What is business intelligence? What are business intelligence tools?

Business IntelligenceBi is short for business intelligence. Business Intelligence is generally understood as a tool that converts existing data in an enterprise into knowledge and helps the enterprise make informed business operation decisions. The data discussed here includes orders, inventories, transaction accounts, customers, suppliers, and other data from the enterprise's industry and competitors, as well as various data from other external environments of the enterprise. data. Business Intelligence can assist in business operation decision-making, either at the operation layer or at the tactical or strategic layer. To convert data into knowledge, data warehouse, Online Analytical Processing (OLAP) tools, data mining, and other technologies must be used. Therefore, in terms of technology, business intelligence is not a new technology, but a comprehensive application of data warehousing, OLAP, data mining, and other technologies.

Business Intelligence is defined as a collection of the following software tools:
End user query and report tools. It is designed to support access to raw data from junior users, excluding finished report generation tools suitable for professionals.
OLAP tools. It provides a multi-dimensional data management environment. Its typical application is modeling business problems and analyzing business data. OLAP is also called multidimensional analysis.
Data mining software. Uses techniques such as Neural Networks and rule induction to discover the relationships between data and make data-based inferences.
Data warehouse and data mart products. Pre-configuration software, including data conversion, management, and access, usually includes some business models, such as financial analysis models.
The concept of Online Analytical Processing (OLAP) was first proposed by E. F. codd, the father of relational databases, in 1993. He also proposed 12 principles for OLAP. The proposal of OLAP has caused a great deal of response. as a type of product, OLAP is clearly distinguished from the OLTP.

Today's data processing can be roughly divided into two categories: online transaction processing OLTP (on-line transaction processing) and Online Analytical Processing OLAP (on-line analytical processing ). OLTP is the main application of traditional relational databases, mainly for basic and daily transaction processing, such as bank transactions. OLAP is the main application of the data warehouse system. It supports complex analysis operations, focuses on decision support, and provides intuitive and easy-to-understand query results.

OLAP is a kind of software technology that enables analysts, managers, or executors to quickly, consistently, and interactively access information from multiple perspectives to gain a deeper understanding of data. OLAP is designed to meet decision-making support or specific query and report requirements in multi-dimensional environments. Its core technology is the concept of "dimension.

"Dimension" is a high-level classification from the perspective of observing the objective world. Dimensions generally contain hierarchical relationships, which are sometimes quite complex. By defining multiple important attributes of an object into multiple dimensions, you can compare data in different dimensions. Therefore, OLAP is also a collection of multidimensional data analysis tools.

OLAP basic multidimensional analysis operations include drilling (roll up and drill down), slice, dice, rotation, drill SS, and drill through.

Drilling is to change the level of the dimension and the granularity of the analysis. It includes roll up and drill down ). Roll up summarizes low-level detailed data to high-level summary data in one dimension, or reduces the dimension, it goes from summarized data to detailed data to observe or add new dimensions.
Slice and slice are the distribution of measurement data on the remaining dimension after selecting a value on some dimensions. If there are only two remaining dimensions, the slice is used; if there are three, the slice is used.
Rotation is to change the direction of the dimension, that is, to reschedule the placement of the dimension in the table (such as row-column swaps ).
OLAP has multiple implementation methods. Different data storage methods can be divided into ROLAP, molap, and holap.
ROLAP indicates the relational database-based OLAP implementation (Relational OLAP ). With relational databases as the core, multidimensional data is represented and stored in a relational structure. ROLAP divides the multidimensional structure of a multi-dimensional database into two types of tables: fact tables used to store data and dimension keywords, and dimension tables, that is, at least one table is used for each dimension to store the description information of dimension levels, member categories, and other dimensions. A dimension table is associated with a fact table by the primary keyword and the external keyword to form a "star mode ". For complex hierarchical dimensions, to avoid occupying too much storage space for redundant data, you can use multiple tables to describe this star mode extension called "Snowflake mode ".

Molap indicates the implementation of OLAP based on multi-dimensional data organization ). Taking multi-dimensional data as the core, that is, molap uses multi-dimensional arrays to store data. Multi-dimensional data will form a "cube" structure in storage, in molap, "rotation", "cut", and "slice" of "cube" are the main technologies used to generate multidimensional data reports.

Holap indicates the OLAP implementation (Hybrid OLAP) based on the hybrid data organization ). For example, the lower layer is relational and the higher layer is multi-dimensional matrix. This method provides better flexibility.

There are other ways to implement OLAP, such as providing a dedicated SQL Server and providing special support for SQL queries in some storage modes (such as star and snowflake.

OLAP is an online data access and analysis tool for specific problems. It analyzes, queries, and reports data in multiple dimensions. Dimension is a specific angle for people to observe data. For example, when considering the sales status of a product, an enterprise usually observes the sales status of the product from different perspectives of time, region, and product. The time, region, and product here are dimensions. Different combinations of these dimensions and multidimensional arrays composed of the measured indicators are the basis of OLAP analysis, which can be formally expressed as (Dimension 1, dimension 2 ,......, Dimensions N, metrics), such as (Region, time, product, sales ). Multi-dimensional analysis refers to the use of slice, dice, drill-down, and roll-up for multi-dimensional data) in order to analyze the data, users can observe the data in the database from multiple perspectives and aspects, so as to gain a deep understanding of the information contained in the data.

Mainstream business intelligence tools include Bo, Cognos, and brio. Some domestic software tool platforms such as kcom (http://www.kcomsoft.com/) are also integrated with some basic business intelligence tools.

According to the different organization methods of comprehensive data, currently common OLAP mainly includes multi-dimensional database molap and relational database-based ROLAP. Molap organizes and stores data in multiple dimensions, while ROLAP uses the existing relational database technology to simulate multi-dimensional data. In data warehouse applications, OLAP applications are generally the front-end tools of Data Warehouse applications. At the same time, OLAP tools can be used together with data mining tools and statistical analysis tools to enhance the decision analysis function.