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Data Discovery

What is Data Discovery?

Data Discovery is a term used to describe a range of techniques designed to find relationships between entities (or data items) that may exist within the same database or across multiple, disparate databases.

In the Bloor Research InBrief Market Update into Data Profiling and Data Discovery tools, Philip Howard defines the term Data Discovery as: “...the discovery of relationships between data elements, regardless of where the data is stored...”

A typical example of Data Discovery.

An example of data discovery would be discovering which systems are connected by certain keys or identifiers. This is important because understanding connectivity between systems is useful for building accurate data models and a true account of what business services depend on certain data sources.

Data Discovery can also refer to the discovery of dependencies between data elements, both within the same table and across disparate tables. We say that two attributes are dependent when the value of one attribute has a possible influences on values of another (or more) attributes. Dependency analysis is a valuable technique for uncovering hidden data quality rules that require ongoing management and control.

Data Discovery and Dependency Analysis require complex analytical processing functionality to be executed and ideally a correlated architecture for performance reasons.

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