⑴ 如何選型商業智能和分析平台，gartner給了這些建議
Gartner是個有著40年歷史的上市公司，位列美國最大的500家公司（S&P500）之中。它在2018年的總營收為39.8億美元，在全球有一萬五千多名員工。它提供研究和咨詢服務，業務范圍涵蓋信息系統、財務、人力資源、客戶服務、法務合規、市場、銷售和供應鏈等，可說是企業服務全家桶，應有盡有。它在全球100多個國家擁有12000多個機構客戶，可謂是影響巨大。
Gartner在2019年總共會發布四十多個不同類別的魔力象限報告，四十多個關鍵能力報告。這些報告匯總一個細分行業內的眾多產品，對其從各個角度加以分析、比較，從而為客戶購買這類產品或服務提供咨詢指導。
Gartner為分析與商務智能平台定義了5類使用用例：
◾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：支持自服務式分析，讓單個業務單元或用戶也能靈活使用
◾可治理管控的數據發現：支持數據從自服務提升到系統級模爛中記錄，對數據的認證和重用。
◾OEM或內嵌式分析：可以將數據分析嵌入到應用程序和工作流之中
◾外部網路部署：支持外部用戶也可以訪問數據和分析功能。
15種關鍵能力——
基礎結構：BI平台管理、安全和架構，雲BI，數據源連接和引入
數據管理：元數據管理，數據存儲和載入選項，數據准備，可擴展性和數據模型復雜度
分析和內容生成：公民數據科學家可用的高級分析，分析儀表盤，交互可視化探索，增強數據發現，移動數據探索和內容創建
發現分享：分析內容內嵌，分析內容的發布、分享和協作
整體平台：易用性、視覺吸引力，工作流集成
行業在不斷發展，那麼對於未來的BI產品，哪些方向是明日之星呢？Gartner所認定的BI發展趨勢有——
基礎結構：對圖資料庫（如Neo4J）和搜索資料庫（如ElasticSearch）的直接訪問，支持跨雲部署，在邊緣設備上運行
數據管理：語義圖，積累好的敏捷數據目錄，可以改善分析的額外數據集的數據協調、關聯度分析，多結構數據的增強數據准備，自動提升用戶生成的模型和內容到系統記錄層級，下推數據處理到大數據源，支持准備、協調和探索實時事件和流數據
分析和內容生成：增強數據發現，增強警示，語音和文本的搜素和自然語言處理，交談式分析，虛擬現實和增強現實，對非結構化數據的內容和文本分析
發現分享：從平台里觸發業務行動，決策管理以形成協作工作流的閉環，相關內容的旦山群體和上下文推薦，集成的假設式分析和優化，作為服務提供的數據，沉浸式分析內容展現
在最新的《Gartner分析與商務智能魔力象限》（Gartner Magic Quadrant for Analytics and Business Intelligence Platforms）中，MicroStrategy位居該象限中唯一的「挑戰者」。在《2019 Gartner分析與商務智能平台關鍵能力報告》中，MicroStrategy在受評的20個供應商中脫穎而出。在15項平台關鍵能力的評估中，MicroStrategy在其中的10項拿到了最高分。
綜上所述，MicroStrategy2019是一個結合數據准備、可視化、基於NLQ數據探索、儀表盤和移動設備的企業級BI分析平台。MicroStrategy更新迭代速度很快，每3個月發布一個基於特性的產品更新版本，每12個月發布一個主要的平台版本。
在過去的一年裡，MicroStrategy不斷提高其平台的可用性、部署性和嵌入性。語義圖譜的增強為智能內容推薦、基於NLQ數據探索和增強分析領域的投資提供了基礎歷雀。Hyperintelligence更是創造了業界BI 首屈一指的新技術，可在「零點擊"的模式下動態顯示你想知道的所有信息(技術基於瀏覽器中打開)。值得注意的是，MicroStrategy已經可向其他BI分析工具開放它的語義層。選擇MicroStrategy的組織甚至可以將Tableau、Microsoft Power BI 作為備選方案。
⑵ 商務智能應用是如何進行數據分析的
商業智能中的數據分析工作主要通過OLAP來實現。原理是根據業務需求，建立人員分析數據的維度比如年月日等等襲汪瞎。
比如我們要利用商務智能工具FineBI做一個財務分析，就可以先確立一個主表，依據分析的陵滾維度取出不同的拍空維表，OLAP會自動建立表間關聯，業務人員只需搭建圖表結構即可實現數據查詢和分析結構的展示。
⑶ 計算機大數據分析技術使商務智能的更具價值
計算機大數據分析技術使商務智能的更具價值
商務智能的真正意義是在有了計算機大數據分析數據以後才成就了現實。由此看來，商業智能是離不開計算機大數據處理技術的。可以這樣說，沒有計算機時代，沒有大數據分析技術，這再有用的商業智能也都無從談起。
商務智能既然跟計算機技術，能跟大數據分析衫含仿技術息息相關，那麼，一切的商業智能都是在計算機環境下才能完成的工作。假使某種有關商務智能的數據被挖掘到有價值的程度，這註定是要保存在計算機中。那麼，誰使用這些商業智能有價值的數據，還要有一定的計算機操作技術。
第一、要學會使用計算機軟體工具的集合端給用戶所設置的查詢或是報告工具，這些工具一般有OLAP工具。這個工具的作用可提供多維數據的管理環境，主要支持對商業中一些問題的建模以及對一些商業數據的分析。數老啟據挖掘軟體靠的是像連接各數據間的神經一般，能對各種數據進行整理歸納，使數據出現規則的狀態，並可藉助這款數據挖掘軟體分析各數據間所存在的聯系，為相應的推斷做基礎性准備。數據倉庫以及數據集市這樣的軟體，能使數據實現轉換和管理，也能實現對數據的保存，所以，這些計算機軟體作為商業智能使用者來說，要懂，要會用。
第二、計算機的聯機處理是一個重要的關鍵環節。計算機聯機事務處理是對最原始關系型資料庫中所要使用的計算機軟體，它能支持對數據最基本的，也是最日常的事物處理，但計算機聯機分析處理軟體是數據倉庫中所要應用的軟體，它主要是支持較為復雜的數據分析操作，也用於對決策者的某些決策上的支持，並且，那些可被觀察到的商業智能數據分析結果，也都是靠這款軟體來支持完成。
商業智能或纖中所能用到的各款計算機支持商業智能數據分析的軟體中，計算機聯機分析處理軟體是一個最為核心性的軟體。因此，熟悉或掌握這款軟體的意義非同一般。
⑷ 商業智能軟體有什麼優勢
1、打破信息孤島，有效整合數據，企業在信息化過程中，各部門或子公司引進不同的信息管理系統（如CRM、ERP、OA等），產生了各種各樣獨立的「信息島」。由於企業內部各信息系統之間的數據資源無法共享，企業難以獲得統一的信息數據，形成「信息孤島」。企業難以有效整合使用這些「信息孤島」上的數據，無法為企業的經營決策提供有效的數據支持。商業智能BI軟體很好地解決了這一問題，它可以將企業信息化的數據孤島整合起來，提供一個全局的視圖，讓決策者可以更加全面地看待問題，降低決策失誤風險性。2、提升數據展示效率，深入分析問題，商業智能BI軟體能夠將數據實時快速地轉換成清晰明了的可視化報告，使決策者能夠迅速准確地作出決策，給企業注入新的革命性的管理思想。決策者可以根據商業智能BI軟體提供的鑽取功能對數據結果進行追根溯源，使問題的分析不止步於表面結果，發掘出數據中包含的機會：即如何以更低的成本、更快的速度、更高的質量完成任務；這使管理者能在質疑中不斷以創新來獲得差異化競爭優勢。BI商務智能軟體的預測功能使企業看問題更長遠，決策更具前瞻性。BI商務智能軟體會根據企業生產經營中積累的大數據進行挖掘，得到數據的之間潛在的規律或趨勢，進而做出下一步預測。4、幫助企業開源和節流，增加利潤IDC一項針對全球20個國家2000多家大中型穗滾企業進行的調查研究顯示，企業如果能採用一套完整的大數據解決方案（包括數據採集、整合、篩選、分析、分享），而不僅是單個數據管理技術，在未來四年中將從其數據資產中發掘出額外60%的數據紅利。5、風險預警，企業可以在BI商務智能軟體中設置數據報警閾值，數據一旦超標，系統會以各種手段通知到管理員，彎大使企業風險可控，減少安全漏洞。6、提高員工的工作效率，在沒有商業智能BI軟體之前，員工要寫大量復雜的SQL語句，製作大量報表以滿足業務要求，決策者在面對大量堆積報表數據也感到頭疼。而商業智能BI軟體則徹底改變了這種現狀，使業務人員可以輕松進行數據分析，使決策者可以自在查看分析，及時猜鬧余查看到決策所關心的數據。現在企業都有許多許多的數據，可是卻不能充分利用這些數據，如果有人可以合理利用BI工具，來幫助公司的規劃和決策，誰就可以搶佔先機，或者至少對自己對市場多一份理解。
⑸ 商務智能：到底什麼是商務智能
也許這里是對商務智能最好的介紹。
Business leaders have access to more data than ever before.
商業領袖比以往更需要處理更多的數據。
But data by itself doesn』t generate insights.
數據本身並沒有價值。
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.
商業智能工具已經成為幫助公司處理大量數據以及分析並制定更合理的決芹伍策腔滾的關鍵伍首余。
The specific definition of BI can vary depending on who you ask.
BI的准確定義要看誰（什麼角色）來問。
Here are a few examples of some of the ways business intelligence is defined:
這里是一些典型的對商務智能的定義：
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 簡單介紹一下加推
加推是什麼？
加推是一款賦能銷售的智能系統。加推將CRM（客戶關系管理平台）與小程序（智能名片系統、微商城、微官網）集成閉環系統，賦能銷售見面、跟進、成交、管理四個關鍵環節，提升企業人效。
加推簡介：
最初，加推希望為銷售打造一款武器，於是研究開發了智能名片系統，這款名片極大的提升了銷售的「見面」效率，不僅標准化的輸出了個人、產品和公司，快速傳播裂變。隨後加推團隊摒棄了傳統CRM手動錄入、強制管控的傳統設計理念，獨立設計了一套以賦能為核心的CRM系統，研發微信鏈接客戶，智能客戶畫像、B2S2C用客戶反饋來管理銷售的新型CRM系統，每個客戶都是活的，沉澱在公司獨立的小程序里，成為公司的核心資產。
加推是一家技術創新型公司，2018年5月獲得了來自紅杉資本、IDG 資本領投的1.68億融資，全年包攬17項科技獎項，其中包括中國互聯網創新創業大賽冠軍等含金量極高的科技大獎。
如何使用加推：
我們並不主張銷售只是銷售部的事。加推也無法獨立完成賦能銷售的使命，加推只是一個工具。事實上，銷售需要每個部門的賦能。品牌、培訓、產品部門在為銷售見面環節建設完整的微商城、微官網，時刻更新更加符合客戶需求的「話術庫」，銷售人員在遞出名片時才能讓公司與更好的與客戶「見面」；推廣部門需要實時推送內容，公司產品的賣點、變化、大事記、節日活動，這是銷售跟進客戶的「物料」，數據會量化說明推廣部門的價值；產品、供應鏈與財務部門需要及時更新產品庫、實時分佣；管理部門制定線索、客戶轉化等各項KPI，運營銷售過程。最後，加推為企業里每個人都建了一個站，我們每個人都可以為公司帶來流量，直接影響著銷售結果，加推也會統計每個人為企業帶來的價值，協同作戰、全員營銷。
加推的影響：
加推CRM+小程序的商業模式徹底解決了國外CRM產品模式在中國水土不服、難以使用的問題，並以1人1天1塊錢的互聯網式定價幫助企業使用CRM、微商城、官網的門檻降至最低，僅6個月，超過25000家企業開始使用加推，其中包括中國移動、中國人壽、科大訊飛、大自然家居、藍盾、創維、獐子島、橫店影視在內的200多家上市公司，領先的技術和革新的商業模式讓加推成為企業服務領域成長最快的公司之一。
⑺ 電信、金融等行業如何實現商業智能化
電信、金融等行業想要實現商業智能化。進行信息化管理。
就要實現企業的ERP管理。
企業在應用信息化軟體後，隨著管理需求的進一步深入，新的信息化需求也隨之誕生。鼎捷軟體未來在滿足這樣需求時不僅以開發新的技術領先的產品為核心競爭優勢，而且要採用單一軟體產品加技術元件整合為解決方案的方式，以ERP為核心應用平台，重新構建為服務搏漏舉型組合、行業型組合和規模型組合。
目前大多數本土軟體公司依然停留在單一軟體產品的優勢上，面對市場瓶頸，軟體廠商往往希望通過構建新的商業模式或概念，讓企業直接快捷的享受信息化成果，而不需要相關積累，這樣失敗的風險將會大大增加。
而鼎捷軟體有限公司不僅積累了所有具備優勢的單一產品，並基碧且擁有大量高滿意度的忠實客戶和實施經驗，因此，在以ERP為主要信息平台的基搜頌礎上，鼎捷軟體有限公司可以針對不同客戶需求，做出不同產品組合和延伸，滿足企業快速，敏捷的需求。
⑻ 商業智能BI工具的2大優勢盤點！
近年來，隨著大數據、數據分析技術的興起，商業智能BI工具應運而生，其中BI工具已成為眾多企業商務決策的重要工具。也許有人會問，為什麼企業需要 商業智能BI工具 ？商業智能BI工具可以為企業帶來什麼？
首先，我們先來了解商業智能BI工具的含義，然後再看看BI工具是什麼，以及商業智能BI是如何工作的。
商務智能(BusinessIntelligence)是指利用現代數據倉庫技術、在線分析處理技術、數據挖掘和數據呈現技術來實現商業價值的數據分析技術。
BI工具究竟如何運作？BI工具是由ETL、DW、OLAP、DM等多個環節組成的復雜技術集合。簡而言之，就是通過ETL工具，將交易系統中已經發生的數據，提取到主題明確的數據倉庫中，OLAP處理後產生的Cube或報告，通過Portal呈現給用戶，用戶利用這些分類(Classification)、聚集(Clustering)、描述和可視化(DescriptionandVisualization)的數據，支持業務決策。
商務智能BI工具可以為企業帶來什麼？商務智能BI是一套完整的解決方案，包含了數據分析過程，重點體現在把商業價值帶到企業中，用它把企業現有的數據進行有效整合，快速准確地提供報表和提供決策依據，幫助企業做出明智的經營決策。那麼，價值是怎麼實現的呢？
以國內主流商業智能BI工具之一思邁特軟體Smartbi為例，分析了商業智能BI工具在企業中的應用。
1. 直觀地呈現報表
BI工具中，利用柱狀圖、餅狀圖、折線圖、二維表格等直觀的圖形化方式，將企業日常業務數據(財務、供應鏈、人力、運營、市場、銷售、產品等)全部呈現出來，再通過各種數據分析、篩選、關聯、跳轉、鑽取等方式查看各類業務指標。
使用可視化的報告分析呈現方式，使用戶對日常業務有一個清晰、直接、准確的認知，同時解放了業務人員手工使用Excel的各種功能進行匯總分析、繪圖，極大地提高了工作效率。
思邁特軟體Smartbi大數據分析平台提供了豐富的ECharts圖形可視化選擇，（堆積）柱圖、（堆積）橫條圖、散點圖、（堆積）面積圖、折線圖、組合圖、瀑布圖、餅圖、環形圖、南丁格爾玫瑰圖、油量圖、散點圖、氣泡圖、雷達圖、關系圖、熱力圖、詞雲圖。此外，當通過電子表格(Excel)繪制圖表時，Excel可以用Excel完成更復雜的圖形設計，如：甘特圖、山形圖、手風琴圖、子彈圖、小型圖、迷你圖、漏斗圖、進度圖、復合餅圖、多層餅圖等。並將條件格式設置表格(數據條，色階，圖示集)。
2. 分析數據"異常"。
數據異常分析採用對比分析方法。若發現某些數據指標反映出的情況超出日常經驗判斷，則業務人員通過可視化報告呈現。在這個時候，就需要對這些「異常」數據進行有目的的分析，通過相關維度、指標鑽取、關聯等分析方法來探究可能的原因。
業務人員通過一次或多次多個維度和指標圖表，逐漸形成了一個可靠、固化的分析模型。現階段的業務人員不再被動接受圖表中反映的信息，而是通過「異常」數據定位背後的業務問題。數據和業務在這個層面上開始有直接的對應關系。此時，他們可以利用數據圖表之間的邏輯關系尋找解決方案，提高企業的運營效率。
總結：商業智能BI的本質是業務問題和管理問題。商業智能BI數據分析來源於業務，通過數據呈現發現業務問題，重新優化升級業務運營的過程，因此利用好商業智能BI工具提高業務能力，取得更好的業績。
⑼ 企業實施商務智能應注意三方面
企業實施商務智能應注意三方面
從實際情況出發：「從實際情況出發」是我們平時說得最多的一句話。然而在商務智能系統的實施中，我們還是要老調重彈。雖然說企業的商務智能能夠發現隱藏的成本和潛在增加營業收入的機會，但是並非所有的企業都適合實施商務智能系統。這需要從企業發展的規模、戰略目標設定等角度來考慮。我國很多企業都存在著盲目的跟風現象，每當出現一種新的技術時，便不顧企業的實際運營狀況，一味投入大量人力、物力和財力，以為這樣做便能抓住新陪野技術的脈搏，提升企業的競爭力，其結果往往事與願違。一旦決定實施商務智能系統，就意味著企業已經具備了相當的軟、硬體條件，在能保證回答諸如「誰是你的用戶？你有前台技術精力嗎？你的滲渣公司在全國甚至全球都需要商務只能么？」這一類問題的基礎上，從業務領域著手，結合業務部門和IT部門協力制定數據框架，把商務智能作為業務戰略的一部分來看待，確保商務智能系統在公司的戰略地位。
企業實施商務智能的問題：雖然商務智能系統在許多企業，特別是國外的大企業中得到了越來越廣泛的應用，但在實際的建設和使用過程中，仍然存在一些問題。首先是存在軟體功能與用戶需求之間的差異；在商務智能系統的建設過程中，往往是IT經理和資深用戶追求所用的功能，而對於大多數使用者：業務管理者、高層管理人員和業務專家等，卻缺少容易使用、熟悉的工具界面和分析方式，這使得他們不願花時間和精力學習。若用戶無法有效地運用智能系統來贏得事實上的競爭優勢，則巨額投資無法彌補，系統的商業價值也將被質疑。因此，企業在搭建商務智能系統時，應注意「量身定做」，選擇合適自己的工具和系統。其次是來自技術領域的困難；計算機處理技術和存儲能力的迅速發展，帶來了信息量的冪級增長，時下的數據倉庫通常都超過了100GB,而且容量超過1TB的數據倉庫系統的數量正在急劇增長。隨著數據量的增長，數據關系的可能的排列方式也大幅度增加，傳統的商務智能工具已經顯得力不從心。比如聯機分析處理系統通常只能處理10-20GB的數據。因此，要充分發揮商務智能系統的功能，企業需要更強有力的工具，這有賴於人工智慧、機器學習、數據倉庫技術、專家智能系統等科學技術的進步和發展。
來自文化上叢亂悄的挑戰。商務智能體系的建立是一項長期、艱巨的任務，執行起來不僅有技術上的困難，而且有文化上的挑戰。有很多企業（特別是中國企業）從來沒有真正用過商務智能，無法認識到商務智能是如何改變了企業經營，使企業變得更加高效，更加強大。而且，有些企業害怕商務智能體系所洞察出的弱點和弊端，從而「諱疾忌醫」。