![]() ![]() The following diagram shows our solution architecture. These datasets are synthetically generated and represent a common problem for entity records stored in multiple, disparate data sources with their own lineage that appear similar and semantically represent the same entity but don’t have matching keys (or keys that work consistently) for deterministic, rule-based matching. In this post, we go through the various steps to apply ML-based fuzzy matching to harmonize customer data across two different datasets for auto and property insurance. We use Amazon Neptune to visualize the customer data before and after the merge and harmonization. In this post, we look at how we can use AWS Glue and the AWS Lake Formation ML transform FindMatches to harmonize (deduplicate) customer data coming from different sources to get a complete customer profile to be able to provide better customer experience. These techniques utilize various machine learning (ML) based approaches. This has led to so-called fuzzy deduplication techniques to address the problem. The underlying schemas were implemented independently and don’t adhere to common keys that can be used for joins to deduplicate records using deterministic techniques. It’s commonly referred to as a data harmonization or deduplication problem. There are customer records in this data that are semantic duplicates, that is, they represent the same user entity, but have different labels or values. This problem particularly impacts companies trying to build accurate, unified customer 360 profiles. ![]() These sources are often related but use different naming conventions, which will prolong cleansing, slowing down the data processing and analytics cycle. Typically, companies ingest data from multiple sources into their data lake to derive valuable insights from the data. Companies are faced with the daunting task of ingesting all this data, cleansing it, and using it to provide outstanding customer experience. In today’s digital world, data is generated by a large number of disparate sources and growing at an exponential rate. I also understand that my agreement to be contacted is not a condition of purchasing any property, goods or services, and that I may call to speak with someone about obtaining an quote.Harmonize data using AWS Glue and AWS Lake Formation FindMatches ML to build a customer 360 view | Amazon Web Services I further expressly consent by electronic signature to being contacted by telephone (via call and/or text) for such marketing/telemarketing purposes at the phone number I provided in this form, even if my phone number is listed on a Do Not Call Registry, and I agree that such contact may be made using an automatic telephone dialing system and/or an artificial or prerecorded voice (standard call, text message, and data rates apply). Marketing Partners, or by one or more agents or brokers of your partners for marketing purposes to discuss my interest, including offers of, at the phone number and/or email address I have provided to you in submitting this form. Not in State Claim Notice AGE Confirmation By clicking Get My Free Quotes and submitting this form, I affirm that I have read and agree to this website's I also understand that my agreement to be contacted is not a condition of purchasing any property, goods or services, and that I may call to speak with someone about obtaining an quote. By clicking Get My Free Quotes and submitting this form, I am providing express written consent to being contacted by you, ![]()
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