About BI (Business intelligence)

First, what is bi

BI (Business Intelligence) makes the "game rules" for massive cloud data (different analysis of different topics), collects, integrates, cleans up and diagnoses scattered data, and then transforms the data into information and knowledge by means of certain analysis methods, and provides quick and accurate report and decision basis. In turn, the enterprise managers to make intelligent business management decisions to provide support.

Second, the content

BI's analysis and processing of data mainly includes three aspects. First, it is the establishment of a data warehouse or data mart to preprocess the data.        BI takes the enterprise management demand as the foundation, according to the different analysis topic, extracts the useful data from the enterprise many different operating system's data, in order to guarantee the data correctness, then through the extraction, the transformation and the loading, namely the ETL process, merges into an enterprise level data warehouse, obtains the enterprise data the global view. Specifically divided into three layers:1, ODS layer (operational data Store) This layer is mainly through the ETL process to extract the database data as the Data Warehouse metadata (table) data may originate from different databases and data table http://www.uisftech.com/html/default/1270109814985505/2299890-12679742/24777320-1268742/3027538310879288/2012/ 2698594010904952.htmhttp//blog.itpub.net/10009036/viewspace-1061623/2, data sharing layer (data Warehouse) DW-The data Warehouse layer consolidates and transforms the data in the ODS layer through the ETL process (similar view) http://Baike.baidu.com/link?url=fgtqjo35fmy--izx0rtojrtil0crpllnxhsxguerwwueozwusl0lfzkis4qg5z_duia-nsobh3jbg2smlf8iokhttp//blog.csdn.net/chenrizhong/article/details/67199503, market layer build data that matches specific analytics business with ETL tools to build schema and cube Note: Regardless of the layer of data, data is stored in a database of ODS, data mart, data warehouse similarities and differences: ODS: Operational Data Warehouse, the earliest data warehouse model. The characteristic is that the data model adopts the source design, the database data structure of the business system, and the structure of the ODS database.        The difference is that the ODS database can provide a history of data changes, so each table in the ODS database adds a date type that represents the point in time of the data, saving the changes in daily data, which facilitates the analysis of the data. Data Warehouse: Abbreviation EDW, Enterprise Data Warehouse, now everyone is talking about this.        The difference is that each industry Edw has a common data model, the structure is streamlined, the extensibility is strong, the application is strong, the data model is not like the ODS is very much redundant. Data mart: referred to as DM, as a starting point for an application of the local DW, why so, DM only care about the data they need. does not consider overall enterprise data architecture and application, each application has its own DM.            So DM can be based on warehouse construction can also be independent construction. Second, it is on-line analytical processing and data mining, which translates data into information and knowledge. On-line Analytical processing is based on the Data warehouse, the business problems are modeled and the data is multidimensional analysis. and data mining through the analysis of each data, from a large number of data to find the law of the technology. Even with techniques such as neural networks and rule induction, data-based inference is used to discover the connection between data.            Through on-line analytical processing and data mining, decision makers and senior management can accurately control the business status of an enterprise from a multidimensional perspective and understand the relationships between different data in order to make the right decisions.        You can build models and data that conform to our business logic by developing two of Saiku multidimensional analysis tools.            Develop the analytical tools we need. Finally, it is the demonstration of knowledge conclusion, which realizes the change of knowledge to wisdom. Through the use of information system, in a simple, rich and intuitive form, the query report, statistical analysis, multidimensional online analysis and data exploration of the conclusion of enterprise managers and decision-makers before.            With the constant accumulation and renewal of knowledge, the knowledge will be transformed into the wisdom of enterprise managers. Visualize your conclusions with the help of analysis tools, such as front-end displays, tables, or icons. 

Iii. examples

Taking the BI project of Nennoia boat as an example, it has established the overall financial index database, the service cloud of financial data, the establishment of the whole service system of the financial operation, and also set up the display of different scenes through the precise positioning of the service-oriented provincial company leaders, managers at all levels and professional analysts of the finance department. It embodies different management themes through the construction of structured chart of financial value Data system. The whole project was divided into ten thematic analysis systems, with specific indicators and thematic analysis, real-time performance control and profit monitoring analysis for each topic. Nennoia-boat BI builds a document-gathering information management platform that ultimately enables financial staff to spend their time and effort on finding problems through data and promoting internal environmental improvements.

Iv. the relationship between business intelligence and data mining

Business intelligence refers to the use of data warehousing, data mining technology to the customer's structured and unstructured data storage and management, and through a variety of data statistical analysis tools to analyze customer data, provide a variety of analysis reports, such as customer value evaluation, customer satisfaction evaluation, service quality evaluation, marketing effectiveness evaluation, Future market demand and so on, for the enterprise's various business activities to provide decision-making information. It does not only the aggregation of data, but also the integration of Information islands, multidimensional Analysis, data mining prediction and so on. Data mining is a technical concept, and business intelligence is a broad application concept for the comprehensive utilization of data in the business field. In the narrow sense, business intelligence is the application of data mining technology in business field.

Five, Enterprise Data Warehouse model

About BI (Business intelligence)