Jeff Jonas blogs about the problems that surface when working with aggregated data sets. He also discusses the best data attributes to use when multiple attributes are known.
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Senzing real-time AI helps organizations uncover critical information to detect and preempt slip and fall fraudsters. Finding collusion between victims and those authorizing settlement is one application for entity resolution.
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Bob Gourley of CTOvision reviews Senzing. Every organization that has data likely has entity resolution challenges- figuring out who is who and what is what in their data.
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Jeff Jonas blogs about his history with PbD on the 70th Anniversary of Universal Declaration of Human Rights. Jeff discusses the PbD paper he co-authored with Ann Cavoukian and how these features are built into Senzing core technology.
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Deepak Rangarao of IBM Analytics/Ecosystem reviews Senzing as a guest blogger. He discusses how Senzing can be used to target identity fraud with up to 100,000 records for free.
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Jeff Jonas speaks with David Becker at the International Association of Privacy Professionals (IAPP) keynote panel: ‘Privacy. Security. Risk.’ for the International Association of Privacy Professionals (IAPP) on October 18, 2018.
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John Breeden II reviews Senzing. Senzing makes connections between elements in many databases, including connections with fuzzy data and purposefully false data. Senzing reassesses previous assumptions in real time.
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Jeff Jonas blogs about how the Senzing name came to be.
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Jeff Jonas blogs about the laborious process of manually or automatically searching each individual system to locate data meeting specific criteria. He then explains how entity resolution effectively and efficiently solves these deficiencies.
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John Breeden II reviews Senzing. Senzing uncovers fraudulent activity or uncover accidental data duplication for a financial institution. Anyone with a complex data set can uncover fraud with Senzing® software.
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Carahsoft announces a strategic partnership with Senzing as master government reseller.
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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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