Steve Lohr from the NYT dives into how Senzing technology is used to clean up voter rolls. Since 2012, Senzing has helped the Electronic Registration Information Center (ERIC) identify 26 million people who are eligible but unregistered to vote.
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Learn about the six areas where the Senzing real-time AI for entity resolution stands apart from its competitors.
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Uncover how a principle-based approach makes entity resolution more accurate than an approach utilizing rules.
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Deploy real-time AI for entity resolution and see a ROI in months or even weeks. Free up your top tech talent to work on more pressing tasks.
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Ben Lorica interviews Jeff Jonas about big data challenges involving entity resolution including deep learning models and big compute.
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Jeff Jonas blogs on being amazed (and surprised) by what people think is computable given their actual observation space.
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Alex Woodie interviews Jeff Jonas about what entity resolution is, why all organizations need it, and how it can benefit everything from CRM to fraud.
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Ton Dobbe of Value Inspiration podcast interviews Jeff Jonas. They discuss how Senzing has developed complex solutions that are easy to use.
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Jeff Jonas blogs about sequence neutrality. Sequence neutral computation is what humans do to reclassify data/assumptions after receiving context. Wouldn’t it be great if systems could do the same thing? Most can’t. Senzing can.
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Jeff Jonas blogs about how over-cleaning data can make systems less intelligent. Bad data means natural variability in data. Bad data combined with entity resolution makes systems smarter.
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Jeff Jonas blogs about why establishing principle- versus rule-based entity resolution eliminates need for experts and training of datasets.
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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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