![]() ![]() Instead of building their own analytics solutions, some companies rely on external solutions to match their records, but this approach also comes with challenges. Adding ML for more accurate, predictive matching requires even more specialized expertise and additional development time to collect and normalize huge amounts of curated data, train and test matching models, and deploy the ML models. In order to build these workflows, organizations have to spend costly developer resources, invest months of development time, and continuously update their data pipelines. To link those records, companies typically use a combination of home-grown complex data pipelines and integrations with external partners, but reconciling records containing disparate or incomplete information is not easy. For example, companies may want to link recent consumer interactions (e.g., ad clicks, cart abandonment, and purchases) with a unique ID, so they can better understand shopping patterns across applications (e.g., ad platforms, loyalty programs, and ecommerce systems). Records containing information about customers, businesses, and products (e.g., product SKUs, UPCs, and manufacturer codes for the same product) are increasingly siloed in hundreds of different applications, channels, and data stores across multiple organizations. Customers can get started with AWS Entity Resolution by visiting /entity-resolution. These integrations will enable customers to more easily translate or enrich their own records while better protecting their information and reducing data movement. ![]() AWS also announced plans to add two entity resolution partner integrations with LiveRamp and TransUnion, as well as interoperability with the Unified ID 2.0 open source framework. With AWS Entity Resolution, businesses can better understand how their data is related, matched, and linked and develop deeper customer insights, clearer supply chain data for improved operations, more relevant marketing campaigns, and improved complex financial investment decisions. Business analysts and developers can improve the fidelity of their data quickly with built-in, preconfigured workflows, or customize the workflows to fit their organization's needs. AWS Entity Resolution uses customizable workflows that leverage rule-based and ML techniques to join related consumer, business, and product information. (AWS), an company (NASDAQ: AMZN), today at AWS Summit New York announced the general availability of AWS Entity Resolution, an analytics service powered by machine learning (ML) that helps organizations easily analyze, match, and link related records stored across applications, channels, and data stores. NEW YORK-(BUSINESS WIRE)- Amazon Web Services, Inc. ML-powered service helps companies match and link records stored across multiple applications, channels, and data storesĪctionIQ, Amazon Ads, Best Western, LiveRamp, Merkle, TransUnion, and Unified ID 2.0 among early customers and partners using AWS Entity Resolution ![]()
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