Jeff Jonas blogs about how bad guys utilize "channel separation" to remain undetected. Senzing uses entity centric learning to “channel consolidate” data, even though bad guys intentionally obfuscate their identities.
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Jeff Jonas blogs about using data to figure out how to ask the right questions. With Data Finds Data behavior, your systems can automatically and in real time flag data that is connected so you can start asking the right questions.
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Kyle Wiggers of VentureBeat reviews Senzing. With seeded data from the financial industry, Senzing discovers relationships between people, objects, and metadata with a 97% success rate.
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Channel separation is a method that hackers employ to confuse security systems. Senzing detects fraud and insider threat by performing channel consolidation.
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Jeff Jonas explains entity resolution in detail, with the assistance of examples, at IBM Think 2018.
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Venky Rao reviews the Senzing app. He discusses questions that arose during the testing process, his initial thoughts, and the results that the Senzing app discovered from his data.
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Jeff Jonas blogs that unstructured data is only useful if structure can be extracted from it. Feature extraction algorithms can be helpful in this process.
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