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  • Customer Master Data Management Basics
  • Customer Data Model
  • Match and Merge Rules
  • Web User Interface
  • Import Capabilities and Error Views
  • Hierarchies
  • Versioning and Auditing
  • Manual Merge and Unmerge capabilities for Data Stewards

Customer master data management is a solution used to manage and create master data records by avoiding duplications. By analysing customer data in a single platform, this type of data management product delivers a single customer view linking heterogeneous data sources together, addressing data quality issues and synchronising customer information.

Customer MDM can help you to:

  • Track history of customer interactions

  • Avoid duplication in data processing

  • Lower marketing costs

  • Increase sales

  • Gain customer insights while delivering more value

  • Connect with your customers and build meaningful relationships

 

We will achieve the below in this course:

  • Introduction to Customer Master Data Management

  • Customer Data Model

  • Match and Merge Rules

  • Import capabilities

  • Error views

  • Hierarchies

  • Versioning and Auditing

  • Manual merge and unmerge capabilities

  • Reporting on customer data

  • Search capabilities

If you want to learn key concepts and capabilities of customer master data management solution, this is the course for you. We will build a complete CMDM solution with Semarchy . The principals used to build our semarchy CMDM solution can be used with other MDM products as well if they offer standard OOTB functionalities like setting up screens, match and merge capabilities etc.

We will use free 7 day trial from Semarchy xDM to build our CMDM solution. Please note this course focuses on building CMDM key capabilities which can be implemented in any standard MDM solution and not on teaching Semarchy basics, have got another course 'Introducing Semarchy xDM (MDM) for Beginners' for that.

  • Basic understanding of any MDM product for implementing Customer Master Data Management (CMDM) solution taught in this course
  • Anyone who wants to understand and learn customer master data management basics and implementation
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  • Section 1 : Introduction 2 Lectures 00:06:04

    • Lecture 1 :
    • Lecture 2 :
    • Course Resources
  • Section 2 : Data Model 6 Lectures 00:40:09

    • Lecture 1 :
    • Introduction to Data Model
    • Lecture 2 :
    • Source Data Files
    • Lecture 3 :
    • Data Model Pre-reqs
    • Lecture 4 :
    • Building Entities
    • Lecture 5 :
    • Establishing relationships and Creating Diagram
    • Lecture 6 :
    • Setting up Enrichers and Validations
  • Section 3 : Match and Merge Rules 2 Lectures 00:13:31

    • Lecture 1 :
    • Defining Match Rules
    • Lecture 2 :
    • Survivorship Rules
  • Section 4 : Application Components and Deployment 2 Lectures 00:17:44

    • Lecture 1 :
    • Creating Application Components
    • Lecture 2 :
    • Deploying our data model
  • Section 5 : Configuring Web User Interface 6 Lectures 00:40:54

    • Lecture 1 :
    • Creating Error Views
    • Lecture 2 :
    • Creating Import Capabilities
    • Lecture 3 :
    • Updating Action Set and Stepper
    • Lecture 4 :
    • Disabling Email Validation and Importing Data
    • Lecture 5 :
    • Fixing Errors
    • Lecture 6 :
    • Enhancing Web UI screen
  • Section 6 : Hierarchies and versioning 2 Lectures 00:14:13

    • Lecture 1 :
    • Setting up Customer Hierarchy
    • Lecture 2 :
    • Versioning and Auditing
  • Section 7 : Manually Merge and Unmerge from Web UI 4 Lectures 00:10:36

    • Lecture 1 :
    • Confirm Duplicates
    • Lecture 2 :
    • Review Suggestions
    • Lecture 3 :
    • Merge Operation
    • Lecture 4 :
    • Unmerge
  • Section 8 : Configuring Search 1 Lectures 00:06:50

    • Lecture 1 :
    • Global Search
  • Section 9 : Dashboard 1 Lectures

    • Lecture 1 :
    • Setting up Customer Dashboard
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388 Course Views

3 Courses

Information Management professional with experience in multiple data management assignments focusing on Master Data Management, Data Governance, Data Quality, Data Integration, Data Migration and Data Analysis. Extensive experience in working as well as leading multiple data management engagements. Also work in close collaboration with the business stakeholders to ensure successful program delivery.
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