Broadly defined, data discovery is the process of finding patterns and trends in processed, analyzed, or visualized data. The reason data discovery must be defined “broadly” is because this process is popular across domains. These patterns and trends can be “discovered” from the data using different methods depending on the context and domain of the work.
Recently, the term “data discovery” has been popularized as a process in Business Intelligence, with a lot of software applications and tools aiding the user in discovering trends, patterns, outliers, clusters, etc. The data discovery process itself has a longer history that dates back to the beginning of data mining. Data mining started as a trend in the 1980s and was a process of extracting information by examining databases (under human control). Other names for data mining include knowledge extraction, information...
- Begoli, E., & Horey, J. (2012). Design principles for effective knowledge discovery from big data. In Software Architecture (WICSA) and European Conference on Software Architecture (ECSA), 2012 joint working IEEE/IFIP conference on IEEE (pp. 215–218). IEEE.Google Scholar
- Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37.Google Scholar
- Haan, K. (2016). So what is data discovery anyway? 5 key facts for BI. Retrieved Sept 24, 2017, from https://www.ironsidegroup.com/2016/03/21/data-discovery-5-facts-bi/.