This usage is common in natural sciences, life sciences, social sciences, software development and computer science, and grew in popularity in the 20th and 21st centuries. When used more specifically to refer to the processing and analysis of sets of data, the term retains its plural form. One example of this usage is the term " big data". This usage is common in everyday language and in technical and scientific fields such as software development and computer science. When "data" is used more generally as a synonym for "information", it is treated as a mass noun in singular form. The expression "data processing" was first used in 1954. The word "data" was first used to mean "transmissible and storable computer information" in 1946. The first English use of the word "data" is from the 1640s. The Latin word data is the plural of datum, "(thing) given", neuter past participle of dare, "to give". (Theoretically speaking, infinite data would yield infinite information, which would render extracting insights or intelligence impossible.) In response, the relatively new field of data science uses machine learning (and other artificial intelligence (AI)) methods that allow for efficient applications of analytic methods to big data. Using traditional data analysis methods and computing, working with such large (and growing) datasets is difficult, even impossible. Data, as a general concept, refers to the fact that some existing information or knowledge is represented or coded in some form suitable for better usage or processing.Īdvances in computing technologies have led to the advent of big data, which usually refers to very large quantities of data, usually at the petabyte scale. Data has been described as "the new oil of the digital economy".
The stock of insights and intelligence that accumulates over time resulting from the synthesis of data into information, can then be described as knowledge. Contextually connected pieces of information can then be described as data insights or intelligence. Thematically connected data presented in some relevant context can be viewed as information. Data can range from abstract ideas to concrete measurements, including but not limited to, statistics. Prior to analysis, raw data (or unprocessed data) is typically cleaned: Outliers are removed and obvious instrument or data entry errors are corrected.ĭata can be seen as the smallest units of factual information that can be used as a basis for calculation, reasoning, or discussion. Data is analyzed using techniques such as calculation, reasoning, discussion, presentation, visualization, or other forms of post-analysis. Experimental data is data that is generated in the course of a controlled scientific experiment. Field data is data that is collected in an uncontrolled in-situ environment. In this context, data represents the raw facts and figures which can be used in such a manner in order to capture the useful information out of it.ĭata is collected using techniques such as measurement, observation, query, or analysis, and typically represented as numbers or characters which may be further processed. Examples of data sets include price indices (such as consumer price index), unemployment rates, literacy rates, and census data. ĭata is commonly used in scientific research, economics, and in virtually every other form of human organizational activity. Data may represent abstract ideas or concrete measurements. Data may be used as variables in a computational process. Data is usually organized into structures such as tables that provide additional context and meaning, and which may themselves be used as data in larger structures. A datum is an individual value in a collection of data.
In the pursuit of knowledge, data ( US: / ˈ d æ t ə/ UK: / ˈ d eɪ t ə/) is a collection of discrete values that convey information, describing quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted. These are some of the different types of data.
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