BI (Business intelligence) editing
Business Intelligence (Bi,business Intelligence). BI (Business Intelligence) is a complete solution for effectively consolidating existing data in the enterprise, providing quick and accurate reporting and decision-making basis to help enterprises make informed business decisions.
- 1 Introduction
- 2 BI Vendors
- 3 features
- ? Main Architecture
- ? Application Scope
- 4 Applied Sciences
- 5 of three levels
- 6 Development Process
- ? Development Trend
- ? Smart Controls
- 7 BI Software
Introduction EditBusiness Intelligence (Bi,business Intelligence). BI (Business Intelligence) is a complete solution for effectively consolidating existing data in the enterprise, providing quick and accurate reporting and decision-making basis to help enterprises make informed business decisions. The concept of business intelligence was first proposed in 1996. At that time, business intelligence was defined as a kind of technology and its application, which consisted of data Warehouse (or data mart), query report, data analysis, data mining, data backup and recovery, to help enterprise decision-making. The data may come from business systems such as CRM and SCM. Business intelligence can assist in business operation decisions, either as an operational layer or as a tactical and strategic layer. To turn data into knowledge, you need to leverage technologies such as data warehousing, online analytical processing (OLAP) tools, and data mining. Therefore, from the technical level, business intelligence is not a new technology, it is only data warehousing, OLAP and data mining technology, such as the comprehensive use. It should be more appropriate to think of business intelligence as a solution. The key to business intelligence is to extract useful data from a number of data from different enterprise operating systems and clean it up to ensure the correctness of the data, then extract (Extraction), transform (transformation), and load (load), the ETL process, Merged into an enterprise-level data warehouse, so as to obtain a global view of enterprise data, based on the use of appropriate query and analysis tools, data mining tools, OLAP tools and other analysis and processing (this time information into the knowledge of auxiliary decision), and finally the knowledge presented to the manager, Provide data support for the decision-making process of the manager. Business intelligence products and solutions can be broadly divided into Data warehouse products, data extraction products, OLAP products, display products, and integration of the above several products for an application of the overall solution.Bi Vendor EditIBM, Oracle, and Microsoft product lines in foreign-funded companies cover all bi areas, especially MicroStrategy independent bi vendors. Three products have better performance to meet the needs of large and small businesses, where Microsoft's SQL Server products are more cost-effective than the other two. Informatica is a leader in data integration and the market is performing well. SAP continues to expand the business objects market share based on ERP. Sybase and Teradata focus on the field of data warehousing, many domestic competitors, but market development needs to be strengthened. SAS software is leading in the field of data mining, and sales are mainly based on system integration.feature editingMany vendors are active in Business Intelligence (hereinafter referred to as BI). In fact, BI products and solutions that meet the needs of users must be built on a stable, consolidated platform that provides user management, security controls, connected data sources, and the ability to access, analyze, and share information. The standardization of BI platform is also very important, because this is related to the Enterprise multiple application system compatibility problem, can not solve the compatibility problem, the BI system can not play its due effect. Here we introduce the BI system by functional anatomy of a lab BI system model, which we call D systems. D system is a face-to-end users, direct access to business data, can enable managers from all angles to analyze the use of business data, timely grasp of the Organization's operational status, make scientific management decision-making system. D systems can be browsed from simple standard reports to advanced data analysis to meet the needs of people within the organization. The D system covers the functions of the Business Intelligence (BI) system in the conventional sense, and the main architectures include the following aspects. Main Architecture
1. Read Data
The D system can read files in a variety of formats (such as Excel, Access, tab-separated txt and fixed-length txt, etc.) while reading data from a relational database (corresponding to ODBC). On the basis of reading text and data, the D system can also be completed: The connection text to the 2 CSV files in the common items as key (key), the data needed to merge into a file, so as to operate the database as convenient, but without user programming can be achieved. Set the project type as the item type for the data, in addition to buttons (button) (text items), numeric items, you can also set date data items for date representations, multimedia items, and reference items that do not require a build button but can be browsed in the list display. Period Set Date project data can be generated according to the annual or quarterly combination after the new?? A new time item is generated after an afternoon or time band combination. Set level for numeric items, you can set any level to generate the corresponding button. For example, you can generate a button that corresponds to a 20-year-old, 30-year-old age group in an age item.
2. Analysis function
Association/Qualification Association analysis is primarily used to identify correlations between events, where one event occurs and the other occurs frequently. The focus of relevance analysis is to quickly discover events that have practical value associated with them. The main basis is that the probability and condition probability of event occurrence should accord with certain statistic meaning. The D System Designs this association analysis into the form of a button, by choosing to have/not correlate, and/or opposite associations. For structured data, taking the customer purchase habit data as an example, using the D system's correlation analysis, we can discover the customer's related purchase needs. For example, a customer who opens a savings account is likely to have a bond transaction and a stock exchange at the same time. Using this knowledge can take an active marketing strategy, expand the range of products purchased by customers, attract more customers. Show numerical scale/indication display order D The system enables the proportional relationship between the data of a numeric item to be rendered by the size of the button, showing its composition ratio, and changing the order of the numeric item data. When the button is selected, the dynamic display changes continuously. This results in an intuitive data comparison that highlights differences and facilitates deeper analysis of the nature behind the phenomenon. The monitoring function pre-sets the condition so that the condition-compliant button displays an alarm (red), note (yellow) signal, so that the problem is at a glance. For example, a store warning (yellow marked) with a sales volume of less than 1 million yuan in the previous quarter, with less than 500,000 yuan of alarms (red marked). After execution, the D system will show the button with the name of the store in the corresponding color. The button increment function combines multiple buttons to form a new button. For example: the "April", "May", "June" three buttons combined to get the New button "2nd quarter". The record selection function selects the button from a large amount of data and extracts the necessary data. The selected data can be re-formed in the same operating environment. This allows users to focus on the data they care about. The multimedia information means that the function is saved by a digital camera, such as a photo or image file, a multimedia file such as a graphics input by a scanner, a text processing or a report made by a spreadsheet software, a document such as the standard form of HTML, etc., which can be searched by a button. Split button function in the case of splitting a particular button class, simply switch the individual buttons that are split, and you can connect to the ongoing training process that has been signed in. The program calls the function to find the extracted data through the button, pass to other software or user's original program, and execute these programs. Find button name function you can specify both exact and fuzzy find methods by using the button name lookup button. In addition, other button classes can also limit the data associated with finding results.
3, the rich picture
The list screen can be changed by and/or to find the criteria, can be counted/sorted. The statistical object is only for numerical items, the statistical method is divided into three kinds: total, number of pieces, average, and can change the display format of the numerical value in 12 ways. The view screen provides a toggle view and Transform views feature, which is emphasized by changing the color of the values (cells) corresponding to the setting criteria. In order to transform the angle of view can be analyzed in many ways. The statistical objects of the view are only for numerical items, and the statistical methods are 12 kinds: total, average, composition ratio (portrait, landscape), Cumulative (portrait, landscape), weighted average, maximum, minimum, newest and absolute. Numeric item switching through the hierarchy of button classes (the rows and columns can be set to a maximum of 8 layers), from the whole to the local, layered downward mining, while analyzing the data, you can more clearly explore the problem. Chart D system uses its own development of the graphics library, provide column chart, line chart, pie chart, area chart, column + polyline five categories of 35 kinds. On the chart screen, you can also freely dig and return layers as you would in hierarchical view.
4. Data output function
Print statistics list and chart screen, etc., can output statistical analysis of good data to other applications to use, or in HTML format to save.
5. Stereotyped treatment
When the required output is displayed, a training login is created, and the training processing button is automatically generated. Later, just press this button, and even complex operations can display the list, view, and chart you want.Application ScopeBusiness Intelligence systems can assist in establishing information centers, such as generating various work reports and analysis reports. Used as the following analysis:
The main analysis of sales indicators, such as gross profit, gross profit margin, cross-ratio, sales, profitability, turnover, year-over, chain, and so on; Analysis dimension can be viewed from the view of management structure, category brand, date, time period and so on, these analytic dimensions use multistage drilling to obtain quite thorough analysis thought At the same time, according to mass data to generate predictive information, alarm information and other analytical data, but also based on a variety of sales indicators to generate a new pivot table.
The main data of commodity analysis comes from sales data and commodity base data, which leads to the analysis thinking of analyzing structure as a main line. The main analysis data have commodity category structure, brand structure, price structure, gross profit structure, settlement style structure, origin structure and so on, resulting in product breadth, commodity depth, commodity elimination rate, commodity introduction rate, commodity replacement rate, key commodities, best-selling goods, slow-moving goods, seasonal commodities and other indicators. Through the D system to the analysis of these indicators to guide the adjustment of the enterprise's commodity structure, strengthen the competitive ability of the goods and reasonable allocation.
Through D system to the company's personnel indicators analysis, in particular, sales personnel indicators (mainly sales indicators, gross profit index, the number of goods sold, consignment goods, capital use, capital turnover, etc.) analysis, in order to achieve the assessment of staff performance, improve staff enthusiasm, and for the rational use of human resources to provide scientific basis. The main analysis of the subject, staff composition, sales per capita sales, sales of personal sales performance, each management structure of the per capita sales, gross profit contribution, purchasing personnel in charge of the purchase of goods, the proportion of sale and distribution, the introduction of merchandise sales and so on.Applied Science Editor
End-user query and reporting tools
Raw data access specifically designed to support novice users, excluding finished report generation tools for professionals. OLAP tools. Provides a multidimensional data management environment that is typically used to model business issues and analyze business data. OLAP is also known as Multidimensional Analysis.
Data Mining (Mining) software
Use techniques such as neural networks and rule induction to discover the relationships between data and make data-based inferences.
data Warehouse and Data Mart (Mart) Products
Pre-configured software, including Data transformation, management, and access, often includes business models such as financial analysis models. The concept of online analytical processing (OLAP) was first proposed by the parent of the relational database e.f.codd in 1993, and he also proposed 12 guidelines on OLAP. OLAP has aroused a great deal of repercussions, OLAP as a class of products and online transaction processing (OLTP) clearly distinguished. Today's data processing can be broadly divided into two broad categories: online transaction processing OLTP (on-line Transaction processing), online analytical processing OLAP (On-line Analytical Processing). OLTP is the main application of the traditional relational database, mainly basic, daily transaction processing, such as bank transaction. OLAP is the main application of Data Warehouse system, supports complex analysis operations, focuses on decision support, and provides intuitive and understandable query results. OLAP is a type of software technology that enables analysts, managers, or executives to access information quickly, consistently, and interactively from multiple angles to gain a deeper understanding of the data. The goal of OLAP is to satisfy the decision support or to meet the specific query and reporting requirements in the multidimensional environment, and its technical core is the concept of "dimension". "Dimension" is the angle that people observe the objective world, and it is a kind of high-level classification. "Dimensions" generally contain hierarchical relationships, which can sometimes be quite complex. By defining several important attributes of an entity as multiple dimensions (dimension), the user can compare data on different dimensions. Therefore, OLAP can also be said to be a collection of multidimensional data analysis tools. The basic Multidimensional Analysis operations of OLAP are drilling (roll up and drill down), slicing (slice) and cutting (dice), rotating (pivot), drill across, drill through, etc. Drilling is changing the dimensions of the dimension, transforming the granularity of the analysis. It includes drill up and drill down (drill down). Roll up is to summarize low-level detail data in a certain dimension to a higher summary of data, or to reduce the number of dimensions, while drill down is the opposite, from aggregated data to detailed data to observe or add new dimensions. Slices and dice are concerned with the distribution of the measurement data on the remaining dimensions after the selected values are on a subset of dimensions. If the remaining dimension is only two, it is a slice, and if there are three, it is cut into pieces. Rotation is the direction of the transformation dimension, which is to rearrange the placement of the dimensions in the table (for example, row and column swaps). OLAP has a variety of implementation methods, according to the way to store data can be divided into ROLAP, MOLAP, HOLAP. ROLAP represents an OLAP implementation based on a relational database (relational OLAP). With the number of relationshipsAccording to the core of the library, the representation and storage of multidimensional data is based on the relational structure. ROLAP divides the multidimensional structure of a multidimensional database into two types of tables: a fact table that stores data and dimension keywords, and a dimension table that uses at least one table for each dimension to hold descriptive information about the dimensions of the dimension, the member category, and so on. Dimension tables and fact tables are linked together by primary and external keywords to form a "star pattern". For hierarchical complex dimensions, to avoid excessive storage space for redundant data, multiple tables can be used to describe the expansion of this star pattern as "snowflake mode". MOLAP represents an OLAP implementation based on a multidimensional data organization (multidimensional OLAP). At the core of multidimensional data organization, which means that MOLAP uses multidimensional arrays to store data. Multidimensional data in the storage will form a "cubic block (Cube)" of the structure, in MOLAP "cubic block" of "rotation", "cut", "slice" is the main technology to produce multidimensional data reports. HOLAP represents an OLAP implementation based on a hybrid data organization. This approach offers better flexibility. There are other ways to implement OLAP, such as providing a dedicated SQL Server that provides special support for SQL queries for some storage modes, such as Star and snowflake types. OLAP tools are online data access and analysis for specific issues. It analyzes, queries, and reports on data in a multidimensional way. Dimension is the specific angle at which people observe data. For example, when an enterprise considers the sales of a product, it usually takes a closer look at the sales of the product from different angles of time, region, and product. The time, region and product here are dimensions. The multi-dimensional arrays of these dimensions are the basis of OLAP analysis, and can be formally represented as (Dimension 1, Dimension 2, ...), dimension n, metrics, such as (region, time, product, sales). Multidimensional Analysis refers to the multi-dimensional organization of data to take slices (Slice), Cut (Dice), drilling (Drill-down and roll-up), rotation (Pivot) and other analytical actions, in order to analyze data, so that users can view the data from multiple angles, multi-side to the database To gain a deeper understanding of the information contained in the data. Mainstream business intelligence tools include Bo, COGNOS, Style Intelligence, BRIO. Some of the domestic software tools platforms, such as kcom, also integrate some basic business intelligence tools. According to the different organization of the comprehensive data, the common OLAP mainly includes the MOLAP based on multidimensional database and the ROLAP two based on relational database. MOLAP organizes and stores data in multidimensional ways, and ROLAP simulates multidimensional data using existing relational database technologies. In data warehousing applications, OLAP applications are typically front-end tools for data warehouse applications, and OLAP tools canUse with data mining tools, statistical analysis tools, and enhance decision analysis capabilities. three levels of editing After several years of accumulation, most of the large-scale enterprises and institutions have established a relatively perfect CRM, ERP, OA and other basic information systems. The unified characteristics of these systems are: through the operation of the business personnel or users, the final database is added, modified, deleted and other operations. The above system can be unified called OLTP (online Transaction process, on-line transaction processing), refers to the system has been running for a period of time, it is necessary to help enterprises collect a large number of historical data. However, the large amount of data that is scattered and isolated in the database is a heavenly book for business people who cannot read. What business people need is information, abstract information that they can understand, understand, and benefit from. At this time, how to transform data into information, so that business people (including managers) can fully grasp, use this information, and assist decision-making, is the main problem of business intelligence. How do you turn data that exists in a database into information that a business person needs? Most of the answers are reporting systems. Simply put, the reporting system can already be called BI, which is the low-end implementation of BI. Foreign enterprises, most of which have entered the mid-end bi, is called data analysis. Some companies have started to enter high-end bi, called data mining. However, most of our enterprises still stay in the reporting stage. Data reports can not replace the traditional reporting system technology has been quite mature, we are familiar with Excel, Crystal Reports, Reporting service, etc. have been widely used. However, with the increase of data and demand, the traditional reporting system is facing more and more challenges. 1. Too much data, too little information dense tables piled up a lot of data, in the end how many business people carefully look at each data? What information and trends does the data represent? The higher the level of leadership, the more concise the information is needed. If I were the chairman, I might just need a word: is our situation good, medium or bad? 2. Difficult to analyze interactively, to understand the various combinations of customized good report is too inflexible. For example, we can list the sales of different regions, different products in one table, and the sales of customers from different regions and different ages in the other table. However, these two tables are unable to answer questions such as "The situation of customers buying digital camera type products in North China". Business problems often require multiple angles of interaction analysis. 3. It is difficult to find out the potential rules the report system often lists the data on the surface, but what are the underlying rules in the depths of the vast data? What customers are most valuable to us and to what extent are the products interconnected? The deeper the rules, the greater the value of decision support, but the harder it is to dig out. 4. It is difficult to trace back the history, the data forms the Islanding business system many, the data exists in the different place. Too old data is often backed up by business systems, which makes macro analysis and long-term historical analysis difficult. Therefore, with the development of the Times, the traditional reporting system has not been fullGrowing business needs, businesses are looking forward to new technologies. The era of data analysis and data mining is coming. It is worth noting that the purpose of data analysis and data mining system is to give us more decision support value, not to replace the data report. The reporting system still has its irreplaceable advantages, and will continue to coexist with data analysis and mining systems for a long time. Eight-D data analysis If OLTP focuses on daily transaction operations such as adding, modifying, and deleting databases, OLAP (online analytics Process, on-line analysis System) focuses on macro issues, comprehensively analyzing data, and gaining valuable information. In order to achieve the purpose of OLAP, the traditional relational database is not enough, need a new technology called multi-dimensional database. The concept of multidimensional databases is not complex. For example, we want to describe the April 2003 Coke in the northern region sales of 100,000 yuan, involving several angles: time, products, regions. These are called dimensions. As for sales, it's called a measure. Of course, there are costs, profits and so on. In addition to time, products and regions, we can also have many dimensions, such as the gender of the customer, occupation, sales department, promotion method and so on. In fact, the multidimensional database in use may be a 8-or 15-dimensional cube. Although the structure of the 15-dimensional cube is complex, it is conceptually simple. The overall architecture of the data analysis system is divided into four parts: source system, Data Warehouse, multidimensional database, client. • Source system: Includes all existing OLTP systems and no need to change existing systems to build a bi system.analysis of sales in a case and classification of current products• Data warehousing: Data is concentrated, through data extraction, the data from the source system is continuously extracted, maybe once a day, or every 3 hours, of course, is automatic. Data warehouses are still built on relational databases and often conform to models called "star structures". • Multidimensional database: Data Warehouse data is modeled in multidimensional form and the cube structure is formed. Each cube describes a business topic, such as sales, inventory, or finance. • Client: Good client software can present the information in multidimensional cubes to the user in a colorful way. Data analysis case: In a practical case, we built a data warehouse using oracle9i, and Microsoft analysis Service 2000 built a multidimensional database, ProClarity 6.0 as the client analytics software. The Decomposition Tree looks like an organization chart. Decomposition Tree in answer to the following questions, the highest sales? · What is the distribution of sales among various products within a particular product category? · Which salesperson completed the highest percentage of sales? In Figure 1, you can get a clear view of the PC's sales and percentage in each region. Any layer of Decomposition Tree can be arbitrarily expanded according to different dimensions. In this decomposition Tree, in the region this layer is based on the country, in the country this layer is classified by product. The projection diagram (Figure 3) uses the format of a scatter plot to display the relationship between two or three measures. The concentration of the data points indicates a strong correlation between the two variables, and the sparse distribution of the data points may show a non-obvious relationship. Projection plots are ideal for analyzing large amounts of data. There are obvious effects in showing causality, such as exceptional data points that can be considered for further study because they fall outside the "normal" point group range.data analysis projection for a caseData mining see through your needs broadly speaking, any process of mining information from a database is called data mining. From this point of view, data mining is bi. But in terms of technical terminology, data Mining refers specifically to the fact that the source data is cleaned and transformed into a data set suitable for mining. Data mining completes the abstraction of knowledge in this kind of data set with fixed form, and finally uses the appropriate knowledge pattern to analyze the decision-making work. From this narrow point of view, we can define: Data mining is the process of refining knowledge from a particular form of data set. Data mining often chooses one or more mining algorithms for specific data and specific problems, and finds the laws hidden under the data, which are often used to predict and support decision-making.Development Process EditorAs early as 1958, people are thinking of the computer has a strong computing power and more to help people do things, at that time, business intelligence has a prototype, 54 years past, how many companies in this goal to pay their own efforts? The following information icon tells you the hard way of business intelligence and the coming future in a timeline. Development TrendCompared with DSS and EIS system, business intelligence has better development prospect. In recent years, the business intelligence market has continued to grow. IDC predicts that by 2005, the BI market will reach 11.8 billion $, with an average annual growth rate of 27% (information Access Tools market Forecast and analysis:2001-2005, idc#24779, June 2 001). With the introduction of Enterprise CRM, ERP, SCM and other application systems, enterprises do not stay in the transaction process and focus on the effective use of enterprise data for accurate and faster decision-making support needs more and more strong, the resulting demand for business intelligence will be huge. The trend of business intelligence can be summed up in the following points: functionally configurable, flexible, and scalable BI systems extend from the specific User Service for the department to the service of all users throughout the enterprise. At the same time, due to the difference in authority and demand of enterprise users, BI system provides a wide range of targeted functions. From simple data acquisition, to the use of Web and LAN, WAN for rich interaction, decision-making information and knowledge analysis and use. The solution is more open, extensible, can be customized according to the core technology, while providing a customized interface for different enterprises unique needs, BI system in the provision of core technology at the same time, the system is also personalized, that is, based on the original solution to add their own code and solutions, enhance the customer interface and extension features Provides business intelligence platform-based customization/p> from individual business intelligence to embedded business intelligence This is a big trend in business intelligence applications where business intelligence components are embedded in existing applications such as finance, manpower, sales, etc. The characteristic of business intelligence in the general sense of transaction processing system. Considering a component of a bi system rather than the entire BI system is not a simple matter, such as the application of OLAP technology to an application system, a relatively complete business intelligence development process, such as enterprise problem analysis, program design, prototype system development, System application process is indispensable. Transforming the enhanced business intelligence functionality from traditional functions to enhanced functionality is a business intelligence feature that is relative to the early implementation of queries with SQL tools. In addition to implementing traditional BI system functions, most of the BI systems in the application have realized the functions of the data analysis layer in Figure 2. and data mining, enterprise modeling is the application that bi system should strengthen, in order to improve the system performance better. ERP system is a typical OLTP (online online processing) system, the BI system is OLAP (online analysis) system, their focus is different, there are different functions and tasks. ERP systems are used to process business processes quickly and efficiently, including the most original and detailed documents. BI systems do online analysis of massive business data to generate decision information and knowledge, which can include not only detailedDocument, the most important thing is to make a summary analysis of the document according to the decision requirements. From traditional bi to Agile bi transform SAP Business OBJECTS,IBM Cognos, Microstrategy, Oracle BIEE, Microsoft and more mature BI products dominate the market. The concept of BI was first proposed by Gartner in 1996. Bi is a commonplace topic in China. The BI market has been surging a few years ago, but as the IT giants put the bi-vendor in the bag, such markets tend to stabilize. Although traditional bi vendors dominate the mainstream, they are inherently deficient. The traditional BI solution is basically two kinds of ideas, one is big data integrated machine, the other is distributed data Warehouse. However, the total cost of ownership of big data integrated machines is high, most enterprises are not blessed, and distributed data warehouse by traffic charges, the use of expensive, 1TB data may need hundreds of thousands of. Such big data products dramatically increase the total cost of ownership (TCO) of BI applications. In today's BI market, new trends are emerging and gaining momentum, such as agile bi and exploratory bi. In the past two years in the United States, two companies Qliktech and Tableau have successfully listed and entered the mainstream market, and in China has emerged in the Yonghong technology as the representative of Agile bi manufacturers. Compared with other types of BI products, agile bi input cost is lower, more civilian, more easy to operate, so that more enterprise customers can enjoy the most professional big data service with lower input.  Smart ControlsBusiness intelligence controls  by encapsulating professional business intelligence capabilities, developers can implement common business intelligence capabilities in management systems without having to systematically master business intelligence-related expertise. Business intelligence controls enable developers to create applications or systems that meet almost every BI requirement for enterprise users, enabling the ability to fully unlock business information with Chinese-style complex reports (web-based reports), text reports, various types of data analysis (human resources, finance, sales, marketing, supply chain, etc.), and various chart displays. To maximize the competitive advantage of the enterprise. 1.
is a highly flexible and fast computing full-featured OLAP control set, the best choice for BI solutions! 2.
Includes: OLAP modelkit™--for multidimensional data analysis, and Chart modelkit™--for graphical data display components. 3.
Is the latest OLAP product developed by Pivotware Labs. 4.
: The product fully supports user customization, fully supports local integration with the DevExpress Chart control, and enables end users to create almost infinite arrays of reports with simple drag and click. 5.
: Pivot Table & Charts component is a rich Internet application designed to view, analyze and manage multidimensional data online. With pivot Table, you can view the same information in different ways with just a few clicks of the mouse.bi Software editingBI software is the acronym for Business Intelligence (Intelligence) software. Business intelligence is often understood as a tool to transform existing data in an enterprise into knowledge and to help companies make informed business decisions. The data in a business intelligence system comes from other business systems in the enterprise. For example, business Intelligence system data includes orders for business systems, inventory, transaction accounts, customer and supplier information, as well as data from industry and competitors, and other external environmental data. And these data may come from the Enterprise CRM, SCM, Invoicing and other business systems.
BI (Business Intelligence)