Enterprises that want to add entity resolution to their applications or services often consider whether to build it or buy it. In this blog we discuss both options and explain why it building doesn’t make sense anymore.
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Humans are producing a tsunami of data. We need smarter tools like entity resolution. We need to connect scattered, disparate big data to reveal new and important discoveries.
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Learn more about how understanding exactly why an entity resolution match was or wasn't made helps build confidence in your data and business decisions.
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Entity resolution often takes too long. At Senzing, we’ve focused on making entity resolution software fast and easy. Watch the first video in our new video series.
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Learn 3 quick ways to install and explore Senzing. Get started and load data in minutes to our desktop tool or API software.
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Check out how we've improved performance, improved infrastructure requirements, and reduced total cost of ownership (TCO) to the Senzing API.
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There are seven key capabilities, each with their own development challenges, that ISVs should be aware of when wanting to build entity resolution models into their offerings.
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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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