Building a customer 360 requires connecting customer records across disparate data sets and establishing common customer identities. The Customer Entity Resolution Solution Accelerator shows how to build that foundation by translating customer attributes into numerical representations, using machine learning to identify matches, and scoring match confidence.

With this Accelerator, you get

• Ready-to-use resources: pre-built code, sample data and step-by-step instructions ready to go in a Databricks notebook
• Build a customer 360 foundation: link customer records across disparate data sets.
• Represent customer attributes: translate text attributes such as names, addresses and phone numbers into quantifiable numerical representations.
• Identify potential matches: train machine learning models to determine whether numerical representations form a match.
• Score match confidence: quantify the confidence associated with each match.

🔗 Launch Solution Accelerator 👈