信息管理与信息系统专业毕业论文中英文译文

信息系统的业务趋势和后果

到目前为止，实施典型数据处理系统的目标主要在于加快各个业务领域的发展速度、降低成本和使流程自动化。目前，企业资源规划 (ERP) 系统和其他软件工具的作用就是为大多数公司实现这些目标。结果，这些 ERP系统、客户关系管理(CRM) 系统、银行和信用卡系统以及公司管理规定使需要分析的数据量呈指数增长。有些公司认为这是负面影响；而另一些公司，如 SAP，却将这巨大的电子信息量看作是一大笔财富。同样的，随着不断增长的全球化发展和日益增加的分散经营模式，认清市场趋势和收集竞争者信息已成为一大需求。这允许公司迅速地应对市场条件的变化。你可以看出在这个网络时代，有效的信息处理已成为在竞争者间维持优势的决定性因素。

现代化全球经营企业里的决策者常常会意识到他们的生存取决于信息的有效使用。遗憾的是，此信息通常分布于许多系统，有时甚至分布于许多国家，从而使信息的有效使用变得极其困难。而这恰恰是现代商务智能（BI）系统试图应对的挑战。若要涵盖从源数据检索到分析的整个流程，则需要广泛的解决方案。各企业必须关注整个企业内作为仓库构建核心的元数据（业务和技术属性以及对象描述）。另外，他们在区分集合程度时需要整合和创建同类全局主数据以及大量的交易数据。

分析师现在询问的问题要比二十年前的问题复杂得多。这是因为他们知道有数据可以解答这些问题。

鉴于上述问题，信息系统需要满足下列由决策者规定的要求：

?直接单点访问所有相关信息，而不考虑信息的来源

?涵盖所有业务流程：跨系统和跨流程分析变得日益重要

?高质量信息，这不仅指数据内容，还包括灵活评估数据的能力

?高质量的决策支持：BI 系统必须支持运营和战略管理的需求；只有这样才能充分支持决策

?实施时间短、所需资源少：为快速进行实施，数据仓库必须启用对相关数据的简单而快速的访问，避免为准备不同数据而耗费大量的劳动力

在不同系统布局中，提取和准备来自于 mySAP 商务套件应用程序和其他提供商的源系统中的整合交易数据和主数据是一个特别的挑战。高质量业务信息需求的日益增长意味着除集成数据收集流程外，还需要详细的数据分析和多媒体演示选项。对合并所有这些功能的商务智能解决方案的需求是巨大的。最近，在履行

分析角色和运营报表角色时已需要访问商务智能系统和基本的数据仓库组件，从而便于满足对准实时数据收集的需求。

面向事务的联机事务处理（OLTP）和面向分析的联机分析处理（OLAP）环境必须被视为单个实体。业务流程数据会产生大量无法轻易用于目标分析的信息。因此，首先清除源数据，然后从技术和语义上准备这些数据（均匀化）。从此数据的分析中获取知识。这有助于企业定义其业务策略和支持从中派生的业务流程。

与 OLTP 连接的商务智能特定示例出现在以下两种业务情景中：一种针对应付帐，另一种针对销售和营销。这两种业务情景均利用复杂的数据挖掘算法来自动操作和从统计上量化分析结果。除逐步细化分析工具外，正确操作的数据挖掘（属于 SAP BI 产品）还会增加更多竞争优势。

注意: BW380 包含 SAP 强力推出的数据挖掘工具集，而 CR900则包含 SAP BI 和 mySAP CRM 之间的极紧密接口。这些包括通过分析流程设计器和许多其他工具和接口将 CRM 系统的可行动知识转换为自动化。

商业智能和数据仓储：定义和好处

由于数据处理技术的不断创新，越来越多的信息以更详尽的格式来存储。因此，需要在减少数据的同时对其进行结构化，这样数据分析才变得有意义。根据收集的原始数据创建“商业智能”所必需的分析需要各种各样的工具集。

若要设置该阶段，首先让我们来定义一般意义上的商业之恩给你。在谷歌中搜索商业智能，https://www.360docs.net/doc/527871975.html,/ 网站上的《1996 年9 月Gartner Group报表》中解释了这一术语，具体定义如下：

“业务智能(BI) 是指广义范围上，用于收集、存储、分析数据，并提供对数据的访问，以便帮助企业用户更好地制定业务决策的应用程序和技术。BI 应用程序包括决策支持系统的活动、查询和报表、联机分析处理(OLAP)、统计分析、预测和数据挖掘。”

对于广义的数据仓库，我认为我们要归功一位研究数据仓储技术的大师“比尔·艾莫”。1990 年，艾莫先生为“数据仓库”提供了如下定义：1990 年，比尔·艾莫给出了“数据仓库”的定义：“仓库是以主题为导向，是与时间相关的非变化、集成式数据集合，可以为管理层制定决策时提供支持。”

更有技术含量的定义可能是：商业智能工具集的子集，负责对分析所需要的基本数据进行建模、结构化、存储，并执行提取、转换和加载(ETL) 。

因此，商业智能软件总的来说是使业务数据变得有意义所必需的应用程序集合。数据仓库是此商业智能工具集的一个组件，是更专业地负责清除、加载和存

储企业所需数据的工具。尽管我们在下一章才介绍全套BI工具集，但这一章的重点还是放在数据仓库组件上。

数据仓库可以有助于组织数据。它会将所有运营数据源（它们大多属于不同系统，详尽程度有所不同）结合在一起。仓库的工作是以实用形式向整个组织提供此数据。然后，可以在将来产生需求时使用该数据。

仓库具有如下属性：

?只读访问：用户具有只读访问权限，这意味着主要通过提取、转换和加载(ETL) 流程将数据加载到数据仓库中。

?跨组织焦点：整个组织（生产、销售和分销、成本控制）中的数据源和可能存在的外部源构成系统的基础。

?数据仓库数据始终会存储一定时期。

?数据可长期存储。

?为高效查询处理而设计：对技术环境和数据结构进行优化是为了解决业务问题，而不是为了快速地存储交易。

另一位研究数据仓储技术的大师金伯尔将“数据仓库”定义为“交易数据的副本，特别为查询和分析而重组结构。”（数据仓库工具，1996 年版，第310 页）。

商业智能系统目标

现代商业智能系统满足以下要求：

对所有业务信息进行标准化构造和显示：决策者急需来自生产、采购、销售和分销、财务和人力资源部门的可靠信息。他们需要对每个业务范围和企业整体有一个最新的全面了解。这导致了对收集基本数据源数据这一流程的高需求。在整个组织内单独定义该数据，以避免其他源中的不同定义导致错误。

通过单点输入简单访问业务信息：信息必须在可调用的中心点按同类和一致性组合在一起。因此，现代的数据仓库通常需要一个单独的数据库。此数据库启用独立的应用环境来提供所需服务。

用于对所有领域进行自我分析的高度发展的报表体系：就演示而言，有效的分析和富含意义的多媒体可视化技术十分关键。系统必须能够处理多个用户组的信息需求。

快速而高效的实施：在实施数据仓库时，有影响力的成本因子是数据仓库与OLTP 系统的集成及不同数据的直接加载。除强大的元数据管理功能外，此处推出的基于业务的商业智能内容还担当着重要角色。

高性能环境。不同源的数据建模：如果不集成不同的源，则无法通过数据仓库执行数据分析。这通常会在读取数据时浪费大量时间。计划工具对于允许在性能友好时间内以单独的批作业加载数据是必需的。

减轻OLTP 系统的负载：过去，OLTP 系统由于需要同时存储和分析数据而严重超载。现在，单独的数据仓库服务器允许您在其他地方执行数据分析。

BI/数据仓库系统和OLTP 系统之间的区别

?详细级别：OLTP 层存储详细级别非常高的数据，而数据仓库中的数据则为了在访问时实现高性能而进行了压缩（集合）。

?历史记录：在OLTP 领域内归档数据意味着其存储的历史记录最少。而数据仓库范围需要全面的历史数据。

?可更改性：数据的频繁更改是运营范围的一大特色，而数据仓库中的数据会在特定点后冻结以进行分析。

?集成：与OLTP 环境不同，对全面和集成的信息的要求非常高。

?标准化：由于减少了数据冗余，运行使用的标准化程度非常高。数据加载和较低性能是数据仓库中标准化程度较低的原因。

?读取访问：针对读取访问优化OLAP 环境。运营应用程序（和用户）也需要定期执行包括更改、插入和删除在内的其他功能。

OLTP 系统和数据仓库/BI (OLAP) 系统的需求存在着根本的区别。因此，从OLTP 系统中技术性地区分所有对数据仓库的集合式、与报表相关的需求是最有利的。

注意:技术和特定的业务案例的发展会混淆OLTP 分析工具和OLAP（BI 工具）之间的界线。例如，BI 具有准实时提取工具和SAP企业资源管理计划中心主件(SAP ECC)，它们可以针对小型公司和特殊情况在同等条件下与BI 环境一起安装。

SAP 平台商务智能：最先进的 BI 软件

作为 SAP 平台的核心组件，BI 提供数据仓储功能、商务智能平台和一套商务智能工具，所有这些能确保企业最大价值地利用他们所收集的信息。BI 中可以集成、转换和整合来自 SAP 应用程序和所有外部数据源的相关业务信息。BI 提供灵活的报表和分析工具以支持您评估和说明数据，并为数据分发提供便利。企业能根据此分析制定出完善的决策，并确定以目标为导向的行动。

BI 套件/商务探测器 (BEx)

包含BEx 的BI 套件提供针对超级用户和最终用户的灵活的报表和分析工具。您可以使用这些工具进行战略分析，并用来支持企业中的决策过程。这些工具包括查询、报表和分析功能。BEx确保广泛用户能使用 SAP 平台入口、企业内部网/互联网（网络应用程序设计）或移动设备（WAP 或 i 模式启用的移动电话和个人数字助理）访问 BI 信息。许多分析功能都是可用的；逐步细化（重点功能）只不过是一个开始。另外，还支持许多输出选项，包括格式化的微软表格、网络主控室、格式化的网络输出（BEx 报表）和 Adobe PDF 文档。

BI 数据库可分成独立的业务信息提供者。在 BEx 查询设计器中根据这些信息提供者定义查询可分析 BI 的数据库。通过选择合并查询中的特性和关键值或可重用结构，您可以确定用来分析所选信息提供者中数据的方式。

基于多维数据源（OLAP 报表）的数据分析允许您同时分析信息提供者的多个维度（例如时间、地点和产品）。这意味着您可以进行任意次数的差异分析（计划/实际比较和经营年度比较）。将类似于主表方式显示的数据作为详细分析的起点，并可用来回答无数问题。无数的交互选项，如排序、筛选、互换特性、重新计算值等，允许您在运行时间灵活地在数据中进行导航。您可以用图形（例如条形图或饼图）来使数据形象化，还可以在地图上按地理范围（针对客户、销售区域和国家这样的特征）评估数据。此外，您还可以使用例外报表来确定特殊情况和重要计量临界值。当符合这些临界值时，信息广播会自动将有关这些问题的消息通过电子邮件或短信服务（SMS）发送到知识管理资源库，通过门户可访问这些消息。

您可以在 BEx 中分析以下领域中的数据：

? BEx 分析器（基于微软表格的分析工具，具有类似于主表的功能）

? BEx 分析器（基于网络的分析工具，具有类似于主表的功能）

? BEx 网络应用设计器（由客户定义并由 SAP BI 内容提供）

? BEx 报表设计器（高度格式化的网络输出）

微软表格和网络区域都是无缝集成的。也就是说，您可以在网络浏览器中以标准视图显示 BEx 分析器中的查询，或者可以通过单击显示以表格格式呈现的网络页数据。

BEx 网络应用设计器

BEx网络应用设计器允许您在网络应用程序和 BI主控室中对简单和高度独立的业务情景实现复杂的 OLAP 导航。这些方案可以使用客户定义的界面要素来创建，而这些界面要素采用标准修饰语言和网络设计 API。网络应用设计器包含大量基于网络的交互式 BI业务情景，您可以使用标准网络技术修改这些方案以满足您的需求。

您可以使用 BEx网络应用设计器（一款用于创建网络应用程序的桌面应用程序）来生成包含 BI 特定内容（如各种表、图表或地图）的 HTML 页面。您可以将网络应用程序另存为 URL，并通过英特网、企业内部网或移动设备来访问它们。您还可以将网络应用程序另存为iView，并将它们集成到企业门户中。

在创建网络应用程序时，已将作为助手的 Web 应用程序向导集成到网络应用设计器。它采用自动化的逐步程序和简化的设计流程。

企业报表

BI 中可以有几种方式实现带有定位控制和显示格式的企业报表（格式化报表）。BEx 分析器的功能是允许使用定制的、高度格式化的表格工作簿，而 BEx 报表设计器则对网络输出或文档转换为 PDF 执行同样操作。万一这些选项不能满足您的需求，则第三方工具可以轻松访问BI 物理数据或物理驻留在其他系统的数据。

信息广播

信息广播提供了一种在所需时间按所需频率执行分析的工具集（BEx 网络、BEx 分析器、工作簿和查询），然后将结果分配给指定收件人。该分配可以通过例外临界值触发，也可以通过基于网络的用户界面（UI）来计划。

BI 平台

BI 平台层包含支持复杂分析任务和功能的 BI 服务。它包含分析引擎，该分析引擎处理通过 BEx 分析导航申请的数据，并支持允许输入和操作数据的界面（属于 BI 集成计划）。最后，诸如分析过程设计器(APD) 和数据挖掘之类的特定分析工具向公司分析师提供合并、挖掘、预处理、存储和分析数据的工具，且不需要技术团队的支持。

注意: 新的公司管理准则（如美国的萨班斯-奥克斯利法案）不赞成创建非受控数据。APD 允许分析师操作数据（例如，他们会在 Microsoft Excel 和 Access 已执行这一操作）并将其保留在仓库之中。

移动报表

您可以使用 BEx移动智能来调用通过网络应用设计器创建的 Web 应用程序。您甚至可以在任何远离办公室的时候执行此操作。支持以下设备：

?装有 Windows CE 3.0 和 Pocket Internet Explorer 的个人数字

助理 (PDA)

? WAP 启用的移动电话

? i 模式启用的移动电话

?装有 EPOC32 操作系统的移动设备（例如诺基亚聊天器9210）

SAP 平台BI：数据仓库层

数据仓库层是本课的主题，其概览将在下一节课中介绍。简而言之，仓库负责清除、加载、存储和管理企业所需数据。

您现在已经掌握了基础知识，但还有一个要点应该注意。与其他 BI 解决方案提供商不同，SAP 为您提供了强大的已交付 BI内容。借助于 BI 内容，SAP 根据一致的元数据交付预配置的基于角色和任务的信息模型与报表业务情景。BI 内容为公司中的选定角色提供他们执行其任务所需要的信息。已交付的信息模型涵盖所有业务范围，并集成几乎所有SAP 应用程序和选定外部应用程序中的内容。在BI 项目中，确定用户需求并设计提取程序是两件最棘手的事情。借助 BI 内容，我们通过网络或 Excel不仅提供这些内容，而且还提供数据库模式、查询和输出，这能满足典型项目 60% 到 90% 的需求。

译文原文出处：SAP.BI-Enterprise Data Warehousing[M]第3页-第17页.HP.2008。

Business Trends and Consequences for Information Systems Until now, the goal behind the implementation of classic data processing systems has primarily been the acceleration, cost reduction, and automation of processes in individual business areas. Enterprise Resource Planning (ERP) systems and other software tools now do this in most companies. The result is that these ERP systems, CRM systems, banking and credit card systems, and Corporate Governance regulations have exponentially increased data volumes needing analysis. Some consider this a negative; others, like SAP, think that this enormous amount of electronic information is a huge benefit. In parallel, ever-increasing globalization and, at the same time, the increasing decentralization of organizations has created the need to recognize market trends and to collect information about competitors. This allows the company to swiftly react to changes in market conditions. You can see that in this Internet age, efficient information processing is a decisive factor in maintaining an advantage over one's competitors.

Decision makers in modern, globally operating enterprises frequently realize that their survival depends on the effective use of this information. Unfortunately this information is often spread across many systems and sometimes many countries, thus making effective use of information extremely difficult. This is precisely the challenge that modern Business Intelligence systems attempt to meet. Extensive solutions are required to cover the entire process, from the retrieval of source data to its analysis. Enterprises must be concerned with metadata (business and technical attributes and descriptions of objects) across the enterprise as the core in building a warehouse. In addition, they need to consolidate and create homogenous global master data, as well massive amounts of transaction data in differing degrees of aggregation. The questions that analysts are asking now are much more sophisticated than those asked 20 years ago. This is because they know the data exists to answer these questions.

As a result of the issues described above, information systems need to meet the following requirements made by decision makers:

?Immediate, single-point access to all relevant information, regardless of source ?Coverage of all business processes: cross-system and cross-process analyses are becoming increasingly important

? High quality of information, not only in terms of data content, but also in terms of the ability to flexibly evaluate data

? High-quality decision-making support: The BI system must support the requirements of both operative and strategic management; only then is it possible to support decisions fully

?Short implementation time with less resources: As well being quick to implement, a Data Warehouse must enable simple and quick access to relevant data, avoiding the labor-intensive preparation of heterogeneous data

In heterogeneous system landscapes, a particular challenge lies in the extraction and preparation of consolidated transaction data and master data from mySAP Business Suite applications and source systems from other providers. The increasing demand for high-quality business information means that in addition to an integrated data collection process, detailed data analysis and multimedia presentation options are also 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 components have been called on to perform both an analysis role and an operational reporting role, facilitating the need for near-real-time data collection. Transaction-orientated OLTP and analysis-orientated 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 comes 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 SAP's BI offering) done correctly adds still more competitive advantage.

Note:BW380 covers SAP'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.

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, https://www.360docs.net/doc/527871975.html,/ 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 subject-oriented, integrated, time-variant and non-volatile collection of data in support of management'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 component 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 component.

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:

?Read-only access: Users have read-only access, meaning that the data is primarily loaded into the Data Warehouse via the extraction, transformation and loading (ETL) process.

?Cross-organizational 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 long-term 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 up-to-date and comprehensive 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 combined homogeneously and consistently at a central point from which it can be called up. For this reason, modern Data Warehouses usually require a separate database. This database enables a standalone application environment to provide the required services.

Highly developed reporting for analysis with self service for all areas: In terms of presentation, efficient analysis and meaningful multimedia visualization techniques are essential. The system must be able to cope with the information needs of varied user groups.

Quick and cost-efficient implementation: When implementing the Data Warehouse, an influential cost factor is its integration into an OLTP system and the straightforward

loading of heterogeneous data. Alongside robust metadata management, delivered business-based Business Intelligence content also has an important role here.

High performance environment. Data modeling from heterogeneous sources:

Data analyses can not be carried out via Data Warehouse without integrating heterogeneous sources. This is usually done with time-consuming read processes. Scheduling tools are necessary to allow the data to be loaded in separate batch jobs at performance-friendly times.

Relieving OLTP systems: In the past, OLTP systems were strongly overloaded by having to store data and analyze it at the same time. A separate Data Warehouse server now allows you to carry out data analysis elsewhere.

Differences Between a BI/Data Warehouse System and an OLTP System

?Level of detail: The OLTP layer stores data with a very high level of detail, whereas data in the Data Warehouse is compressed for high-performance access (aggregation). ? History: Archiving data in the OLTP area means it is stored with minimal history. The Data Warehouse area requires comprehensive historical data.

?Changeability: Frequent data changes are a feature of the operative area, while in the Data Warehouse, the data is frozen after a certain point for analysis.

?Integration: In contrast to the OLTP environment, requests for comprehensive, integrated information for analysis is are very high.

? Normalization: Due to the reduction in data redundancy, normalization is very high for operative use. Data staging and lower performance are the reasons why there is less normalization in the Data Warehouse.

?Read access: An OLAP environment is optimized for read access. Operative applications (and users ) also need to carry out additional functions regularly, including change, insert, and delete.

There are fundamentally different demands on an OLTP system compared with a Data Warehouse/ BI (OLAP) system.

It is therefore most advantageous to technically separate all aggregated reporting-related demands made on the Data Warehouse from the OLTP system.

Note: Developments in technology and specific business cases are blurring the lines between OLTP analysis tools and OLAP (BI tools). BI, for instance, has near-real-time extraction tools, and SAP ERP Central Component (SAP ECC). can be installed along with the BI environment in the same box for smaller companies and special situations.

SAP NetWeaver Business Intelligence: State-of-the-Art BI Software

As a core component of SAP Net Weaver, BI provides Data Warehousing functionality, a Business Intelligence plat form, and a suite of Business Intelligence tools that enable businesses to attain the maximum value from the information they collect. Relevant business information from productive SAP applications and all external Data Sources can be integrated, transformed, and consolidated in BI. BI provides flexible reporting and analysis tools to support you in evaluating and interpreting data, as well as facilitating its distribution. Businesses are able to make well-founded decisions and deter mine target-orientated activities on the basis of this analysis.

BI Suite/Business Explorer (BEx)

The BI Suite containing the Business Explorer (BEx) provides flexible reporting and analysis tools targeted at both power users and end users. You can use these tools for strategic analysis and to support the decision-making process in your organization. These tools include query, reporting, and analysis functions. BEx enables a broad range of users to access BI information using the SAP NetWeaver Portal, intranet/Internet (Web Application Design), or mobile devices (WAP or i-mode-enabled mobile telephones and personal digital assistants). Many analysis features are available; slice and dice (pivot like functions) is only the beginning. In addition, many outputs options are supported, including formatted Microsoft Excel, Web cockpits, formatted Web output (BEx Reports) and Adobe PDF documents.

The BI database is divided into self-contained business information providers (InfoProviders). You analyze the database of BI by defining queries against these InfoProviders in the BEx Query Designer. You can determine the way in which the data from your chosen InfoProvider is analyzed by selecting and combining characteristics and key figures or reusable structures in a query.

Data analysis based on multidimensional Data Sources (OLAP reporting) allows you to analyze more than one dimension of an InfoProvider (for example, time, place, and product) at the same time. This means that you can make any number of variance analyses (plan/actual comparison and business year comparison). The data, which is displayed in a manner similar to a pivot table, serves as the starting point for a detailed analysis, and can be used to answer a myriad of questions.

Numerous interaction options ?such as sorting, filtering, swapping characteristics, recalculating values, and so on ----allow you to flexibly navigate in the data at runtime.

You can visualize the data in graphics (bar or pie charts, for example) and you can also evaluate data geographically (for characteristics such as customer, sales region, and country) on a map. Moreover, you can use exception reporting to determine special situations and critical measurement thresholds. When these thresholds are met, Information Broadcasting can automatically send messages about these issues via e-mail or SMS (short message service) or to the Knowledge Management repositories with access to it from the portal.

You can analyze data in the following areas in the Business Explorer:

? BEx Analyzer (Microsoft Excel-based analysis tool with pivot-table-like

features)

?BEx Web Analyzer (Web-based analysis tool with pivot-table-like features)

?BEx Web Application Designer (customer-defined and SAP BI Content provided) ?BEx Report Designer (highly formatted Web output)

Both the Microsoft Excel and Web areas are seamlessly integrated. In other words, you can display queries from the BEx Analyzer in a standard view in the Web browser or you can display the Excel rendering of the data from a Web page with a single click. BEx Web Application Designer

The BEx Web Application Designer allows you to implement complex OLAP navigation in Web applications and in Business Intelligence cockpits for both simple and highly individual scenarios. These scenarios can be created using customer-defined interface elements using standard markup languages and Web design APIs. The Web Application Designer encompasses a wide spectrum of interactive Web-based Business Intelligence scenarios that you can modify to suit your requirements using standard Web technology.

You can use the BEx Web Application Designer, the desktop application for creating Web applications, to generate HTML pages that contain BI-specific content such as various tables, charts, or maps. You can save the Web applications as URLs and access them from the Internet, intranet, or mobile devices. You can also save Web applications as iViews and integrate them into an enterprise portal.

An assistant, the Web Application Wizard, has been integrated into the Web Application Designer to support you when creating Web applications. It uses an automatic step-by-step procedure and a simplified design process.

Enterprise Reporting

Enterprise reporting (formatted repor ting) with positioning control and display

formatting can be accomplished in sev eral ways in BI. Features of the BEx Analyzer allow for customized, highly formatted Excel workbooks, while the BEx Report Designer does the same for Web output or conversion of the document to PDF. In the unlikely event that these optio ns do not meet your needs, third-party tools can easily access physical BI data or data residing physically on other systems.

Information Broadcasting

Information broadcasting provides a tool set to execute analyses (BEx Web, BEx Analyzer, workbooks, and queries) at a desired time and frequency, then distribute the results to intended recipients. The distribution can be exception-threshold-triggered and can be scheduled via a Web-based UI.

The BI Platform

The BI platform layer contains BI services to support complex analysis tasks and functions. It contains the Analytic Engine, which processes the data requested though BEx analysis navigations and supports the interface that allows for the entry and manipulation of data as part of BI Integrated Planning. Finally, special analysis tools such as the Analysis Process Designer (APD) and the Data Mining provide the analysts at your company with the tools to merge, mine, preprocess, store, and analyze data without support from your technical team.

Note: New corporate governance rules, such as the Sarbanes-Oxley Act in the United States, frown on the creation of uncontrolled data. The APD lets analysts manipulate the data (like they would have done in Microsoft Excel and Access) and keep it in the warehouse.

Mobile Reporting

You can use BEx Mobile Intelligence to call up the Web applications you created with the Web Application Designer. You can even do this when away from your desk. The following devices are supported:

? Personal digital assistant (PDA) with Windows CE 3.0 and Pocket Internet

Explorer

?WAP-enabled mobile telephone

? i-Mode-enabled mobile telephone

?Mobile device with EPOC32 operating system (the Nokia Communicator

9210, for example)

SAP NetWeaver Business Intelligence: Data Warehouse Layer

The Data Warehouse layer is the subject of this class, and its overview will be in the

lesson that follows. Briefly, the warehouse is responsible for the cleansing, loading, storage, and management of the data needed for the enterprise.

You now have the basics, but one other m ajor point should be made. SAP, unlike other providers of BI solutions, provides you with robust, delivered BI content. With BI Content, SAP delivers preconfigured role- and task-based information models and reporting scenarios that are based on consistent metadata. BI Content gives selected roles in a company the information they need to carry out their tasks. The information models delivered cover all business areas and integrate content from almost all SAP applications an d selected external applications. In BI projects, determininguser requirements and then designing extraction programs are the two hardest things. With BI Content we provide these as well as database schemas, queries, and outputs via the Web or Excel for 60% to 90% of a typical project's requirements.