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{{about||data in computer science|Data (computing)|the journal|Scientific data (journal)|other uses|Data (disambiguation)|and|Datum (disambiguation)}}
{{pp-move-indef}}
[[File:Data types - en.svg|thumb|right|200px|Some of the different types of data.]]

'''Data''' ({{IPAc-en|ˈ|d|eɪ|t|ə}} {{respell|DAY|tə}}, {{IPAc-en|ˈ|d|æ|t|ə}} {{respell|DAT|ə}}, {{IPAc-en|ˈ|d|ɑː|t|ə}} {{respell|DAH|tə}})<ref>The pronunciation {{IPAc-en|ˈ|d|eɪ|t|ə}} {{respell|DAY|tə}} is widespread throughout most varieties of English. The pronunciation {{IPAc-en|ˈ|d|æ|t|ə}} {{respell|DAT|ə}} is chiefly [[Hiberno-English|Irish]] and [[American English|North American]]. The pronunciation {{IPAc-en|ˈ|d|ɑː|t|ə}} {{respell|DAH|tə}} is chiefly [[Australian English|Australian]], [[New Zealand English|New Zealand]], and [[South African English|South African]]. Each pronunciation may be realized differently depending on the dialect/language of the speaker.</ref> is a [[set (mathematics)|set]] of values of [[qualitative property|qualitative]] or [[quantitative data|quantitative]] [[variable (research)|variables]].

Data and [[information]] are often used interchangeably; however, the extent to which a set of data is informative to someone depends on the extent to which it is unexpected by that person. The amount of information content in a data stream may be characterized by its [[Shannon entropy]].

While the concept of data is commonly associated with [[scientific research]], data is collected by a huge range of organizations and institutions, including businesses (e.g., sales data, revenue, profits, [[stock price]]), governments (e.g., [[crime rate]]s, [[unemployment rate]]s, [[literacy]] rates) and non-governmental organizations (e.g., censuses of the number of [[homelessness|homeless people]] by non-profit organizations).

Data is [[measurement|measured]], [[data reporting|collected and reported]], and [[data analysis|analyzed]], whereupon it can be [[data visualization|visualized]] using graphs, images or other analysis tools. Data as a general [[concept]] refers to the fact that some existing [[information]] or [[knowledge]] is ''[[Knowledge representation and reasoning|represented]]'' or ''[[code]]d'' in some form suitable for better usage or [[data processing|processing]]. ''[[Raw data]]'' ("unprocessed data") is a collection of [[number]]s or [[character (computing)|characters]] before it has been "cleaned" and corrected by researchers. Raw data needs to be corrected to remove [[outlier]]s or obvious instrument or data entry errors (e.g., a thermometer reading from an outdoor Arctic location recording a tropical temperature). Data processing commonly occurs by stages, and the "processed data" from one stage may be considered the "raw data" of the next stage. [[Field work|Field data]] is raw data that is collected in an uncontrolled "[[in situ]]" environment. [[Experimental data]] is data that is generated within the context of a scientific investigation by observation and recording. Data has been described as the new [[Petroleum|oil]] of the [[digital economy]].<ref>[https://www.wired.com/insights/2014/07/data-new-oil-digital-economy/ Data Is the New Oil of the Digital Economy]</ref><ref>[https://web.archive.org/web/20180716224058/https://spotlessdata.com/blog/data-new-oil Data is the new Oil]</ref>

== Etymology and terminology ==
The first English use of the word "data" is from the 1640s. The word "data" was first used to mean "transmissible and storable computer information" in 1946. The expression "data processing" was first used in 1954.<ref name="eol">http://www.etymonline.com/index.php?term=data</ref>

The [[Data (word)|Latin word ''data'']] is the plural of ''datum'', "(thing) given," neuter past participle of ''dare'' "to give".<ref name="eol"/> Data may be used as a plural noun in this sense, with some writers—usually scientific writers—in the 20th century using ''datum'' in the singular and ''data'' for plural. However, in non-specialist, everyday writing, "data" is most commonly used in the singular, as a [[mass noun]] (like "information", "sand" or "rain").<ref>{{cite web|last=Hickey |first=Walt |url=http://fivethirtyeight.com/datalab/elitist-superfluous-or-popular-we-polled-americans-on-the-oxford-comma/ |title=Elitist, Superfluous, Or Popular? We Polled Americans on the Oxford Comma |publisher=FiveThirtyEight |date=2014-06-17 |accessdate=2015-05-04}}</ref>

== Meaning ==
Data, [[information]], [[knowledge]] and [[wisdom]] are closely related concepts, but each has its own role in relation to the other, and each term has its own meaning. According to a common view, data is collected and analyzed; data only becomes information suitable for making decisions once it has been analyzed in some fashion.<ref>{{cite web|title=Joint Publication 2-0, Joint Intelligence|url=http://www.jcs.mil/Portals/36/Documents/Doctrine/pubs/jp2_0.pdf|work=Joint Chiefs of Staff, Joint Doctrine Publications|publisher=Department of Defense|accessdate=July 17, 2018|pages=I-1|date=23 October 2013}}</ref> [[Knowledge]] is derived from extensive amounts of experience dealing with information on a subject. For example, the height of [[Mount Everest]] is generally considered data. The height can be recorded precisely with an [[altimeter]] and entered into a database. This data may be included in a book along with other data on Mount Everest to describe the mountain in a manner useful for those who wish to make a decision about the best method to climb it. Using an understanding based on experience climbing mountains to advise persons on the way to reach Mount Everest's peak may be seen as "knowledge". Some complement the series "data", "information" and "knowledge" with "wisdom", which would mean the status of a person in possession of a certain "knowledge" who also knows under which circumstances is good to use it.

Data is often assumed to be the least abstract concept, information the next least, and knowledge the most abstract.<ref>{{cite web|author=Akash Mitra|year=2011|title=Classifying data for successful modeling|url=https://dwbi.org/data-modelling/dimensional-model/16-classifying-data-for-successful-modeling}}</ref> In this view, data becomes information by interpretation; e.g., the height of Mount Everest is generally considered "data", a book on Mount Everest geological characteristics may be considered "information", and a climber's guidebook containing practical information on the best way to reach Mount Everest's peak may be considered "knowledge". "Information" bears a diversity of meanings that ranges from everyday usage to technical use. This view, however, has also been argued to provide an upside-down model of the relation between data, information, and knowledge.<ref>{{cite journal|last=Tuomi|first=Ilkka|date=2000|title=Data is more than knowledge|journal=Journal of Management Information Systems|volume=6|issue=3|pages=103–117|doi=10.1080/07421222.1999.11518258}}</ref> Generally speaking, the concept of information is closely related to notions of constraint, communication, control, data, form, instruction, knowledge, meaning, mental stimulus, pattern, perception, and representation. Beynon-Davies uses the concept of a [[sign]] to differentiate between data and information; data is a series of symbols, while information occurs when the symbols are used to refer to something.<ref>{{cite book|author=P. Beynon-Davies|year=2002|title=Information Systems: An introduction to informatics in organisations|publisher=[[Palgrave Macmillan]] |location=Basingstoke, UK|isbn=0-333-96390-3}}</ref><ref>{{cite book|author=P. Beynon-Davies|year=2009|title=Business information systems|publisher=Palgrave |location=Basingstoke, UK|isbn=978-0-230-20368-6}}</ref>

Before the development of computing devices and machines, only people could collect data and impose patterns on it. Since the development of computing devices and machines, these devices can also collect data. In the 2010s, computers are widely used in many fields to collect data and sort or process it, in disciplines ranging from [[marketing]], analysis of [[social services]] usage by citizens to scientific research. These patterns in data are seen as information which can be used to enhance knowledge. These patterns may be interpreted as "[[truth]]" (though "truth" can be a subjective concept), and may be authorized as aesthetic and ethical criteria in some disciplines or cultures. Events that leave behind perceivable physical or virtual remains can be traced back through data. Marks are no longer considered data once the link between the mark and observation is broken.<ref>{{cite book|author=Sharon Daniel|title=The Database: An Aesthetics of Dignity}}</ref>

Mechanical computing devices are classified according to the means by which they represent data. An [[analog computer]] represents a datum as a voltage, distance, position, or other physical quantity. A [[Computer|digital computer]] represents a piece of data as a sequence of symbols drawn from a fixed [[alphabet]]. The most common digital computers use a binary alphabet, that is, an alphabet of two characters, typically denoted "0" and "1". More familiar representations, such as numbers or letters, are then constructed from the binary alphabet. Some special forms of data are distinguished. A [[computer program]] is a collection of data, which can be interpreted as instructions. Most computer languages make a distinction between programs and the other data on which programs operate, but in some languages, notably [[Lisp (programming language)|Lisp]] and similar languages, programs are essentially indistinguishable from other data. It is also useful to distinguish [[metadata]], that is, a description of other data. A similar yet earlier term for metadata is "ancillary data." The prototypical example of metadata is the library catalog, which is a description of the contents of books.

===Data collection===

Gathering data can be accomplished through a primary source (the researcher is the first person to obtain the data) or a secondary source (the researcher obtains
the data that has already been collected by other sources, such as data disseminated in a scientific journal). Data analysis methodologies vary and include data triangulation
and data percolation.<ref>Mesly, Olivier (2015). ''Creating Models in Psychological Research.'' États-Unis : Springer Psychology : 126 pages. {{ISBN|978-3-319-15752-8}}
</ref> The latter offers an articulate method of collecting, classifying and analyzing data using five possible angles of analysis (at least three) in order to maximize
the research's objectivity and permit an understanding of the phenomena under investigation as complete as possible: qualitative and quantitative methods, literature reviews
(including scholarly articles), interviews with experts, and computer simulation. The data are thereafter "percolated" using a series of pre-determined steps so as to extract
the most relevant information.

== In other fields ==
Although data is also increasingly used in other fields, it has been suggested that the highly interpretive nature of them might be at odds with the ethos of data as "given". Peter Checkland introduced the term ''capta'' (from the Latin ''capere'', “to take”) to distinguish between an immense number of possible data and a sub-set of them, to which attention is oriented.<ref>{{cite book | author = P. Checkland and S. Holwell | title = Information, Systems, and Information Systems: Making Sense of the Field. | year = 1998 | publisher = John Wiley & Sons | location = Chichester, West Sussex | isbn = 0-471-95820-4 | pages = 86–89 }}</ref> [[Johanna Drucker]] has argued that since the humanities affirm knowledge production as "situated, partial, and constitutive," using ''data'' may introduce assumptions that are counterproductive, for example that phenomena are discrete or are observer-independent.<ref>{{cite web
|author=Johanna Drucker
|year=2011
|title=Humanities Approaches to Graphical Display
|url=http://www.digitalhumanities.org/dhq/vol/5/1/000091/000091.html
}}</ref> The term ''capta'', which emphasizes the act of observation as constitutive, is offered as an alternative to ''data'' for visual representations in the humanities.

== See also ==
{{div col|colwidth=15em}}
* [[Biological data]]
* [[Computer memory]]
* [[Data acquisition]]
* [[Data analysis]]
* [[Data cable]]
* [[Data curation]]
* [[Dark data]]
* [[Data domain]]
* [[Data element]]
* [[Data farming]]
* [[Data governance]]
* [[Data integrity]]
* [[Data maintenance]]
* [[Data management]]
* [[Data mining]]
* [[Data modeling]]
* [[Data visualization]]
* [[Computer data processing]]
* [[Data preservation]]
* [[Data publication]]
* [[Information privacy|Data protection]]
* [[Data remanence]]
* [[Data science]]
* [[Data set]]
* [[Data structure]]
* [[Data warehouse]]
* [[Database]]
* [[Datasheet]]
* [[Environmental data rescue]]
* [[Fieldwork]]
* [[Information engineering (field)|Information engineering]]
* [[Machine learning]]
* [[Open data]]
* [[Scientific data archiving]]
* [[Statistics]]
* [[Secondary Data]]

{{div col end}}

== References ==
{{FOLDOC}}
{{Reflist}}

==External links==
{{Wiktionary}}
*{{Commonscat-inline}}
*[http://purl.org/nxg/note/singular-data Data is a singular noun] (a detailed assessment)

{{Statistics}}

[[Category:Computer data|*]]
[[Category:Data| ]]
[[Category:Data management]]

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