利用SQL Server 2005构建企业智能解决方案

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《Delivering Business Intelligence with Microsoft SQL Server 2005》是一本由Brian Larson编著，由McGraw-Hill/Osborne出版于2006年的IT专业书籍，共792页，ISBN号为0072260904。该书专注于讲解如何利用Microsoft SQL Server 2005进行商业智能(Business Intelligence, BI)的开发、分析和报告，旨在帮助读者为企业提供竞争优势。本书分为两个主要部分： **Part I - 商业智能的实施** 1. **第一章：装备组织有效决策** - 强调组织内部如何为高效决策做好准备，包括构建合适的文化和基础设施。 2. **第二章：充分利用现有资源** - 提供如何运用商业智能工具和技术，最大化现有数据的价值。 3. **第三章：寻找情报来源** - 探讨数据源的识别与选择，为BI项目奠定基础。 4. **第四章：一站式解决方案** - 介绍统一维度模型，如何整合数据以支持全面的分析。 5. **第五章：开发起步** - 开始指导读者如何制定和执行BI项目的初始步骤。 **Part II - 定义和构建BI结构** 6. **第六章：建立根基** - 阐述如何创建和填充数据仓库（Data Mart），这是BI系统的核心组件。 7. **第七章：集成与充实** - 解释如何通过数据集成技术增强数据的可用性和质量，以便于后续分析。书中详细地展示了如何一步步地使用SQL Server 2005的各项功能，包括查询优化、数据清洗、数据仓库设计、OLAP（Online Analytical Processing）工具、报表和仪表板的设计以及如何确保信息的有效分发。对于IT专业人士来说，这本书是深入理解并实践商业智能应用在SQL Server环境中的宝贵指南，有助于企业在竞争激烈的市场环境中做出明智的决策并提升运营效率。

What Business Intelligence Can Do For You

In Chapter 1, we saw how business intelligence is used to support effective decision making. It provides

foundational information on which to base a decision. Business intelligence also provides us with feedback

information that can be used to evaluate a decision. It can provide that foundational and feedback information in

a number of different ways.

When We know What We Are Looking For

In some cases, we know what information we are looking for. We have a set of particular questions we want

answered. What is the dollar amount of the sales or services our organization is providing in each region? Who

are our top salespeople? In some of these situations, we not only know what we are looking for, but we also

have a good idea where to find the information when we design the business intelligence solution.

Layout-Led Discovery

When we know the question we want answered and have a good idea where that answer is going to be found,

we can use printed reports to deliver our business intelligence. This is the most common form of business

intelligence and one we are all familiar with. For many situations, this format works well.

For example, if we want to know the dollar amount of the sales or services provided in each region, we know

where to find this information. We can design a report to retrieve the information and the report will consistently

deliver what we need. The report serves as an effective business intelligence tool.

This is an example of layout-led discovery. With layout-led discovery, we can only learn information that the

report designer thought to put in the report layout when it was first designed. If the information wasn't included

at design time, we have no way to access it at the time the report is read.

Suppose our report shows the dollar amount for a given region to be unusually low. If the report designer did

not include the supporting detail for that region, we have no way to drill into the region and determine the cause

of the anomaly. Perhaps a top salesperson moved to another region. Maybe we have lost a key client. The

report won't give us that information. We quickly come to a dead end.

Data-Led Discovery

In some cases, we know the question, but we don't know exactly where to look for our answer. This often

occurs when the information we initially receive changes the question slightly. As in the example from the

previous section, an anomaly in the information may cause us to want to look at the data in a slightly different

way. The unusually low dollar amount for sales or services provided in a specific region led us to want detailed

numbers within that region.

In other cases, we know where to look, but it is not practical to search through all of the detailed information.

Instead, we want to start at an upper level, find a number that looks interesting, and then drill to more detail. We

want to follow the data that catches our attention to see where it leads.

This is data-led discovery: the information we find determines where we want to go next. The developer of this

type of solution cannot know everywhere the report user may want to go. Instead, the developer must provide

an interactive environment that enables the user to navigate at will.

To implement data-led discovery, we need some type of drilldown mechanism. When we see something that

looks interesting, we need to be able to click on that item and access the next level of detail. This is, of course,

not going to happen on a sheet of paper. Data-led discovery must be done online.

Discovering New Questions and Their Answers

In some cases, our data may hold answers to questions we have not even thought to ask. The data may

contain trends, correlations, and dependencies at a detail level, which would be impossible for a human being to

notice using either layout-led or data-led discovery. These relationships can be discovered by the computer

using data mining techniques.

Figure 2-3: Timing of the foundation and feedback information at each level of the organization

The Top of the Pyramid

Decision makers at the upper levels of our organizations are charged with looking at the big picture. They are

charged with setting long-term goals for the organization. Decision makers need to have a broad overview of

their area of responsibility and not get caught up in the minutiae.

Highly Summarized Measures

The business intelligence utilized at this level needs to match these characteristics. The measures delivered to

these decision makers must be highly summarized. In many cases, each measure is represented, not by a

number, but by a status indicator showing whether the measure is in an acceptable range, is starting to lag, or

is in an unacceptable range. These highly summarized measures are known as Key Performance Indicators.

Definition

Key Performance Indicators (KPIs) are highly summarized measures designed to quickly

relay the status of that measure. They usually reflect the most vital aspects of the

organization.

KPIs are used to provide these high-level decision makers with a quick way to determine the health of the

essential aspects of the organization. KPIs are often presented as a graphical icon, such as a traffic light or a

gauge, designed to convey the indicator's status at a glance. We discuss KPIs in greater detail in Chapter 9 of

this book.

Higher Latency

Because these upper-level decision makers are dealing in long-term policies and direction, they do not need up-

to-the-minute business intelligence. Another way to state this is to say they can have more latency in their

business intelligence. These decision makers need to see downward trends in time to make corrections. They

do not need to see the daily blips in the organization's operation.

Definition

The latency of business intelligence is the amount of time between the occurrence of a

transaction and the loading of that transaction's information into the business intelligence

system.

Mid-Level

Mid-level decision makers are managing the operation of departments and other working units within the

organization. They are setting short-term goals and doing the planning for the functioning of these areas. Mid-

level decision makers are still at a level where they should not be in the details of day-to-day processes.

Summarized Measures with Drilldown

These mid-level decision makers need business intelligence that is still summarized, but they often need to drill

down into this information to get at more detail. Therefore, these decision makers can make use of printed

reports along with interactive systems allowing data-led discovery. These decision makers can also make use of

information from data mining.

Some Latency Acceptable

Because these decision makers are closer to the everyday functions, they may require business intelligence

with less latency. In some cases, they may need to see measures that are updated daily. In other cases, these

decision makers are looking for trends discernable from weekly or monthly loads.

The Broad Base

At the broad base of our business intelligence pyramid are the forepersons, managers, and group leaders

taking care of daily operations. These people are setting daily operational goals and making decisions on

resource allocation for the next week, the next day, or perhaps the next shift. They are planning the next sales

campaign or maybe just the next sales call. These decision makers usually need business intelligence systems

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