"Data discovery is the process where an organization obtains an understanding of which data matters the most and identifies challenges with that data. The outcome of data discovery is that the scope of a data quality initiative should be clear and data quality rules can be defined." (Robert Hawker, "Practical Data Quality", 2023)
"Data Discovery describes a range of techniques designed to collect and consolidate information before an alysing it to find relationships and outliers between entities (or data items) that may exist. This process may be done on data from the same database or across multiple, disparate databases. (experian) [source]
"Data discovery involves the collection and evaluation of data from various sources and is often used to understand trends and patterns in the data." (Tibco) [source]
Data discovery is not a tool. It is a business user oriented process for detecting patterns and outliers by visually navigating data or applying guided advanced analytics. Discovery is an iterative process that does not require extensive upfront model creation. (BI Survey) [source]
"Data discovery is the process of using a range of technologies that allow users to quickly clean, combine, and analyze complex data sets and get the information they need to make smarter decisions and impactful discoveries." (Qlik) [source]
"The process of analyzing the type, quality, accessibility, and location of data in all available data repositories. It's critical for determining the current state of a data environment, especially when a recent and accurate data dictionary doesn't exist." (Forrester)
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