什么是商务智能

1. 什么是商务智能

商务智能就是商业智能。具体的含义百度百科上解释的很清楚，商业智能更确切地说是信息化建设的一个过程，一个解决方案或是一套集成的应用系统。想要真正理解体会最好的方法就是下一个商业智能，连接自己的数据，新建分析试试。
推荐你试试帆软的finebi。我之前用的帆软报表，后来领导要求，就整了一套finebi，在优酷看视频教程一个下午就都会了,反正真的还不错，你可以看一下。

2. 什么是商务智能

商业智能是商业数据海洋中的指南针，它从历史数据中提取信息，搞清楚经营状况，通过信息的分析获取对经营决策有价值的知识，从而帮助用户对自身的业务经营做出正确而明智的决定。比如，通过商业智能可以解决客户在不同地域的分布情况，可以对客户进行各个角度的分类，还可以把客户和订单联系起来，找出其变化趋势。

商业智能的概念最早是gartner于1996年提出来的。当时将商业智能定义为一类由数据仓库（或数据集市）、查询报表、联机分析、数据挖掘、数据备份和恢复等部分组成的，以帮助企业决策为目的的技术及其应用。而后随着商务环境的变迁和技术的进步，人们对于bi有了更多和更深的认识。

2007年3月，gartner商务智能峰会重新定义bi为分析应用（
工具
）、基础架构和
平台
以及良好的实践（
模型
）。商务智能的发展呈现出“从数据驱动转向业务驱动、从关注技术转向关注应用、从关注工具转向关注工具产生的绩效”的发展特点。

3. 智能商务是什么？

智能商务系统是电脑互联网WEB网站、移动手机网站WAP及移动WAP短信群发三合一系统，使用同一域名进步访问。在网站统一管理后台可以自定义公司
WEB、WAP形象网站（主页、首页页面模板样式选择及自定义），添加编写企业相关资料（例如公司简介、产品展示等），简单易操作，二者只用一个后台统一
管理，信息发布同步。在统一后台管理等特点的同时，还可通过WAP短信群发的方式来通知企业客户关于企业最新动态消息。

4. 什么叫做智能商务？

智能商务系统是电脑互联网WEB网站、移动手机网站WAP及移动WAP短信群发三合一系统，使用同一域名进步访问。在网站统一管理后台可以自定义公司WEB、WAP形象网站（主页、首页页面模板样式选择及自定义），添加编写企业相关资料（例如公司简介、产品展示等），简单易操作，二者只用一个后台统一管理，信息发布同步。在统一后台管理等特点的同时，还可通过WAP短信群发的方式来通知企业客户关于企业最新动态消息。
通过数据仓库、多维数据分析和数据挖掘等技术，集成企业主要的业务和财务系统并进行分析，从数据中提取有用的信息和知识，有效帮助领导层和业务部门提高管理和经营的洞察力。

5. 你认为商务智能的含义是什么？

商业智能，又称商务智能，英文为Business Intelligence，简写为BI。  商业智能通常被理解为将企业中现有的数据转化为知识，帮助企业做出明智的业务经营决策的工具。这里所谈的数据包括来自企业业务系统的订单、库存、交易账目、客户和供应商等来自企业所处行业和竞争对手的数据以及来自企业所处的其他外部环境中的各种数据。而商业智能能够辅助的业务经营决策，既可以是操作层的，也可以是战术层和战略层的决策。为了将数据转化为知识，需要利用数据仓库、联机分析处理（OLAP）工具和数据挖掘等技术。因此，从技术层面上讲，商业智能不是什么新技术，它只是数据仓库、OLAP和数据挖掘等技术的综合运用。  目前，学术界对商业智能的定义并不统一。商业智能通常被理解为将企业中现有的数据转化为知识，帮助企业做出明智的业务经营决策的工具 可以认为，商业智能是对商业信息的搜集、管理和分析过程，目的是使企业的各级决策者获得知识或洞察力（insight），促使他们做出对企业更有利的决策。商业智能一般由数据仓库、联机分析处理、数据挖掘、数据备份和恢复等部分组成。商业智能的实现涉及到软件、硬件、咨询服务及应用，其基本体系结构包括数据仓库、联机分析处理和数据挖掘三个部分。  因此，把商业智能看成是一种解决方案应该比较恰当。商业智能的关键是从许多来自不同的企业运作系统的数据中提取出有用的数据并进行清理，以保证数据的正确性，然后经过抽取（Extraction）、转换（Transformation）和装载（Load），即ETL过程，合并到一个企业级的数据仓库里，从而得到企业数据的一个全局视图，在此基础上利用合适的查询和分析工具、数据挖掘工具、OLAP工具等对其进行分析和处理（这时信息变为辅助决策的知识），最后将知识呈现给管理者，为管理者的决策过程提供支持。

满意请采纳

6. 商务智能：到底什么是商务智能

 也许这里是对商务智能最好的介绍。
   Business leaders have access to more data than ever before.   商业领袖比以往更需要处理更多的数据。
   But data by itself doesn’t generate insights.   数据本身并没有价值。
   Business Intelligence Tools have become the go-to resource for helping companies harness the power of big data and analytics and make smarter, data-driven decisions.   商业智能工具已经成为帮助公司处理大量数据以及分析并制定更合理的决策的关键。
                                           The specific definition of BI can vary depending on who you ask.   BI的准确定义要看谁（什么角色）来问。
   Here are a few examples of some of the ways business intelligence is defined:   这里是一些典型的对商务智能的定义：
                                                                                                                                                                                                           n our view, each of those definitions is incomplete.
   Many of them are focused only on the software used for business intelligence. While the term is often heard in relation to software vendors, there’s more to BI than just software tools.
   In addition, many of the common definitions of BI neglect to include the primary goal of business intelligence.
   Our definition of BI is as follows:    Business Intelligence helps derive meaningful insights from raw data. It’s an umbrella term that includes the software, infrastructure, policies, and procedures that can lead to smarter, data-driven decision making. 
                                           The term “business intelligence” has been around for decades, but it was first used as it is today by Howard Dresner in 1988.
   Dresner  defined business intelligence  as the “concepts and methods to improve business decision making by using fact-based support systems.”
   Today, business intelligence is  defined by Forrester  as “a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making.”
   In the first stages of business intelligence, IT teams ran reports and queries for the business side, though today’s systems are focused more on enabling self-service intelligence for business users.
   As with any technology, the offerings from vendors have evolved over time and continue to do so. As core features like reporting and analytics are becoming commoditized, vendors are looking at other features to differentiate themselves. Likewise, as the business environment changes, so do the requirements organizations have for their business intelligence applications.
   These are a few of the biggest trends and developments in business intelligence right now:   The blending of software and consulting services – Vendors are beginning to offer “information as a service” and presenting intelligence to clients, as opposed to selling the software and infrastructure businesses need to access intelligence on their own.   Increasing Self-service – Software is increasingly focused on increasing the functions that be performed without having to involve IT staff or data scientists.   Cloud-based business intelligence – While cloud computing has taken hold in other areas, it’s beginning to catch on in business intelligence, too. As this progresses, it will allow businesses to use intelligence without dedicating internal resources to manage infrastructure and perform software upgrades.   Mobile intelligence – Mobile is becoming a key part of day-to-day business and it’s no different in business intelligence. Mobile tools allow decision makers to access intelligence wherever they need it, not just when they’re at their desks.   Big Data – Businesses have access to more data than ever, and a lot of it comes from outside the organization in non-structured form. Business intelligence is increasingly being combined with Big Data analytics, so businesses can make decisions using all the information they have at their disposal, regardless of what form it takes.
   While ideally the end result of business intelligence is not complex, there is a lot of complex technology involved in turning raw data into actionable information. Here are a few of the core components of a typical business intelligence deployment:
                                           Business intelligence all starts with the data.
   As we mention above, businesses have access to more data than ever. Much of that comes from transactional systems, such as CRM systems, ERP systems, inventory databases, HR and payroll systems, and many others.
   Data used in BI also comes from external sources. One common source is social media, which organizations use to capture statements in which users mention the company. Other sources can vary greatly depending on what questions the organization is trying to answer, but may include public data from government reports, weather information and industry news reports.
                                           Simply having access to the data doesn’t mean it’s ready to be used for intelligence.
   A key part of BI is the tools and processes used to prepare data for analysis. When data is created by different applications, it’s not likely all in the same format, and data from one application can’t necessarily be looked at in relation to data from another. In addition, if business intelligence is relied on to make critical decisions, businesses must make sure the data they’re using is accurate.
   The process of getting data ready for analysis is known as Extract, Transform, Load (ETL). The data is extracted from internal and external sources, transformed into a common format, and loaded into a data warehouse. This process also typically includes data integrity checks to make sure the data being used is accurate and consistent.
                                           A data warehouse is a repository containing information from all the business’s applications and systems, as well as external sources, so it can be analyzed together.
   The ETL process ends with data being loaded into the warehouse, because when the data is contained within the separate sources, it’s not much use for intelligence. That’s for two primary reasons. First, those sources are typically applications that are designed for processing transactions, not for performing analysis. Analyzing the data in that state would take too long and disrupt critical business operations.
   Second, the point of business intelligence is to generate more insight about the organization as a whole, so the data from all of those systems must be combined in order to understand a single, holistic view of what’s happening in the company.
                                           The data warehouse and ETL process represent the back end of business intelligence, while Online Analytical Processing (OLAP) represents the front end. OLAP tools present data to users and allow them to group, aggregate and sort the data based on various criteria.
   This is the function that allows users to pull out the data they want and make the comparisons they need in order to have their questions answered.
                                           As mentioned above, one of the goals of business intelligence is to make data accessible and useful to non-technical business users. As such, data must often be transformed into something beyond spreadsheets and lists of numbers so that it can be properly understood.
   Visualization tools present data using charts, graphs and other formats to aid understanding. Traditional formats include bar graphs, pie charts and scorecards, while advanced data visualization can create interactive and dynamic content, automatically choosing the best type of representation and personalizing content for the user.
                                           The dashboard is the primary graphical interface used when working with a business intelligence system. Typically the first thing the user sees when logging on, the dashboard presents the most important reports and data visualizations for the user, customized based on the person’s role.
   The dashboard is a simple way to organize information in one place and allow the user to dig deeper for more.
   Why do companies use business intelligence? The primary goal is stay ahead of the competition and make the right decision at the right time. Those decisions can be made around pretty much any aspect of running a business, such as:
   One of the key aspects of business intelligence is that it’s designed to put information in the hands of business users. Organizations are required to make decisions at an increasingly faster pace, so today’s business intelligence tools help decision makers access the information they need without having to first go through the IT department or specifically designated data scientists.
   Rather than request a report and then wait for it to be created, the user can log into the business intelligence application and view all the critical information presented in a way that doesn’t take a specialist to understand.
    Since the goal is to help business leaders use intelligence to make better decisions, BI tools must be easy for those users to understand 
   As mentioned above, business intelligence is more than just software. For a successful implementation, businesses need to have the right processes and infrastructure in place in addition to the right business intelligence applications.
   Unfortunately, a lot of implementations aren’t successful. According to a 2011 report from Gartner,  70%-80% of business intelligence projects fail .
   In order to prevent that, here are some of the best practices organizations should follow when they formulate their business intelligence strategy:
   There’s a lot of hype around business intelligence, and many companies may make the mistake of investing a lot of money into the technology just because they think they need to. Instead, the organization must first be clear on what it wants to accomplish and identify a specific business need business intelligence can help solve.
   According to Gartner, one of the top reasons for such a high failure rate is that many organizations assume that business intelligence is a requirement, rather than fully understanding the needs of the business.
   Figuring out what those needs are should be the first step in any business intelligence strategy. The key is for IT and the business units to work together to list the needs and determine how and if they can be met using business intelligence, and whether business intelligence or some other solution is needed.
   Even when a business intelligence project is completed and all the necessary components are installed and deployed, that doesn’t mean the organization is getting the most out of its investment.
   One reason businesses run into challenges is because they rely on many different systems and applications used throughout the organization. That makes it hard to get a holistic view of the company and the “single version of the truth” that is critical to business intelligence success.
   Only 35% percent of organizations have standardized on one or a few business intelligence products throughout the company, according to InformationWeek’s  2014 Analytics, BI, and Information Management Survey . The rest use different software and systems in different business units. However, a successful business intelligence should come from the top down, with standardized tools and process that work for all departments. It helps to have the entire organization involved from the beginning so that everyone’s input is taken into account.
   When evaluating software options, it’s especially important to pay attention to how easy the systems are for the people who will use them on a regular basis. Executives, managers and others from the business side are increasingly using business intelligence tools without the help of IT, analysts and others.
   Software should have self-service functionality and the ability to display information and reports in a way that the average business person can understand. Again, this is one area in which it helps to have input from everyone during the planning stages.
   In addition, the business also needs to give people the right training so they can get the most of the tools that are selected. If the company simply hands access to people who are used to getting all of their information from spreadsheets, they likely won’t get much out of it.
   Good intelligence starts with good data. When asked what was their biggest barrier to successful business intelligence initiative, 59% of respondents in InformationWeek’s survey answered data quality issues.
   Coming up with a plan for a business intelligence deployment takes more than just deciding what software to use. A key piece is figuring out a strategy to ensure data quality. Businesses must look at what data they have or will be able to capture, and decide what they need and how they ensure its integrity.
   According to Aberdeen research,  data quality must be addressed first , before any other action is taken. Companies with the most success in business intelligence are those that invest in tools and processes to make sure records are complete and accurate. Governance processes must also be used to avoid data duplication and make sure old, outdated, or no-longer-relevant data is deleted.

7. 商务智能产生的背景是什么?

商务智能的产生是随着信息进步发展，企业产生的需求所致。
一是因为很多企业内部都存在信息孤岛，数据无法进行方便地统一分析
二是传统的数据分析工具，报表工具要写sql，效率不高，而且只是数据汇总呈现，无法辅助决策者看到最核心最关心的数据信息，辅助决策
三是企业积累的数据量越来越大，对即席多维自由分析的需求越来越强烈。
你可以试用一下帆软的商务智能finebi，感受一下它和一般的数据分析，excel，报表的区别，就更能明白他产生的背景了。

8. 什么是商业智能