CONTEXT: THE CASE AND PLACE FOR SMART DATA STRATEGY in .NET framework

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CONTEXT: THE CASE AND PLACE FOR SMART DATA STRATEGY
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will see a reduction in data con icts in enterprise operations In addition, it will enable more cost ef cient data reconciliation processes and products, which in turn can extend the reach of the enterprise into increasingly marginal (low-volume) applications Basic to the data engineering discipline is the need to semantically characterize all classes of data relevant to the enterprise (all data that is passed system to system) through a formal process This process is practiced through various methods of formalization of metadata: for example, de nition of data elements, information modeling, and metamodeling It can be practiced at four levels 1 Unmanaged, Ad Hoc Data is de ned locally on the y 2 Systematic Data De nitions Data is de ned on an element-by-element basis This is basically the conduct of a data inventory, which is the necessary rst step to higher levels of data engineering In a bottom up fashion, data elements are rigorously de ned using a prescriptive process such as the International 11179 Standard Accuracy and synchronization with physical sources and instances of data are largely manually achieved, as is con guration control and governance supported by a variety of data repository types, such as databases and spreadsheets 3 Formalized Information Modeling Data accuracy and synchronization are achieved at a higher level of abstraction through information modeling of the underlying metadata Information modeling more fully describes data/metadata by describing the relationships between data elements as well as de ning the data elements themselves This increases the semantic content of the data enabling the interoperability of data by means of semantic mediation engines Con guration management at this level requires a more complex data repository to maintain the models as well as data element de nitions 4 Metadata-Driven Information Integration A model-driven architecture prevails for the enterprise Supported by both a design-time metadata repository and run-time integration engine, this level facilitates enterprise integration by application and system developers as well as during operations Both are achieved through extensive data engineering in a top down fashion such that metadata and information models are integrated through metamodeling and similar means Grid Engineering Grid engineering is the discipline that develops and evolves the smart grid architecture for an enterprise, consisting partially of adapting the generic SOA to speci cs of the enterprise This consists of selection of integration tools, processes, and standard protocols, such as the Web Services Description Language (WSDL) and Simple Object Access Protocol (SOAP) It also establishes the rules of engagement criteria for applications and systems to participate in the enterprise It also prescribes the method for integrating legacy systems and applications into the enterprise
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With respect to the enterprise, selecting protocols and then broadcasting the selected protocols to system developers accomplishes grid engineering However, grid engineering also entails the development and enforcement of an enterprise information assurance and security strategy Future web service protocols that directly address this topic may simplify this task Grid engineering also includes de ning the service layers that connect enterprise systems to the grid The attributes of a smart grid will not be attained through protocols alone Every system that participates in the enterprise must provide functional capability as structured layers of services Grid engineering can be practiced at one of four levels 1 Unmanaged, Ad Hoc Legacy applications and corresponding business intelligence are integrated into the enterprise locally on the y 2 Encapsulation and Objecti cation Legacy applications are adapted to a process of encapsulation and objecti cation A service-oriented adaptor stack provides the face to the enterprise This stack includes translation and security engines Modeling of information and process steps objecti es data and processes Objecti ed data is maintained locally to the application Con guration management is exerted locally mostly by application developers 3 Capture of Business Intelligence Business intelligence is captured in local applications and systems in a comprehensive fashion A local content repository is used to store and manage business intelligence Common local functions, such as troubleshooting and recovery, are selectively exported to the enterprise Con guration management is exercised at the system level by system integrators 4 Externalization of Business Intelligence Business intelligence is captured out of local applications and systems in reference to enterprise level models and forms and exported to an enterprise repository This enables asset mining by enterprise users Through enterprise modeling, all business intelligence is derived from comprehensive uni ed models, resulting in a model-driven architecture Con guration management is exercised at the enterprise level Process Engineering Process engineering is the practice of designing and documenting enterprise processes Done properly, it enables process improvement while maintaining enterprise operations At its most basic level, it involves development of the rules of interaction for the processes that comprise the enterprise and their enforcement These rules of interaction, which apply to processes as well as data, are similar to the business process rules of today, but only apply to process characteristics that impact the execution of other processes within the enterprise Rules that constrain the internal operations of a business process application are excluded from the category of rules of interaction Rules of interaction deal with formality in describing the outcomes of processes, not constraining how they work internally Their purpose is to provide a human and machine interpretable characterization of what the process does so that other process designers can accurately anticipate the result as they design their process
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