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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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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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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Jeff Jonas blogs about our latest study and report encompassing businesses large and small across the largest EU economies. The consensus is that many businesses will struggle to meet the challenge of adhering to GDPR.
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Senzing's website is live as of January 2018! We have been operating in stealth mode since August 2016 when we signed a unique partnership with IBM.
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