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David Loshin - One of the best experts on this subject based on the ideXlab platform.

  • Chapter 12 – MDM and the Functional Services Layer
    Master Data Management, 2020
    Co-Authors: David Loshin
    Abstract:

    Publisher Summary The value of Master Data management (MDM) does not lie solely in 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. This chapter covers the functional application services layer. There is a symbiotic relationship between MDM and a services-oriented architecture (SOA): a services-oriented approach to business solution development relies on the existence of a Master Data repository, and the success of an MDM program depends on the ability to deploy and use Master Object access and manipulation services. Employing a services-based approach serves multiple purposes within the MDM program. As the program is launched and developed, the use of multiple service layers simplifies both the implementation of the capabilities supporting the Data Object life cycle and the transition from the diffused distributed application architecture into the consolidated Master application infrastructure. The flexibility provided by an SOA triggers the fundamental organizational changes necessary to use the shared information asset in its most effective manner. By driving application development from the perspective of the business instead of as a sequence of procedural operations, the organization can exploit the Master Data Object set in relation to all business operations and the ways that those Objects drive business performance.

  • Data Consolidation and Integration
    Master Data Management, 2009
    Co-Authors: David Loshin
    Abstract:

    There are two aspects to consolidation and integration: the initial migration of Data from source Data and the ongoing integration of Data instances created, modified, or removed from the Master environment. Data integration comprises the processes for collecting Data from different sources and making that Data accessible to specific applications. Because Data integration has largely been used for building Data warehouses, it is often seen as part of collecting Data for analysis. However, as more operational Data sharing activities are seen, it is observed that Data integration has become a core service necessary for both analytics and operations. This chapter explores the techniques employed in extracting, collecting, and merging Data from various sources. The intention is to identify the component services required for creating the virtual Data set that has a single instance and representation for every unique Master Data Object and then use the Data integration and consolidation services to facilitate information sharing through the Master Data asset. The central driver of Master Data management is the ability to locate the variant representations of any unique entity and unify the organization's view to that entity. The process by which this is accomplished must be able to access the candidate Data sets, extract the appropriate identifying information, and then correctly assess similarity to a degree that allows for linkage between records and consolidation of the Data into an application service framework that permits access to the unified view. The technical components of this process—parsing, standardization, matching and identity resolution, and consolidation—must be integrated within two aspects of the Master environment: the underlying technical infrastructure and models that support the implementation, and the governance framework that oversees the management of information access and usage policies.

  • Chapter 10 – Data Consolidation and Integration
    Master Data Management, 2009
    Co-Authors: David Loshin
    Abstract:

    Publisher Summary There are two aspects to consolidation and integration: the initial migration of Data from source Data and the ongoing integration of Data instances created, modified, or removed from the Master environment. Data integration comprises the processes for collecting Data from different sources and making that Data accessible to specific applications. Because Data integration has largely been used for building Data warehouses, it is often seen as part of collecting Data for analysis. However, as more operational Data sharing activities are seen, it is observed that Data integration has become a core service necessary for both analytics and operations. This chapter explores the techniques employed in extracting, collecting, and merging Data from various sources. The intention is to identify the component services required for creating the virtual Data set that has a single instance and representation for every unique Master Data Object and then use the Data integration and consolidation services to facilitate information sharing through the Master Data asset. The central driver of Master Data management is the ability to locate the variant representations of any unique entity and unify the organization's view to that entity. The process by which this is accomplished must be able to access the candidate Data sets, extract the appropriate identifying information, and then correctly assess similarity to a degree that allows for linkage between records and consolidation of the Data into an application service framework that permits access to the unified view. The technical components of this process—parsing, standardization, matching and identity resolution, and consolidation—must be integrated within two aspects of the Master environment: the underlying technical infrastructure and models that support the implementation, and the governance framework that oversees the management of information access and usage policies.

  • Chapter 7 – Identifying Master MetaData and Master Data
    Master Data Management, 2008
    Co-Authors: David Loshin
    Abstract:

    Publisher Summary It is necessary to identify the Master Data Object types and determine the Data assets that make up those Object types across the enterprise. This chapter discusses 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. This chapter considers the challenge of Master Data discovery, which depends on the effective collection of metaData from the many application Data sets that are subject to inclusion in the Master Data repository. This depends on a process for analyzing enterprise metaData—assessing the similarity of syntax, structure, and semantics as a prelude to identifying enterprise sources of Master Data. Because the Objective in identifying and consolidating Master Data representations requires empirical analysis and similarity assessment as part of the resolution process, it is reasonable to expect that tools will help in the process. The same kinds of tools and techniques that will subsequently be used to facilitate Data integration can also be employed to isolate and catalog organizational Master Data.