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Machine learning is helping data discovery evolve into a more robust information relationship-mapping concept
Data discovery began as an early analysis of singular data sources. It has now evolved into far more robust ways of analyzing information and the relationships between different fields and information sources. According to David A. Teich, a B2B technology analyst and consultant, this evolution is being facilitated by machine learning (ML). Writing for Forbes, he adds that ML is simplifying the complexity involved in finding relationships between information residing in multiple systems and defining the relationship between information scattered across disparate systems.
ML systems can help enterprises maintain information in multiple systems in two ways. First, by enabling intelligent, rapid, transiting of the systems, it can quickly interrogate indices and other metadata. This will aid in building a model of the defined relationships and help an analyst identify consistency and quality issues. Second, an ML system can examine the data dispassionately without preconceptions and identify new relationships between entities and data. The result reflects an integrated and holistic view of the information available in enterprise systems.
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