The Experts below are selected from a list of 12 Experts worldwide ranked by ideXlab platform
David Loshin - One of the best experts on this subject based on the ideXlab platform.
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Chapter 8 – Data Modeling for MDM
Master Data Management, 2020Co-Authors: David LoshinAbstract:Publisher Summary The Data sources that contribute to the creation of the Master representation may have variant representations, but at some point, there must be distinct models for the managing and subsequent sharing of Master Data. A core issue for Master Data management (MDM) is creating the consolidation models to collect and aggregate Master Data. This chapter discusses the issues associated with developing models for Master Data—from extraction, consolidation, persistence, and delivery. It explores the challenges associated with the variant existing Data models and then examines some of the requirements for developing the models used for extraction, consolidation, persistence, and sharing. It is important to realize that becoming a skilled practitioner of Data modeling requires a significant amount of training and experience. The purpose of this chapter is to highlight the more significant issues to be considered when developing models for MDM. The creation of Data models to accommodate the Master Data integration and management processes requires a combination of the skills a Data modeler has acquired. Further, it also requires the understanding of the business process requirements expected during a transition to a Master Data Environment.
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MDM Paradigms and Architectures
Master Data Management, 2020Co-Authors: David LoshinAbstract:It is useful to understand the ways that application architectures adapt to Master Data, to assess the specific application's requirements, and to characterize the usage paradigms. This drives the determination of the operational service level requirements for Data availability and synchronization that dictates the specifics of the underlying architecture. This chapter examines typical Master Data management usage scenarios as well as the conceptual architectural paradigms. Following this, the chapter deals with the variable aspects of the architectural paradigms in the context of the usage scenarios and enumerates the criteria that can be used to determine the architectural framework that best suits the business needs. Understanding how Master Data are expected to be used by either human or automated business clients in the Environment is the first step in determining an architecture, especially when those uses are evaluated in relation to structure. While there are some archetypical design styles ranging from a loosely coupled, thin registry, to a tightly coupled, thick hub, these deployments reflect a spectrum of designs and deployments providing some degree of flexibility over time. Fortunately, a reasonable approach to Master Data management design will allow for adjustments to be made to the system as more business applications are integrated with the Master Data Environment.
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MetaData Management for MDM
Master Data Management, 2020Co-Authors: David LoshinAbstract:The metaData requirements for Master Data management (MDM) exceed the typical demands of application development, because the ability to consolidate and integrate Data from many sources is bound to be hampered by variant business terms, definitions, and semantics. However, once a decision is made to use metaData as a lever for enabling the migration to the Master Data Environment, it is wise to consider the more sophisticated means for enterprise information management that can be activated via the metaData management program. 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. This chapter discusses Data standards and metaData management. It explores each layer of metaData from the bottom up and reviews its relevance to the MDM framework. In turn, the MDM team should use the requirements to identify candidate metaData management tools that support the types of activities described in this chapter. The technology should not drive the process, but it should be the other way around. Solid metaData management supports more than just MDM—good enterprise information management relies on best practices in activating the value that metaData provides.
Pat Herbert - One of the best experts on this subject based on the ideXlab platform.
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How to Leverage DataFlux® and SAS® for Operational Empowerment
2020Co-Authors: Scott Chastain, Lisa Dodson, Pat HerbertAbstract:The need for decision support is expanding beyond the strategic scope and growing ever more important in tactical operations. The challenge is that as the need for operational intelligence increases, so does the importance of credible Data and real-time analytics. This paper will discuss and demonstrate how SAS® may be deployed in a Master Data Environment to support advanced operational decision support.
Scott Chastain - One of the best experts on this subject based on the ideXlab platform.
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How to Leverage DataFlux® and SAS® for Operational Empowerment
2020Co-Authors: Scott Chastain, Lisa Dodson, Pat HerbertAbstract:The need for decision support is expanding beyond the strategic scope and growing ever more important in tactical operations. The challenge is that as the need for operational intelligence increases, so does the importance of credible Data and real-time analytics. This paper will discuss and demonstrate how SAS® may be deployed in a Master Data Environment to support advanced operational decision support.
Lisa Dodson - One of the best experts on this subject based on the ideXlab platform.
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How to Leverage DataFlux® and SAS® for Operational Empowerment
2020Co-Authors: Scott Chastain, Lisa Dodson, Pat HerbertAbstract:The need for decision support is expanding beyond the strategic scope and growing ever more important in tactical operations. The challenge is that as the need for operational intelligence increases, so does the importance of credible Data and real-time analytics. This paper will discuss and demonstrate how SAS® may be deployed in a Master Data Environment to support advanced operational decision support.