原创于2009年01月13日，2009年10月19日迁移至此。
The Changing Face of Business Intelligence
正在改变中的BI
的脸
之所以翻译该文章，首先是因为好久没有关注BI
领域了，本来一直说不清BI
和数据仓库究竟为何物；其次是今天到ttnn
上看了一下，发现Qing
在谈论一篇关于BI
新观点的文章——你看，BI
的脸偷偷地在改变
,
感觉有点兴趣；最后是想挑战一下自己的E
文，毕竟好久没有安心去看过翻过了。
翻了2
页发现实在太累，就通过google
的在线翻译，偷懒翻了一下发现还确实不错，最后自己在把相应的语法和词汇调整一下，呵呵。
一连翻译了好几天了，算是告以段落了，虽然翻译的狗屁不通；不过在我看来文章没有太多的创意，在作者看来以往更多的强调是技术和业务的统一性，现在更加强调了业务中心论，即更加关注业务、分析、分析师的作用，具体怎么做，好像也没有给出明确的答案。
现在在忙一个数据中心的项目，其中也牵涉到数据集成和数据转换，当然和数据仓库、BI
还不是一个层面上的东西，不过反过来也可以从另外一个层面去思考一下BI
，希望到项目完结的时候，有机会再回过头来总结一下吧。
好了不说了，还是多关注一下目前前沿的概念吧。
原文地址：http://www.b-eye-network.com/view/9007
Published: November 18, 2008
Dave Wells predicts that the next evolution of business
intelligence will happen soon, it will happen quickly, and it will expose and
overcome the self-delusion that is part of business analytics today.
Dave
预测商业智能
的下一轮变革即将到来，而且将会很快发生，同时这将会揭露和克服商业智能的自欺情况，这就是今天商业分析的一部分现状。
Howard Dresner originated the term business intelligence. In the early
1990s, he defined businenss intelligence (BI) as “a set of concepts and
methodologies to improve decision making in business through use of facts and
fact-based systems.” While many of us focused on data
integration and believed
that data warehousing was leading-edge, Dresner created the vision that shaped
what we know as business intelligence today.
Howard Dresner
发明了商业智能这个术语。在1990
年代初期，他把商业智能（BI
）定义为“通过事实或急于事实表的系统用于提高商业决策支持的一套完整的概念和方法论”。当我们大多数人把精力还集中在数据集成并且相信数据仓库是前沿的时候，Dresner
创建了这个版本，这也就是我们所知道的商业智能概念。
The business intelligence that was once visionary is now commonplace, but
sometimes disappointing. Tomorrow’s business intelligence must become something
very different. Too much of today’sbusiness analytics
has little
connection with realbusiness analysis
. At times I am tempted to declare
that “the emperor has no clothes.”
一度令人认为是空想的商业智能现在已经普及了，但是有些时候令人失望。将来的商业智能必须变得与现在与众不同。今天的业务分析与真正的业务分析几乎没有任何联系。有时候我甚至想宣布这是“皇帝的新装”。
But I believe that a significant BI shift is about to occur. Conditions
are aligned to drive change. Economic factors demand smart business. New
expectations for corporate and executive accountability raise the stakes.
Consolidation of the BI tools market opens the door to new and innovative
vendors. The next evolution of BI will happen soon, it will happen quickly, and
it will expose and overcome the self-delusion that is part of business
analytics today.
但我认为，一个重要的商业智能的转变即将发生。条件是相一致的驱动器的变化。经济
因素的智能业务的需求。新的期望企业
和行政问责制提高赌注。巩固BI
工具 市场打开了大门，以新的和创新的供应商。下一代的商业智能会发生不久，它将很快发生，这将揭露和克服自我欺骗的一部分，这是商业分析今天
但是我认为一个重要的商业智能的转变即将发生。环境将驱动变革。经济因素需要更加智能的业务。对企业和行政问责制的期望也促使这种赌注的提高。BI
工具市场的巩固也为新的和创建的相关厂商打开了大门。商业智能的下一轮变革即将到来，而且将会很快发生，同时这将会揭露和克服商业智能的自欺情况，这就是今天商业分析的一部分现状。
In recent years, we have strayed from Dresner’s early vision. Current
definitions describe business intelligence largely as tools and technology.
Some fail to mentionbusiness
and others include it almost as an
afterthought. The next evolution of BI must return to the vision, enrich that
vision and expand upon it to create opportunity for truly intelligent business.
The next developments in business intelligence will occur in five significant
areas:
近年来，我们已经逐渐偏离Dresner
的早期版本。目前的定义描述商业智能主要是工具和技术。不能不提到的一些商业和其他包括几乎视为事后的想法。下一代的BI
必须重新回到这个设想，重新丰富这个设想和并能够扩展它，以为真正的智能业务创建机会。在未来的商业智能发展将发生在5
个重要领域：
- Compelling
definition - 令人信服的定义
- Focus on
business analytics - 关注业务分析
- Closing the
gap between analytics and analysis - 结束分析和分析师之间的差距
- Focus on business
analysts - 关注业务分析师
- Focus on
business - 关注业务
Compelling Definition
The evolution of business intelligence begins with a definitional shift.
Perhaps the most widely quoted BI definition today is David Loshin’s “the
processes, technologies and tools needed to turn data into information,
information into knowledge, and knowledge into plans that drive profitable
business actions.” Larissa Moss describes BI as “an architecture and a
collection of integrated operational as well as decision-support applications
and databases that provide the business community easy access to business
data.” And Steve Dine defines it as “the process, architecture, technologies
and tools that help companies transform. their data into accurate, actionable
and timely information and disseminate that information across the
organization.”
商业智能的演变开始于定义的转变。也许最广泛引用的BI
定义是David Loshin
今日的“一套需要把数据转换成信息，信息转化为知识，知识纳入驱动盈利的商业行为计划的过程，技术和工具”
。Larissa Moss
把BI
描述为“用以决策支持应用程序和数据库
，以向商业界提供方便的业务数据的架构和一套集成操作。”Steve Dine
则定义为“一套帮助企业把他们的数据转化为准确，可操作的和及时的信息和把信息传播到整个组织的过程，架构，技术和工具。”
While all of the definitions are technically correct, it feels like
something is missing. The troubling thing is that all of the definitions are
IT-centric. They describe processes, tools, technologies, data, databases and
applications. In each of the few instances where the word “business” appears,
it is used as an adjective that qualifies seemingly more important nouns:
action, community and data.
尽管所有的定义从技术上讲都是正确的，但是感觉仍有点缺失。令人不安的是，所有的定义是IT
为中心的。他们描述流程，工具，技术，数据，数据库及应用。在每一个少数情况下改为“业务”出现时，它是作为一个形容词的资格似乎更重要的名词：行动，社区和数据。
So what is compelling about these definitions? What do they offer as
motivation for a business to spend time, money and energy on business
intelligence? What do they provide to a BI program as purpose, direction, and the
basis for goals and measures of success? I think that they fall short on all
counts.
那么关于这些定义什么是令人信服的呢？对于一种商业动机来说，花费时间，金钱和精力上在商业智能上，BI
能够提供什么呢？对于BI
来说，能够提供什么东西作为衡量成功和目标的目的，方向和基础？我认为这些都达不到。
I define business intelligence as “the ability of an organization or
business to reason, plan, predict, solve problems, think abstractly,
comprehend, innovate and learn in ways that increase organizational knowledge,
inform. decision processes, enable effective actions, and help to establish and
achieve business goals.” This definition, I believe, is compelling. It
describes the qualities of an intelligent business and is sufficiently specific
to serve as the basis for purpose, direction, goals and measures. Equally
important, it reflects and builds upon Dresner’s BI vision. To further
understand the origin and the implications of this definition.
我把商业智能定义为“一种组织增进知识的能力，包括探求原因、计划、预测、解决问题、抽象思考、理解、创新和学习
等方式，指导决策过程，促使有效行动，帮助建立并实现业务目标。”这个定义，我认为是引人注目的。它描述的智能业务的品质，充分的规范化了所服务的目的，方向，目标和措施的基础。同样重要的是，它反映和借鉴Dresner
的BI
视野。为了进一步了解的起源和影响这一定义。
Focus on Business Analytics
关注业务分析论（分析学）
The second major transformation in the changing face of BI is a shift of
attention from data to analytics. Don’t be tempted to read “focus on analytics”
as “focus on dashboards and scorecards.” Dashboards and scorecards are not
analytics; they are simply useful ways to deliver metrics to analytic
processes.
在正在变革的BI
面孔里，第二个主要的转变是注意力从数据到分析的转移。不要试图把“关注分析论”看作为“关注于仪表板和记分卡。”仪表板和记分卡不是分析论（分析学），他们只不过是在以有效的方式提供分析过程的数据和程序。
Business analytics encompasses the science, disciplines and processes of
business analysis. It follows reporting (which may use dashboards and
scorecards) and precedes understanding (which is done by people). This is the
point at which information leads to knowledge – it takes both analysis and understanding
to achieve knowledge. To achieve useful knowledge, the analysis and
understanding must have purpose. For business analytics, that purpose is
positioning to reason, plan, predict, solve problems, innovate and learn – the
defining capacities of intelligent business. Figure 1 illustrates the role and
placement of analytics in business intelligence.
业务分析论（分析学）涵盖了科学，学科和业务分析的过程。信息产生知识-
这需要分析论和理解力才能实现知识。为了实现有用的知识，分析论和理解力必须就有目的。对于业务分析论（分析学），这一目的是定位原因，计划，预测，解决问题，不断创新和学习-
确定能力的智能业务。
图1
显示了在商业智能中分析论的作用和位置。
The point of business analytics is knowledge: knowing what has happened,
knowing why it happened, knowing what to expect in the future and knowing what
to do about it. This is the next “hot spot” of business intelligence. There is
power in analytics, but also complexity. It involves statistics, profiling and
pattern recognition, behavioral analysis, time series analysis, predictive
modeling, visualization, cause-and-effect studies and more.
业务分析论的关键是知识：知道发生了什么事，知道它为什么发生的，知道未来期望什么和知道怎样做的。这是商业智能的下一个“热点”。在分析论有一种能力，而且也很复杂。它涉及到统计，分析和模式识别，行为分析，时间序列分析，预测建模，可视化，因果的研究等等。
The complexity that makes business analytics powerful also presents a
dilemma. Despite integrated data and powerful tools, most business analysis is
performed by loading local data into simple spreadsheets. Anecdotal evidence
suggests that as much as eighty-five percent of business analysis is actually
performed using "manualytics
" processes. It is clear that a gap exists
between the potential of business analytics and the realities of business
analysis.
使业务分析功能强大的这种复杂性也处于两难境地。尽管集成了数据和有了功能强大的工具，大多数商业分析仍然是由当地的数据加载到简单的试算表。有证据表明，高达85%
的业务分析实际上是用“人工分析”处理的。很明显，在商业分析的潜力和现实的业务分析之间还存在着很大的差距。
Closing the Gap
结束这个差距
The analytic gap shows itself in virtually every organization as two
distinct and highly polarized approaches to business analysis. Figure 2
illustrates the polarity as described by Gartner. The IT-intensive approach is
one of managed reporting, end-user query, OLAP, dashboards, scorecards and data
mining. It is regarded as expensive, rigid, slow, inaccessible, server-centric
and dependent upon a large infrastructure. The office productivity approach is
dominated by Excel spreadsheets and Access databases. It is a world of data
dumps, locally created data, manualytics and spreadmarts. This desktop-centric approach
is considered to be non-scalable, untraceable, unrepeatable, unsecured and
particularly difficult to audit.
分析论差距表明自己在几乎每一个组织为两个不同的和高度两极化的方法对业务分析。
图2
显示Gartner
公司所描述的那样的差别。与IT
紧密相关的办法是管理
报告，终端用户查询，多维分析，仪表板，记分卡和数据挖掘的一种。它被视为昂贵的，严格的，缓慢的，不可访问的，以服务器为中心，并依赖于庞大的基础架构。Office
生产力的方法论主要是Excel
电子表格和Access
数据库。这是一个数据转储，本地数据， 人工分析和spreadmarts
的世界。这种以桌面为中心的方法被认为是非可扩展性，难以跟踪，不可重复的，不安全的和并且很难审计的。
Despite negative characterizations – expensive, rigid, untraceable,
difficult, etc. – both of today’s analytic approaches have merit. The goal of
next generation analytics is not to choose – certainly not to eliminate one
approach in favor of the other. Instead, it is to fill in the middle, moving
from two extremes to a continuum of analytic options. Trends in BI practices
and in analytic technologies show movement in the right direction.
尽管有着各种负面的印象-
昂贵的，僵化的，不可跟踪，困难，等等-
今天的分析方法还是有优点的。目下一代分析方法论的目标不是选择-
当然不是为了去选择，为了取悦一个方法而消除一个。相反，它是为了填补中间的缺失，从两个极端到一种持续的分析选项。BI
实践的趋势商业智能分析技术，显示了在正确方向的变化。
Among the practices that will shape the future of business analytics are:
在实践中的将会塑造未来的商业分析如下：
Pervasive BI
– Extending the
reach of business intelligence into the business community by reaching more
people at all levels with valuable information. The key to pervasive ismore
people
.
普遍深入的商业智能-
通过给越来越多的人在各个级别上提供宝贵的信息，扩大了商业智能在商业界的影响。深入人心的关键是涉及到越来越多的人。
BI for the Masses
– Extending the
range of BI capabilities that are within the grasp of small businesses and
small IT departments. Where the pervasive focus is more people, the goal here
ismore affordable
.
面向大众的BI-
扩大BI
接受程度的范围在小企业和小IT
部门的应用。如果普及的重点是更多的人，这里的目标是让更多的人支付的起。
Role-Based BI
– Analytic
outputs are tailored to the needs and interests of specific audiences.
Measures, metrics, trends, scorecards, dashboards, etc. are tailored to
business functions (research, marketing, sales, finance, etc.) and to business
level (strategic, tactical, operational). Role-based business intelligence
seeks to achievemore relevance
.
基于角色的BI –
分析产出适应的需要和利益的具体对象。措施，指标，趋势，记分卡，仪表板等，适合于商务功能（研究，市场营销，销售，财务等）
，业务水平（战略，战术，作战） 。基于角色的商业智能力求取得更多的相关性。
Discovery-Based Analytics
–
Interactive, exploratory, investigative analytic processes recognize the
natural cycle of analysis where each answer brings new questions. Discovery
analytics enable the principle of “listening to the data” to learn what it can
tell you. The goal here ismore knowledge
.
基于分析的发现-
互动，探索，调查分析过程认识到自然周期的分析，每个答案带来了新的问题。发现分析方**增强了“监听数据”的原则 ，以了解它可以告诉你。这里的目标是更多的知识。
Agile Analytics
– Providing rapid
response to situations where immediate need for analytics exists. Agile
analytics encompasses the ability to quickly and continuously adapt to changing
circumstances, both business and technical. The agile objectives aremore
adaptable and faster
.
敏捷分析-
提供快速反应的情况迫切需要分析存在。敏捷分析包括迅速和不断地适应不断变化的环境，包括业务和技术的。敏捷的目标是更多的适应能力和速度。
None of these trends, of course, are practical without the aid of supporting
technology. Innovative companies now offer tools and technology to enable the
next generation of analytics. Some of the interesting products are:
所有这些趋势，当然，实际的帮助，支持的技术。现在不断创新公司们能够提供的工具和技术，以增强一代的分析。一些有趣的产品有：
Cloud9 Analytics
– role-based, on-demand business intelligence
applications using a software-as-a-service (SaaS) model.
Cloud9
分析-
以角色为基础，用SaaS
模式提供按需的商业智能应用。
eThority
– web
and desktop based, user-focused, accessible and
scalable approach to business-driven analysis of enterprise data.
eThority -
基于Web
和桌面的，以用户为中心，可利用和可扩展的方法，业务驱动分析企业数据。
illuminate
– a correlation database that brings agility to the
back-end data integration tasks that are barriers to agile analytics.
illuminate
-
一套相关的数据库，能够提供灵活的后端数据集成任务，扫除原有的敏捷分析障碍。
Lyza
– depolarizing and “filling the middle” with
desktop-based data gathering, data analysis, reporting and analytic publishing.
Lyza –
用基于桌面的数据收集，数据分析，报告和分析发布去消除极端化和“填补中间”。
Netmap
– a visual approach to discovery-based analysis.
Netmap –
一种可视化的方法用来实现基于发现的分析。
PolyVista
–
extending OLAP with prepackaged, easy-to-use data mining and discovery
automation capabilities.
PolyVista -
用预先封装的，易于使用的数据挖掘和自动发现能力来扩展OLAP
。
QlikView
– rapid deployment of visual analytics from back-end
data integration to front-end data views.
QlikView -
快速部署的视觉分析从后端数据集成到前端数据的意见。
This is certainly not an exhaustive list, but a sample of the kinds of
products that are shaping the future of analytics and changing the face of
business intelligence. Each company in different ways contributes to closing
the analytics-to-analysis gap.
这肯定不是一个详尽的清单，但各种产品的样品，能够塑造未来的分析和改变着商业智能的面孔。每家公司都在以不同的方式致力于缩小分析方法论到分析的差距。
data wareshouse3