Data can be seen as the smallest units of factual information that can be used as a basis for calculation, reasoning, or discussion. Data can range from abstract ideas to concrete measurements, including, but not limited to,statistics. Thematically connected data presented in some relevant context can be viewed asinformation. Contextually connected pieces of information can then be described asdata insights orintelligence. The stock of insights and intelligence that accumulate over time resulting from the synthesis of data into information, can then be described asknowledge. Data has been described as "the newoil of thedigital economy".[4][5] Data, as a general concept, refers to the fact that some existinginformation orknowledge isrepresented orcoded in some form suitable for better usage orprocessing.
Advances in computing technologies have led to the advent ofbig data, which usually refers to very large quantities of data, usually at the petabyte scale. Using traditional data analysis methods and computing, working with such large (and growing) datasets is difficult, even impossible. (Theoretically speaking, infinite data would yield infinite information, which would render extracting insights or intelligence impossible.) In response, the relatively new field ofdata science usesmachine learning (and otherartificial intelligence) methods that allow for efficient applications of analytic methods to big data.
TheLatin worddata is the plural ofdatum, "(thing) given," and the neuter past participle ofdare, "to give".[6] 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.[6]
When "data" is used more generally as a synonym for "information", it is treated as amass noun in singular form. This usage is common ineveryday language and in technical and scientific fields such assoftware development andcomputer science. One example of this usage is the term "big data". When used more specifically to refer to the processing and analysis of sets of data, the term retains its plural form. This usage is common in the natural sciences, life sciences, social sciences, software development and computer science, and grew in popularity in the 20th and 21st centuries. Some style guides do not recognize the different meanings of the term and simply recommend the form that best suits the target audience of the guide. For example,APA style as of the 7th edition requires "data" to be treated as a plural form.[7]
Data,information,knowledge, andwisdom are closely related concepts, but each has its role concerning the other, and each term has its 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.[8] One can say that 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 contained in a data stream may be characterized by itsShannon entropy.
Knowledge is the awareness of its environment that some entity possesses, whereas data merely communicates that knowledge. For example, the entry in a database specifying the height ofMount Everest is a datum that communicates a precisely measured value. This measurement 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 decide on the best method to climb it. Awareness of the characteristics represented by this data is knowledge.
Data are often assumed to be the least abstract concept, information the next least, and knowledge the most abstract.[9] 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 range from everyday usage to technical use. This view, however, has also been argued to reverse how data emerges from information, and information from knowledge.[10] 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 asign to differentiate between data and information; data is a series of symbols, while information occurs when the symbols are used to refer to something.[11][12]
Before the development of computing devices and machines, people had to manually collect data and impose patterns on it. With the development of computing devices and machines, these devices can also collect data. In the 2010s, computers were widely used in many fields to collect data and sort or process it, in disciplines ranging frommarketing, analysis ofsocial service usage by citizens to scientific research. These patterns in the data are seen as information that 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.[13]
Mechanical computing devices are classified according to how they represent data. Ananalog computer represents a datum as a voltage, distance, position, or other physical quantity. Adigital computer represents a piece of data as a sequence of symbols drawn from a fixedalphabet. 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. Acomputer program is a collection of data, that 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, notablyLisp and similar languages, programs are essentially indistinguishable from other data. It is also useful to distinguishmetadata, 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.
With respect to ownership of data collected in the course of marketing or other corporate collection, data has been characterized according to "party" depending on how close the data is to the source or if it has been generated through additional processing. "Zero-party data" refers to data that customers "intentionally and proactively shares".[14] This kind of data can come from a variety of sources, including: subscriptions, preference centers, quizzes, surveys, pop-up forms, and interactive digital experiences.[15] "First-party data" may be collected by a company directly from its customers.[16] The secure exchange of first-party data among companies can be done usingdata clean rooms.[17] "Second-party data" refers to data obtained from other organizations or partners, through purchase or other means and has been described as "another organization's first-party data".[18][19] "Third-party data" is data collected by other organizations and subsequently aggregated from different sources, websites, and platforms.[18]
Some of these data documents (data repositories, data studies, data sets, and software) are indexed inData Citation Indexes, while data papers are indexed in traditional bibliographic databases, e.g.,Science Citation Index.
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.[20] The latter offers an articulate method of collecting, classifying, and analyzing data using five possible angles of analysis (at least three) 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 is thereafter "percolated" using a series of pre-determined steps so as to extract the most relevant information.
An important field incomputer science, technology, andlibrary science is the longevity of data. Scientific research generates huge amounts of data, especially ingenomics andastronomy, but also in themedical sciences, e.g. inmedical imaging. In the past, scientific data has been published inpapers and books, stored in libraries, but more recently practically all data is stored onhard drives oroptical discs. However, in contrast to paper, these storage devices may become unreadable after a few decades. Scientific publishers and libraries have been struggling with this problem for a few decades, and there is still no satisfactory solution for the long-term storage of data over centuries or even for eternity.
Data accessibility. Another problem is that much scientific data is never published or deposited in data repositories such asdatabases. In a recent survey, data was requested from 516 studies that were published between 2 and 22 years earlier, but less than one out of five of these studies were able or willing to provide the requested data. Overall, the likelihood of retrieving data dropped by 17% each year after publication.[21] Similarly, a survey of 100 datasets inDryad found that more than half lacked the details to reproduce the research results from these studies.[22] This shows the dire situation of access to scientific data that is not published or does not have enough details to be reproduced.
A solution to the problem of reproducibility is the attempt to requireFAIR data, that is, data that is Findable, Accessible, Interoperable, and Reusable. Data that fulfills these requirements can be used in subsequent research and thus advances science and technology.[23]
Although data is also increasingly used in other fields, it has been suggested that their highly interpretive nature might be at odds with the ethos of data as "given".Peter Checkland introduced the termcapta (from the Latincapere, "to take") to distinguish between an immense number of possible data and a sub-set of them, to which attention is oriented.[24]Johanna Drucker has argued that since the humanities affirm knowledge production as "situated, partial, and constitutive," usingdata may introduce assumptions that are counterproductive, for example, that phenomena are discrete or are observer-independent.[25] The termcapta, which emphasizes the act of observation as constitutive, is offered as an alternative todata for visual representations in the humanities.
^American Psychological Association (2020). "6.11".Publication Manual of the American Psychological Association: the official guide to APA style. American Psychological Association.ISBN978-1-4338-3216-1.
^P. Checkland and S. Holwell (1998).Information, Systems, and Information Systems: Making Sense of the Field. Chichester, West Sussex: John Wiley & Sons. pp. 86–89.ISBN0-471-95820-4.
