization skills are one element of DPA."
=== Objectives ===
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== See also ==
{{Div col|colwidth=
* [[Analytics]]
* [[Climate change art]]
* [[Computational visualistics]]
* [[Data management]]
* [[Data physicalization]]
* [[Data profiling]]
* [[Data warehouse]]
* [[imc FAMOS]], graphical data analysis
* [[Information management]]
* [[List of information graphics software]]
* [[List of countries by economic complexity]], example of Treemapping
* [[List of mathematical art software]]
* [[
* [[Pirouette: Turning Points in Design]]
* [[
== Notes ==
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== Further reading ==
* {{cite book|last1=Few|first1=Stephen|title=Show me the numbers: designing tables and graphs to enlighten|date=2012|edition=2|publisher=Analytics Press|isbn=9780970601971}} — Practical guide focusing on business applications of data visualisation.
* {{cite book |first=Kieran |last=Healy |title=Data Visualisation: A Practical Introduction |publisher=Princeton University Press |year=2019 |isbn=978-0-691-18161-5}} — Modern guide that balances technical skills with design principles, featuring R-based examples.
* Schwabish, Jonathan A. 2014. "[https://www.aeaweb.org/articles?id=10.1257/jep.28.1.209 An Economist's Guide to Visualizing Data]." ''Journal of Economic Perspectives'', 28 (1): 209–34. — Specialised guide for economic data visualisation with principles applicable across domains.
* {{cite book |first=Claus O. |last=Wilke |title=Fundamentals of Data Visualisation |publisher=O'Reilly |year=2018 |isbn=978-1-4920-3108-6 |url=https://serialmentor.com/dataviz/ |archive-date=2019-10-19 |access-date=2018-09-22 |archive-url=https://web.archive.org/web/20191019105107/https://serialmentor.com/dataviz/ |url-status=dead }} — Comprehensive guide focusing on principles of effective visualisation with real-world examples, available in print and open-access online resource.
* {{cite book|title=The visual display of quantitative information|last1=Tufte|first1=Edward R.|author-link1=Edward Tufte|date=2015|publisher=Graphics Press|edition=2|isbn=9780961392147}} — Classic foundational text on visualisation principles that remains influential decades after its first publication.
==External links==
{{Sister project links|auto=y|d=y}}
*[http://www.math.yorku.ca/SCS/Gallery/ Milestones in the History of Thematic Cartography, Statistical Graphics, and Data Visualization], An illustrated chronology of innovations by Michael Friendly and Daniel J. Denis.
*[http://compsci.capture.duke.edu/Panopto/Pages/Viewer.aspx?id=ee45ebd7-da62-4d27-8d16-5647aa167946 Duke University-Christa Kelleher Presentation-Communicating through infographics-visualizing scientific & engineering information
{{Visualization}}
{{Authority control}}
[[Category:Data and information visualization| ]]
[[Category:Visualization (graphics)]]
[[Category:Statistical charts and diagrams]]
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