⑴ 如何选型商业智能和分析平台，gartner给了这些建议
Gartner是个有着40年历史的上市公司，位列美国最大的500家公司（S&P500）之中。它在2018年的总营收为39.8亿美元，在全球有一万五千多名员工。它提供研究和咨询服务，业务范围涵盖信息系统、财务、人力资源、客户服务、法务合规、市场、销售和供应链等，可说是企业服务全家桶，应有尽有。它在全球100多个国家拥有12000多个机构客户，可谓是影响巨大。
Gartner在2019年总共会发布四十多个不同类别的魔力象限报告，四十多个关键能力报告。这些报告汇总一个细分行业内的众多产品，对其从各个角度加以分析、比较，从而为客户购买这类产品或服务提供咨询指导。
Gartner为分析与商务智能平台定义了5类使用用例：
◾敏捷、中心化的BI 配置：支持敏捷、IT使能的工作流。
◾去中心化的分析：支持自服务式分析，让单个业务单元或用户也能灵活使用
◾可治理管控的数据发现：支持数据从自服务提升到系统级模烂中记录，对数据的认证和重用。
◾OEM或内嵌式分析：可以将数据分析嵌入到应用程序和工作流之中
◾外部网络部署：支持外部用户也可以访问数据和分析功能。
15种关键能力——
基础结构：BI平台管理、安全和架构，云BI，数据源连接和引入
数据管理：元数据管理，数据存储和载入选项，数据准备，可扩展性和数据模型复杂度
分析和内容生成：公民数据科学家可用的高级分析，分析仪表盘，交互可视化探索，增强数据发现，移动数据探索和内容创建
发现分享：分析内容内嵌，分析内容的发布、分享和协作
整体平台：易用性、视觉吸引力，工作流集成
行业在不断发展，那么对于未来的BI产品，哪些方向是明日之星呢？Gartner所认定的BI发展趋势有——
基础结构：对图数据库（如Neo4J）和搜索数据库（如ElasticSearch）的直接访问，支持跨云部署，在边缘设备上运行
数据管理：语义图，积累好的敏捷数据目录，可以改善分析的额外数据集的数据协调、关联度分析，多结构数据的增强数据准备，自动提升用户生成的模型和内容到系统记录层级，下推数据处理到大数据源，支持准备、协调和探索实时事件和流数据
分析和内容生成：增强数据发现，增强警示，语音和文本的搜素和自然语言处理，交谈式分析，虚拟现实和增强现实，对非结构化数据的内容和文本分析
发现分享：从平台里触发业务行动，决策管理以形成协作工作流的闭环，相关内容的旦山群体和上下文推荐，集成的假设式分析和优化，作为服务提供的数据，沉浸式分析内容展现
在最新的《Gartner分析与商务智能魔力象限》（Gartner Magic Quadrant for Analytics and Business Intelligence Platforms）中，MicroStrategy位居该象限中唯一的“挑战者”。在《2019 Gartner分析与商务智能平台关键能力报告》中，MicroStrategy在受评的20个供应商中脱颖而出。在15项平台关键能力的评估中，MicroStrategy在其中的10项拿到了最高分。
综上所述，MicroStrategy2019是一个结合数据准备、可视化、基于NLQ数据探索、仪表盘和移动设备的企业级BI分析平台。MicroStrategy更新迭代速度很快，每3个月发布一个基于特性的产品更新版本，每12个月发布一个主要的平台版本。
在过去的一年里，MicroStrategy不断提高其平台的可用性、部署性和嵌入性。语义图谱的增强为智能内容推荐、基于NLQ数据探索和增强分析领域的投资提供了基础历雀。Hyperintelligence更是创造了业界BI 首屈一指的新技术，可在“零点击"的模式下动态显示你想知道的所有信息(技术基于浏览器中打开)。值得注意的是，MicroStrategy已经可向其他BI分析工具开放它的语义层。选择MicroStrategy的组织甚至可以将Tableau、Microsoft Power BI 作为备选方案。
⑵ 商务智能应用是如何进行数据分析的
商业智能中的数据分析工作主要通过OLAP来实现。原理是根据业务需求，建立人员分析数据的维度比如年月日等等袭汪瞎。
比如我们要利用商务智能工具FineBI做一个财务分析，就可以先确立一个主表，依据分析的陵滚维度取出不同的拍空维表，OLAP会自动建立表间关联，业务人员只需搭建图表结构即可实现数据查询和分析结构的展示。
⑶ 计算机大数据分析技术使商务智能的更具价值
计算机大数据分析技术使商务智能的更具价值
商务智能的真正意义是在有了计算机大数据分析数据以后才成就了现实。由此看来，商业智能是离不开计算机大数据处理技术的。可以这样说，没有计算机时代，没有大数据分析技术，这再有用的商业智能也都无从谈起。
商务智能既然跟计算机技术，能跟大数据分析衫含仿技术息息相关，那么，一切的商业智能都是在计算机环境下才能完成的工作。假使某种有关商务智能的数据被挖掘到有价值的程度，这注定是要保存在计算机中。那么，谁使用这些商业智能有价值的数据，还要有一定的计算机操作技术。
第一、要学会使用计算机软件工具的集合端给用户所设置的查询或是报告工具，这些工具一般有OLAP工具。这个工具的作用可提供多维数据的管理环境，主要支持对商业中一些问题的建模以及对一些商业数据的分析。数老启据挖掘软件靠的是像连接各数据间的神经一般，能对各种数据进行整理归纳，使数据出现规则的状态，并可借助这款数据挖掘软件分析各数据间所存在的联系，为相应的推断做基础性准备。数据仓库以及数据集市这样的软件，能使数据实现转换和管理，也能实现对数据的保存，所以，这些计算机软件作为商业智能使用者来说，要懂，要会用。
第二、计算机的联机处理是一个重要的关键环节。计算机联机事务处理是对最原始关系型数据库中所要使用的计算机软件，它能支持对数据最基本的，也是最日常的事物处理，但计算机联机分析处理软件是数据仓库中所要应用的软件，它主要是支持较为复杂的数据分析操作，也用于对决策者的某些决策上的支持，并且，那些可被观察到的商业智能数据分析结果，也都是靠这款软件来支持完成。
商业智能或纤中所能用到的各款计算机支持商业智能数据分析的软件中，计算机联机分析处理软件是一个最为核心性的软件。因此，熟悉或掌握这款软件的意义非同一般。
⑷ 商业智能软件有什么优势
1、打破信息孤岛，有效整合数据，企业在信息化过程中，各部门或子公司引进不同的信息管理系统（如CRM、ERP、OA等），产生了各种各样独立的“信息岛”。由于企业内部各信息系统之间的数据资源无法共享，企业难以获得统一的信息数据，形成“信息孤岛”。企业难以有效整合使用这些“信息孤岛”上的数据，无法为企业的经营决策提供有效的数据支持。商业智能BI软件很好地解决了这一问题，它可以将企业信息化的数据孤岛整合起来，提供一个全局的视图，让决策者可以更加全面地看待问题，降低决策失误风险性。2、提升数据展示效率，深入分析问题，商业智能BI软件能够将数据实时快速地转换成清晰明了的可视化报告，使决策者能够迅速准确地作出决策，给企业注入新的革命性的管理思想。决策者可以根据商业智能BI软件提供的钻取功能对数据结果进行追根溯源，使问题的分析不止步于表面结果，发掘出数据中包含的机会：即如何以更低的成本、更快的速度、更高的质量完成任务；这使管理者能在质疑中不断以创新来获得差异化竞争优势。BI商务智能软件的预测功能使企业看问题更长远，决策更具前瞻性。BI商务智能软件会根据企业生产经营中积累的大数据进行挖掘，得到数据的之间潜在的规律或趋势，进而做出下一步预测。4、帮助企业开源和节流，增加利润IDC一项针对全球20个国家2000多家大中型穗滚企业进行的调查研究显示，企业如果能采用一套完整的大数据解决方案（包括数据采集、整合、筛选、分析、分享），而不仅是单个数据管理技术，在未来四年中将从其数据资产中发掘出额外60%的数据红利。5、风险预警，企业可以在BI商务智能软件中设置数据报警阈值，数据一旦超标，系统会以各种手段通知到管理员，弯大使企业风险可控，减少安全漏洞。6、提高员工的工作效率，在没有商业智能BI软件之前，员工要写大量复杂的SQL语句，制作大量报表以满足业务要求，决策者在面对大量堆积报表数据也感到头疼。而商业智能BI软件则彻底改变了这种现状，使业务人员可以轻松进行数据分析，使决策者可以自在查看分析，及时猜闹余查看到决策所关心的数据。现在企业都有许多许多的数据，可是却不能充分利用这些数据，如果有人可以合理利用BI工具，来帮助公司的规划和决策，谁就可以抢占先机，或者至少对自己对市场多一份理解。
⑸ 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芹伍策腔滚的关键伍首余。
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 proceres 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 instry 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 procts 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 ring 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 plication and make sure old, outdated, or no-longer-relevant data is deleted.
⑹ 简单介绍一下加推
加推是什么？
加推是一款赋能销售的智能系统。加推将CRM（客户关系管理平台）与小程序（智能名片系统、微商城、微官网）集成闭环系统，赋能销售见面、跟进、成交、管理四个关键环节，提升企业人效。
加推简介：
最初，加推希望为销售打造一款武器，于是研究开发了智能名片系统，这款名片极大的提升了销售的“见面”效率，不仅标准化的输出了个人、产品和公司，快速传播裂变。随后加推团队摒弃了传统CRM手动录入、强制管控的传统设计理念，独立设计了一套以赋能为核心的CRM系统，研发微信链接客户，智能客户画像、B2S2C用客户反馈来管理销售的新型CRM系统，每个客户都是活的，沉淀在公司独立的小程序里，成为公司的核心资产。
加推是一家技术创新型公司，2018年5月获得了来自红杉资本、IDG 资本领投的1.68亿融资，全年包揽17项科技奖项，其中包括中国互联网创新创业大赛冠军等含金量极高的科技大奖。
如何使用加推：
我们并不主张销售只是销售部的事。加推也无法独立完成赋能销售的使命，加推只是一个工具。事实上，销售需要每个部门的赋能。品牌、培训、产品部门在为销售见面环节建设完整的微商城、微官网，时刻更新更加符合客户需求的“话术库”，销售人员在递出名片时才能让公司与更好的与客户“见面”；推广部门需要实时推送内容，公司产品的卖点、变化、大事记、节日活动，这是销售跟进客户的“物料”，数据会量化说明推广部门的价值；产品、供应链与财务部门需要及时更新产品库、实时分佣；管理部门制定线索、客户转化等各项KPI，运营销售过程。最后，加推为企业里每个人都建了一个站，我们每个人都可以为公司带来流量，直接影响着销售结果，加推也会统计每个人为企业带来的价值，协同作战、全员营销。
加推的影响：
加推CRM+小程序的商业模式彻底解决了国外CRM产品模式在中国水土不服、难以使用的问题，并以1人1天1块钱的互联网式定价帮助企业使用CRM、微商城、官网的门槛降至最低，仅6个月，超过25000家企业开始使用加推，其中包括中国移动、中国人寿、科大讯飞、大自然家居、蓝盾、创维、獐子岛、横店影视在内的200多家上市公司，领先的技术和革新的商业模式让加推成为企业服务领域成长最快的公司之一。
⑺ 电信、金融等行业如何实现商业智能化
电信、金融等行业想要实现商业智能化。进行信息化管理。
就要实现企业的ERP管理。
企业在应用信息化软件后，随着管理需求的进一步深入，新的信息化需求也随之诞生。鼎捷软件未来在满足这样需求时不仅以开发新的技术领先的产品为核心竞争优势，而且要采用单一软件产品加技术元件整合为解决方案的方式，以ERP为核心应用平台，重新构建为服务搏漏举型组合、行业型组合和规模型组合。
目前大多数本土软件公司依然停留在单一软件产品的优势上，面对市场瓶颈，软件厂商往往希望通过构建新的商业模式或概念，让企业直接快捷的享受信息化成果，而不需要相关积累，这样失败的风险将会大大增加。
而鼎捷软件有限公司不仅积累了所有具备优势的单一产品，并基碧且拥有大量高满意度的忠实客户和实施经验，因此，在以ERP为主要信息平台的基搜颂础上，鼎捷软件有限公司可以针对不同客户需求，做出不同产品组合和延伸，满足企业快速，敏捷的需求。
⑻ 商业智能BI工具的2大优势盘点！
近年来，随着大数据、数据分析技术的兴起，商业智能BI工具应运而生，其中BI工具已成为众多企业商务决策的重要工具。也许有人会问，为什么企业需要 商业智能BI工具 ？商业智能BI工具可以为企业带来什么？
首先，我们先来了解商业智能BI工具的含义，然后再看看BI工具是什么，以及商业智能BI是如何工作的。
商务智能(BusinessIntelligence)是指利用现代数据仓库技术、在线分析处理技术、数据挖掘和数据呈现技术来实现商业价值的数据分析技术。
BI工具究竟如何运作？BI工具是由ETL、DW、OLAP、DM等多个环节组成的复杂技术集合。简而言之，就是通过ETL工具，将交易系统中已经发生的数据，提取到主题明确的数据仓库中，OLAP处理后产生的Cube或报告，通过Portal呈现给用户，用户利用这些分类(Classification)、聚集(Clustering)、描述和可视化(DescriptionandVisualization)的数据，支持业务决策。
商务智能BI工具可以为企业带来什么？商务智能BI是一套完整的解决方案，包含了数据分析过程，重点体现在把商业价值带到企业中，用它把企业现有的数据进行有效整合，快速准确地提供报表和提供决策依据，帮助企业做出明智的经营决策。那么，价值是怎么实现的呢？
以国内主流商业智能BI工具之一思迈特软件Smartbi为例，分析了商业智能BI工具在企业中的应用。
1. 直观地呈现报表
BI工具中，利用柱状图、饼状图、折线图、二维表格等直观的图形化方式，将企业日常业务数据(财务、供应链、人力、运营、市场、销售、产品等)全部呈现出来，再通过各种数据分析、筛选、关联、跳转、钻取等方式查看各类业务指标。
使用可视化的报告分析呈现方式，使用户对日常业务有一个清晰、直接、准确的认知，同时解放了业务人员手工使用Excel的各种功能进行汇总分析、绘图，极大地提高了工作效率。
思迈特软件Smartbi大数据分析平台提供了丰富的ECharts图形可视化选择，（堆积）柱图、（堆积）横条图、散点图、（堆积）面积图、折线图、组合图、瀑布图、饼图、环形图、南丁格尔玫瑰图、油量图、散点图、气泡图、雷达图、关系图、热力图、词云图。此外，当通过电子表格(Excel)绘制图表时，Excel可以用Excel完成更复杂的图形设计，如：甘特图、山形图、手风琴图、子弹图、小型图、迷你图、漏斗图、进度图、复合饼图、多层饼图等。并将条件格式设置表格(数据条，色阶，图示集)。
2. 分析数据"异常"。
数据异常分析采用对比分析方法。若发现某些数据指标反映出的情况超出日常经验判断，则业务人员通过可视化报告呈现。在这个时候，就需要对这些“异常”数据进行有目的的分析，通过相关维度、指标钻取、关联等分析方法来探究可能的原因。
业务人员通过一次或多次多个维度和指标图表，逐渐形成了一个可靠、固化的分析模型。现阶段的业务人员不再被动接受图表中反映的信息，而是通过“异常”数据定位背后的业务问题。数据和业务在这个层面上开始有直接的对应关系。此时，他们可以利用数据图表之间的逻辑关系寻找解决方案，提高企业的运营效率。
总结：商业智能BI的本质是业务问题和管理问题。商业智能BI数据分析来源于业务，通过数据呈现发现业务问题，重新优化升级业务运营的过程，因此利用好商业智能BI工具提高业务能力，取得更好的业绩。
⑼ 企业实施商务智能应注意三方面
企业实施商务智能应注意三方面
从实际情况出发：“从实际情况出发”是我们平时说得最多的一句话。然而在商务智能系统的实施中，我们还是要老调重弹。虽然说企业的商务智能能够发现隐藏的成本和潜在增加营业收入的机会，但是并非所有的企业都适合实施商务智能系统。这需要从企业发展的规模、战略目标设定等角度来考虑。我国很多企业都存在着盲目的跟风现象，每当出现一种新的技术时，便不顾企业的实际运营状况，一味投入大量人力、物力和财力，以为这样做便能抓住新陪野技术的脉搏，提升企业的竞争力，其结果往往事与愿违。一旦决定实施商务智能系统，就意味着企业已经具备了相当的软、硬件条件，在能保证回答诸如“谁是你的用户？你有前台技术精力吗？你的渗渣公司在全国甚至全球都需要商务只能么？”这一类问题的基础上，从业务领域着手，结合业务部门和IT部门协力制定数据框架，把商务智能作为业务战略的一部分来看待，确保商务智能系统在公司的战略地位。
企业实施商务智能的问题：虽然商务智能系统在许多企业，特别是国外的大企业中得到了越来越广泛的应用，但在实际的建设和使用过程中，仍然存在一些问题。首先是存在软件功能与用户需求之间的差异；在商务智能系统的建设过程中，往往是IT经理和资深用户追求所用的功能，而对于大多数使用者：业务管理者、高层管理人员和业务专家等，却缺少容易使用、熟悉的工具界面和分析方式，这使得他们不愿花时间和精力学习。若用户无法有效地运用智能系统来赢得事实上的竞争优势，则巨额投资无法弥补，系统的商业价值也将被质疑。因此，企业在搭建商务智能系统时，应注意“量身定做”，选择合适自己的工具和系统。其次是来自技术领域的困难；计算机处理技术和存储能力的迅速发展，带来了信息量的幂级增长，时下的数据仓库通常都超过了100GB,而且容量超过1TB的数据仓库系统的数量正在急剧增长。随着数据量的增长，数据关系的可能的排列方式也大幅度增加，传统的商务智能工具已经显得力不从心。比如联机分析处理系统通常只能处理10-20GB的数据。因此，要充分发挥商务智能系统的功能，企业需要更强有力的工具，这有赖于人工智能、机器学习、数据仓库技术、专家智能系统等科学技术的进步和发展。
来自文化上丛乱悄的挑战。商务智能体系的建立是一项长期、艰巨的任务，执行起来不仅有技术上的困难，而且有文化上的挑战。有很多企业（特别是中国企业）从来没有真正用过商务智能，无法认识到商务智能是如何改变了企业经营，使企业变得更加高效，更加强大。而且，有些企业害怕商务智能体系所洞察出的弱点和弊端，从而“讳疾忌医”。