MDM - The Book
Master Data Management, written by David Loshin, is a book that equips you with a deeply practical, business-focused way of thinking about MDM—an understanding that will greatly enhance your ability to communicate with stakeholders and win their support. Moreover, it will help you deserve their support: you’ll master all the details involved in planning and executing an MDM project that leads to measurable improvements in business productivity and effectiveness.
• Presents a comprehensive roadmap that you can adapt to any MDM project.
• Emphasizes the critical goal of maintaining and improving data quality.
• Provides guidelines for determining which data to “master.”
• Examines special issues relating to master data metadata.
• Considers a range of MDM architectural styles.
• Covers the synchronization of master data across the application infrastructure.
The book has 13 chapters:
Chapter 1, Master Data and Master Data Management, introduces the historical issues that have created the need for master data management, describes both “master data” and “master data management,” begins to explore what goes into an MDM program, and reviews the business value of instituting an MDM program, along with this overview of the rest of the book.
Chapter 2, Coordination: Stakeholders, Requirements, and Planning describes who the MDM stakeholders are, why they are relevant to the success of an MDM program, and what their expected participation should be over the course the program’s development.
Every organization exhibits different levels of maturity when it comes to sharing consolidated information, and Chapter 3, MDM Components and the Maturity Model, provides a capability model against which an organization’s maturity can be measured. By assessing the organization’s current state, considering the level of maturity necessary to achieve the organization’s objectives, and determining where the organization needs to be, one can assemble an implementation road map that enables action.
Master data management is an enterprise initiative, and that means an enterprise data governance program must be in place to oversee it. Governance is a critical issue for deploying MDM, and in Chapter 4, Data Governance for Master Data Management, we discuss how business policies are composed of information directives, and how data rules contribute to conformance to those information directives. We’ll look at what data governance is, introduce data stewardship roles and responsibilities, and propose a collaborative enterprise data governance framework for data sharing.
No book on MDM would be complete without a discussion of the value of data quality, and Chapter 5, Data Quality and MDM, examines the historical evolution of MDM from data quality to its reliance on high-quality information. This chapter provides a high level view of the data quality components and methods that are used for the purposes of master data integration.
The key to information sharing through an MDM repository is a solid set of data standards for defining and managing enterprise data and a comprehensive business metadata management scheme for controlling the use of enterprise data. Chapter 6, Metadata Management for MDM, discusses data standards and metadata management and explores how master metadata is managed.
As part of the process, it is necessary to identify the master data object types and determine the data assets that make up those object types across the enterprise. In Chapter 7, Identifying Master Metadata and Master Data, we look at the process of identifying and finding the data sets that are candidates as sources for master data and how to qualify them in terms of usability.
A core issue for MDM is creating the consolidation models to collect and aggregate master data. Chapter 8, Data Modeling for MDM, is where we will look at some of the issues associated with different source models and how to address data modeling issues for MDM.
There are different architectural paradigms for master data management, and in Chapter 9, MDM Paradigms and Architectures, we look at existing application and information architectures and different architectural styles for MDM, how they all reflect a spectrum of implementations, and the pros and cons of each of those styles.
Given a model and understanding the sources of master data, the next step is the actual process of data consolidation and integration, and Chapter 10, Data Consolidation and Integration, looks at collecting information from across the organization and formulating that into the integrated master data asset.
The power of MDM increases greatly when the master data can be integrated back into the existing application environment. Chapter 11, Master Data Synchronization, discusses the needs and approaches for synchronizing data back to the existing applications.
The value of MDM does not lie solely with the integration of data. The ability to consolidate application functionality (e.g., new customer creation) using a services layer that supplements multiple application approaches will provide additional value across the existing and future applications. The topic of a functional application services layer is covered in Chapter 12, Master Data Management and the Functional Services Layer.
The book concludes with Chapter 13, Management Guidelines for MDM, a summary of the guidance provided throughout the preceding chapters to inform management decisions. To address the ongoing management issues, we offer some management guidelines for transitioning the project from developing the business case to maintaining a successful program.
admin @ June 16, 2008