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Can an enterprise can successfully apply business intelligence depends on two factors: the first, whether there is a correct data, that is, whether the data is complete, accurate, consistent, timely data; second, whether there is data to convert data to decision information Tools and means. To solve these two problems, there is a need for a certain architecture and support technology.
The architecture of business intelligence is as follows:
1. Data source system: including the front-rear end OLTP (On-line Transaction Processing, online transaction), e-commerce system and external information provider, and more. These are not only a data source, but also the operational objects of knowledge and actions.
2. Business Intelligence Tools: including data warehouse models and constructors, access tools, decision support tools OLAP and data mining tools.
Data Warehouse Model and Construction Tool
Includes ETL (Extract / Transformation / Load) tools and data warehouse templates, metadata exchange, joint data warehouses, and data market systems. These tools are used to capture data from the operating system and the external data source system. After data processing and conversion, it is finally loaded into a global or departmental data warehouse.
Access tool
Including application interfaces and middleware, demand drive data, the model, rules, and metrics of the decision engine allows customers to access and process business information in the database and file systems. The database middleware allows customers to transparently access various heterogeneous database servers in the background, and the web server middleware allows web customers to connect to the database. These products are used to manage business information that end users interested. Generally, the three-layer information storage, the highest level is the data warehouse, the data warehouse integrates commercial information; the intermediate-level is a departmental data warehouse, and it is called a data market, which stores a business unit, user group or department. Commercial information, these data markets can be directly established on the basis of enterprise operating systems;
The minimum level of the structure stores information after cropping according to user and application requirements.
Decision Support Tools OLAP and Data Mining Tools
Includes all kinds of tools from basic queries and report tools to advanced online analysis processing to information excavation tools. All tools support the GUI customer interface. Many can also be used on the web interface. Now, most of these tools can be designed to handle structural information from database products, but will need complex and non-structured information on file systems, multimedia, and even email or web servers.
3. Commercial intelligent application system
Including human resource management, analysis and reporting, financial management, customer resource management, analysis and reporting supply chain management, corporate plan management analysis and reporting. These applications are many complete business intelligence solutions for cropping in different industries or applications. Many information management systems are built on relatively dispersed architectures. The various departments of the enterprise are one piece of information, and the information between each other is difficult to share. In order to get true intelligent enterprise management, the information structure must be seamless with commercial intelligence.
4. Knowledge and action application system
Including corporate knowledge management portals, business information and suggestions and knowledge actions. BI software providers typically provide a single, network-based entry portal to provide reports, OLAP (On-line Analysis Processing) and data mining information. These entrances are different from the enterprise information portal (EIP, Enterprise Information Portal), usually not directly accessed by the user. By providing a connection to the most popular universal EIP to implement the BI application, it is a key component of a comprehensive information management.
Third, business intelligence support technology
Commercial intelligence support technology is mainly composed of data warehouse (DW), online analysis processing (OLAP), and data mining (DM).
Data warehouse
The data warehouse is the foundation of business intelligence, and many basic reports can be generated, but its greater use is a data source that is further analyzed. The so-called data warehouse is the topic, integrated, stable, different time data sets to support decision making processes in business management. Multi-dimensional analysis and data mining are the most commonly heard examples, and the data warehouse can supply them, neatly consistent data they need.
In order to effectively perform data management, companies often need to summarize the data from all over the headquarters and build a huge data warehouse. This data warehouse can not only save historical data, staged data, and analyze from time, and can load external data, and accept a large number of external queries.
The process of establishing a data warehouse typically includes extracting operational data, setting the automatic program as required to set the operation data and automatically update the data warehouse, and perform the total calculation of the total calculation.
Fast, simple, easy-to-use query and reporting tools help managers fully utilize different levels of data in the enterprise, obtain specific information required, and display in a reasonable format. At the same time, excellent tools support a variety of network environments, allowing users to transmit analysis results on client / server networks, internal networks, or Internet. They should also have sufficient flexibility to support various types of queries and reporting requirements, from simple subscription, periodic reports to use SQL and other query languages random query.
2. Online Analysis Processing (OLAP) online analysis processing is a highly interactive process, and information analysis experts can instantly analyze and quickly obtain the desired results. Online analysis processing is also a process of analyzing data stored in a multidimensional database (MDD) or relational database (RDBMS). This analysis can be multi-dimensional online analysis, relational online analysis processing, or mixed online analysis processing.
3. Data mining
Data mining is the process of discovering the previous unknown, understandable information from the vast amount of data and documentation. Since data mining value is to scan data warehouses or establish a very complex query, data and text mining tools must provide high throughput and have parallel processing capabilities, and multiple acquisition technologies can be supported. Data mining tools should have good extension features and support various data (or documents) and computing environments that may be encountered in the future.
Fourth, the role of business intelligence solutions in business operations
Business Intelligence Technology is an advanced technology that helps companies complete information collection and analysis. It contains all queries and reports in the decision-making process, online analysis processing (OLAP) and information acquisition applications and tools. The role of business intelligence solutions in business operations is mainly in three areas:
1. RELATIONSHIP Marketing: It is vital to maintain customers' attention to companies through effective exchanges and good service. Business intelligence passes through helping companies complete customer division, customer acquisition, cross-selling, customer retention, etc., allowing companies to customize products, services, and "face-to-face" customers according to customer needs Communication. 2. Profitability Analysis: Business Intelligence Solutions can help enterprises analyze the source of profits, and all kinds of products have the degree of contribution to total profits, and whether advertising costs are proportional to sales.
3. Reduce costs: Business intelligence technology can help companies reduce costs in which areas where the smallest business is minimized. Decisive decisions to reduce costs can be based on detailed target data.
V. Business Intelligence Application
Business intelligence has been applied to many fields. Some applications are as follows:
(1) Retail
Business intelligence has the following applications in the retail industry:
Prediction: Prediction of demand, better manages inventory based on forecast results.
Marketing: Analysis of customer data, not only knowing what to sell, but also understand what "who" bought, and realize the marketing of consumers "pull".
Product sales model: sales characteristics of a product, relationship between different products, as a reference for the purchase and store layout.
(2) Insurance industry
Business intelligence has the following applications in the insurance industry:
Claims analysis: According to the insurance, policyholders, claims, and other characteristic analysis of the claims, to determine the quantity of the reserve, claims analysis can help identify insurance fraud.
Customer Profit Rate Analysis: Quantitative analysis of the cost of different varieties, different regions, different agents, different customer bases, to find out the difference in profit margin, to use the development of new varieties Customer improvement improvements in existing varieties and identify customers who can bring high profit margins.
Customer value analysis: Customer profit margin is not the only indicator of customers for insurance companies, maybe a customer has the potential to purchase high-profit insurance products in the future, maybe become a good high-profit margin customer introducer, so we must consider The value of customers in the entire process of dealing with insurance companies.
Customer Division: Customers with a variety of common features are divided into different customer bases, master the needs of their needs and products, to identify marketing solutions; analyze the profit margins of the principal, identify opportunities, and improve services.
Risk Analysis: Learn about the risk of introducing new insurance and developing new customers. Identify high-risk customer groups and customers who can bring opportunities to reduce the rate of compensation.
(3) Finance and securities industry
Business intelligence has some of the following applications in the financial and securities industry:
Customer Profit Rate Analysis: Understand the current and long-term profit margins. Make the sales of high-value customers to reduce costs for low-value customers.
Credit Management: Understand the credit status of various products, establish a credit model of different customer groups, and help customers avoid the occurrence of credit issues, predict the impact of credit policy changes, and reduce credit losses;
(4) Telecommunications industry
Business intelligence has the following applications in the telecommunications industry:
User Division: Analyze users using the historical data of telecom products, analyzing user call behavior, providing personalized services and effective incentives;
Demand Analysis: Analyze the use of various products and cost data of users, in-depth understanding of customers for new products and services, analyzing communication network investment, pricing and competitiveness.
(5) Manufacturing
Business intelligence has some applications in the manufacturing industry:
Marketing: Provides customer-oriented trading data, realizing marketing of consumers "pull";
Prediction: (with the retail industry); procurement analysis: Master the cost, delivery and timely function of suppliers;
