Configuring Identity Resolution Rules for Customer 360

In modern data systems, customer information is often scattered across multiple platforms, leading to duplicate profiles and disjointed insights. Unifying this data requires a precise strategy to match, reconcile, and merge records without losing critical details. This text-based course guides you through the foundational concepts and practical strategies of identity resolution. You will learn how to design, configure, and optimize rulesets that successfully combine disparate data points into a single, trusted Customer 360 profile. What you'll learn: - Understand the core terminology of identity resolution, including deterministic versus probabilistic matching. - Configure match rules based on unique identifiers like emails, phone numbers, and device IDs. - Apply reconciliation rules to determine which data sources take precedence when conflicts arise. - Design robust rulesets that balance high match accuracy with the prevention of false positives. - Implement data hygiene best practices to clean and standardize inputs before the resolution process begins. - Address modern data privacy and compliance considerations when consolidating customer information. You will start with key definitions and fundamental concepts before progressing to step-by-step written scenarios. Through clear explanations and practical design exercises, you will learn how to model data reconciliation for real-world business needs. This course is designed for beginner data analysts, database administrators, and marketing operations professionals who want to understand data unification. No prior experience with identity resolution tools is required. Start reading today to unlock the power of clean, unified customer data.