Build and Extend
MDM SaaS Data Models
for 360 Applications

Module 1: MDM SaaS and Business 360 Console Fundamentals

• Position MDM SaaS Within the IDMC Platform
• Explore MDM SaaS and the Business 360 Console Architecture
• Identify Technical Use Cases Across C360, S360, and P360
• Navigate User Interfaces Across 360 Applications and Business 360 Console
• Lab: Get Started
• Lab: Perform Data Purge

Module 2: Data Model Configuration

• Add Basic, Smart, Group, and Reference Fields to a Business Entity
• Configure Field Properties
• Configure Dynamic Pools and Dynamic Field Definitions
• Add Dynamic Field Values to Business Entities and Configure Inheritance
• Lab: Customize a Predefined Business Entity
• Lab: Configure Dynamic Pools, Field Definitions, and Field Values

Module 3: Data Quality and Survivorship Configuration

• Explore Types of Data Quality Rules
• Configure a Basic Data Quality Rule
• Configure Survivorship for Business Entities, Field Groups, and Dynamic Fields
• Lab: Add a Basic Data Quality Rule
• Lab: Configure Survivorship

Module 4: Data Onboarding

• Add New Records for C360, P360, and S360
• Manage Records, Related Records, and Dynamic Field Inheritance
• Import Business Entity Data from CSV Files
• Lab: Manage Customer, Product, and Supplier Records
• Lab: Import Business Entity Data from a CSV File
• Lab: Add Related Records and Verify Dynamic Field Inheritance

Module 5: Match Model Configuration and Automated Merge

• Explore Challenges with Duplicate Records
• Configure a Match Model
• Add Directed AI Match Rules
• Define and Run a Match and Merge Job
• Lab: Configure a Match Model
• Lab: Run a Match and Merge Job to Automatically Merge Records

Module 6: Record Merging with Manual Review and Workflow Approval

• Manage Business Events
• Generate Merge Tasks and Perform Manual Merge
• Match and Export Data Using an Existing Match Model
• Lab: Create a Business Event for Manual Merge Approval
• Lab: Match and Export Partner Contacts to Identify Existing Customers

Module 7: AI Features: CLAIRE Copilot and Adaptive AI Matching

• Explore Data with CLAIRE Copilot
• Explore Adaptive AI Match Models and Training Metrics
• Configure Match Fields for Adaptive AI Training
• Label Record Pairs and Evaluate Model Accuracy
• Lab: Discover and Explore Data with CLAIRE Copilot
• Lab: Train an Adaptive AI Match Model

Module 8: Hierarchies and Data Analysis

• Configure the Hierarchy Model
• Manage Hierarchy Instances
• Create Dashboards and Add Predefined Reports
• Explore the Match Analysis and Explainability Dashboard
• Monitor Usage and Job Metrics with the Usage Statistics Dashboard
• Analyze Relationships Using Relationship Graphs
• Lab: Manage Hierarchies and Analyze Relationships
• Lab: Create a Dashboard and Add Predefined Reports
• Lab: Monitor Usage and Job Metrics with the Usage Statistics Dashboard

Module 9: REST API Integration

• Explore Public REST API Overview and Guidelines
• Identify Resources Supported for C360, P360, and S360
• Manage Hierarchies Using the REST API
• Lab: Use REST APIs to Perform Tasks and Retrieve Data
• Lab: Manage Hierarchies Using the REST API