外文翻译---信息系统的业务趋势和后果(编辑修改稿)

外文翻译---信息系统的业务趋势和后果(编辑修改稿)内容摘要：

require d. The demand for Business Intelligence solutions that incorporate all of these features is immense. More recently, Business Intelligence systems and the underlying Data Warehouse ponents have been called on to perform both an analysis role and an operational reporting role, facilitating the need for nearrealtime data collection. Transactionorientated OLTP and analysisorientated OLAP environments must be considered a single entity. The data for the business processes produces a multitude of information that cannot easily be used for targeted analysis. Therefore, the source data is initially cleansed, then technically and semantically prepared (homogenized). From the analyses of this data es knowledge. This helps the organization define its business strategy and supports the business processes derived from it. Specific examples of Business Intelligence interfacing with OLTP appear in the following two scenarios: one for accounts payable and one for sales and marketing. Both of these scenarios leverage sophisticated Data Mining algorithms to automate and statistically quantify analysis results. In addition to slice and dice analytical tools, Data Mining (a part of SAP39。 s BI offering) done correctly adds still more petitive advantage. Note:BW380 covers SAP39。 s robust delivered Data Mining tool set, while CR900 covers the very tight interfaces between SAP BI and mySAP CRM. These include automation in the return of actionable knowledge to the CRM system via the Analysis Process Designer and many other tools and interfaces. 重庆 邮电 大学本科学生毕业设计 （论文）附件 附件 C：译文 C10 Business Intelligence and Data Warehousing:Definitions and Benefits Due to continuous innovation in data processing, more and more information is stored in a more detailed format. As a result, there is a need to both reduce and structure this data so it can be analyzed meaningfully. The analysis necessary to create .business intelligence. from the collected raw data requires a varied tool set. To set the stage, let’s first define business intelligence generically. In a Google search for business intelligence, attributed the term business intelligence to a September, 1996 Gartner Group report: Business intelligence (BI) is a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI applications include the activities of decision support systems, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and Data Mining. For the generic definition of a Data Warehouse, I think we need to give the credit to one of the gurus of Data Warehousing .Bill Inmon.. In 1990 Mr. Inmon defined a Data Warehouse as follows: In 1990, Bill Inmon defined a DataWarehouse: A warehouse is a subjectoriented, integrated, timevariant and nonvolatile collection of data in support of management39。 s decision making process . A more technical definition might be: the subset of a Business Intelligence tool set responsible for modeling, structuring, storing as well as extraction translation and loading (ETL) of the underlying data needed for analysis. So in summary, Business Intelligence software is the collection of applications needed to make sense of business data. The Data Warehouse, a ponent of this Business Intelligence tool set, is the more specific tool responsible for the cleanup, loading, and storage of the data needed by the business. Although we will address the overall BI tool set in the next lesson, this class focuses on the Data Warehouse ponent. A Data Warehouse can help to organize the data. It brings together all operative DataSources (these are mostly heterogeneous and have differing degrees of detail). The job of the warehouse is to provide this data in a usable form to the whole organization. The data can then be used for future requirements as the need arises. A warehouse has the following properties: 重庆 邮电 大学本科学生毕业设计 （论文）附件 附件 C：译文 C11 • Readonly access: Users have readonly access, meaning that the data is primarily loaded into the Data Warehouse via the extraction, transformation and loading (ETL) process. • Crossorganizational focus: DataSources from the entire organization (production, sales and distribution, controlling), and possibly external sources, make up the basis of the system. • Data Warehouse data is stored persistently over a particular time period. • Data is stored on a longterm basis. • Designed for efficient query processing: The technical environment and data structures are optimized to answer business questions . not to quickly store transactions. R. Kimball, another guru of Data Warehousing, defines a Data Warehouse as .A copy of transaction data, specially restructured for queries and analyses.. (The Data Warehouse Toolkit, 1996, page 310). Business Intelligence Systems Objectives A modern Business Intelligence system must meet the following requirements: Standardized structuring and display of all business information: Decision makers urgently need reliable information from the production, purchasing, sales and distribution, finance, and human resources departments. They require an uptodate and prehensive picture of each individual business area and of the business as a whole. This results in high demand being put on the data collection process from the underlying DataSources. The data is defined uniquely across the entire organization to avoid errors arising through varied definitions in different sources. Simple access to business information via a single point of entry: Information must be bined homogeneously and consistently at a。