ISVs trying to decide whether to build or buy better data matching or relationship detection for their commercial software product can get advanced and affordable entity resolution in a couple of sprints.
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Mike Barrett, head of anti-money laundering products, discusses the business and technical reasons why NICE Actimize decided to embed Senzing entity resolution into its AML product suite.
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Companies evaluating entity resolution software often make three painful mistakes. Learn how to avoid these costly errors and reduce your risk of buyer’s remorse. Get tips on how to ensure your evaluation is a success.
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Entity resolution is the process of determining when people or organizations are the same, despite differences in how they are described. Entity resolution is known by many names including data matching, record linkage, fuzzy matching and more.
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Jeff Jonas blogs about a tutorial showing how easy it is to accurately combine data from different public sources, in this case Paycheck Protection Program (PPP) loan data fraud using the Senzing App.
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Jeff Jonas blogs about Senzing- witness the deployment of a fully scalable AWS serverless stack from start to finish in 24 minutes of clock time.
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Jeff Jonas blogs about why the record matching method, the long-standing standard in entity resolution, no longer cuts it this day and age. Entity-centric learning is the more advanced way to do entity resolution.
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Richard Wozniak of IBM interviews Jeff Jonas in his personal blog. They discuss the origins of Senzing, Jeff's quest to democratize entity resolution and the partnership with IBM Cloud Pak.
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Jeff Jonas blogs about entity resolution algorithms. What does your system do? Try our Synthetic Truth Set.
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Jeff Jonas and Anju Gupta blog about one quick and easy way for cost take-out and why CDOs and data strategists may be overlooking it.
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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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Jeff Jonas blogs on an entity resolution subtlety that is exceptionally hard to detect and correct in real time, at scale. It's rare in most data ecosystems, but important nonetheless.
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