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Data discovery, in the context of IT, is the process of extracting actionable patterns from data. The extraction is generally performed by humans or, in certain cases, by artificial intelligence systems. The data presented is typically in a visual format and may look like a dashboard, depending on how it is presented in the application.
Data discovery is a type of data use that relies, in large part, on technologies that have enabled the aggregation and use of big data. Big data sets are composed of large and heterogeneous types of data that are fed into business systems for the purpose of gaining business intelligence (BI).
In data discovery, humans – or, in some cases, certain types of artificial intelligence technologies – look at data from various sources and try to extract important or meaningful information from that data. This is done in order to support various business objectives. Data discovery tools use a variety of methods such as heat maps, pivot tables, pie charts, bar graphs and geographical maps to help users accomplish their goals.
Some experts see data discovery as similar to data mining, which is a process used by some companies to try to extract actionable data from a large data set. In some ways, data discovery may also be explained by its similarity to electronic discovery (e-discovery); in e-discovery, which often pertains to the legal field, assigned IT professionals extract data from large data sets that may be applicable or relevant to a case or process. The idea of data discovery takes a similar approach – sifting through a large field of data for relevant and actionable items that stand out.