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Data management concerns of MDM-CDI architecture

Read about the data management concerns associated with MDM-CDI architecture.

Service provider takeaway: There's big money expected to be spent on master data management (MDM) products and services in the next few years, especially among financial services, communications, high-tech and pharmaceutical companies. Learn more about how to help your customers increase revenue, reduce administrative costs and improve client retention in this chapter excerpt from McGraw-Hill's Mastering Data Management and Customer Integration for a Global Enterprise.

Download the .pdf of the chapter here.

The preceding chapters discussed the enterprise architecture framework as the vehicle that helps resolve a multitude of complex and challenging issues facing MDM and CDI designers and implementers. As we focused on the Customer Data Integration aspects of the MDM architecture, we showed how to apply the Enterprise Architecture Framework to the service-oriented view of the CDI platform, often called a Data Hub. And we also discussed a set of services that any Data Hub platform should provide and/or support in order to deliver key data integration properties of matching and linking detail-level records—a service that enables the creation and management of a complete view of the customers, their associations and relationships.

We also started a discussion of the services required to ensure the integrity of data inside the Data Hub as well as services designed to enable synchronization and reconciliation of data changes between the Data Hub and surrounding systems, applications, and data stores.

We have now reached the point where the discussion of the Data Hub architecture cannot continue without considering issues and challenges of integrating a Data Hub platform into the overall enterprise information environment. To accomplish this integration, we need to analyze the Data Hub architecture components and services that support cross-systems and cross-domain information management requirements. These requirements include challenges of the enterprise data strategy, data governance, data quality, a broad suite of data management technologies, and the organizational roles and responsibilities that enable effective integration and interoperability between the Data Hub, its data sources, and its consumers (users and applications).

An important clarification: as we continue to discuss key issues and concerns of the CDI architecture, services, and components, we focus on the logical and conceptual architecture points of view. That means that we express functional requirements of CDI services and components in architecture terms. These component and service requirements should not be interpreted literally as the prescription for a specific technical implementation. Some of the concrete implementation approaches—design and product selection guidelines that are based on the currently available industry best practices and state of the art in the MDM and CDI product marketplace—are provided in Part IV of this book.


Data Management Concerns of MDM-CDI Architecture
  Introduction
  Data Strategy
  Data Quality
  Managing Data in the Data Hub
  Overview of Business Rules Engines
  Metadata Basics

About the book

Master Data Management and Customer Data Integration for a Global Enterprise explains how to grow revenue, reduce administrative costs, and improve client retention by adopting a customer-focused business framework. Learn to build and use customer hubs and associated technologies, secure and protect confidential corporate and customer information, provide personalized services, and set up an effective data governance team. Purchase the book from McGraw-Hill Osborne Media.

Reprinted with permission from McGraw-Hill from Master Data Management and Customer Data Integration for the Global Enterprise by Alex Berson and Larry Dubov (McGraw-Hill, 2007)


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