Data doesn’t lie, and it tells us things like this.

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 rates, unemployment rates, literacy rates) and non-governmental organizations (e.g., censuses of the number of homeless people by non-profit organizations).

Data is measured, collected and reported, and analyzed, whereupon it can be visualized using graphs, images or other analysis tools. 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. Raw data (“unprocessed data”) is a collection of numbers or characters before it has been “cleaned” and corrected by researchers. Raw data needs to be corrected to remove outliers 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 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 oil of the digital economy.

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.

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. 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.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.