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After the industrial revolution, the knowledge of book with letters as the carrier doubles in a decade; After 1970, the knowledge doubles in every three years; Currently, the total global information doubles in every two years; The data volume of internet in 2010 is more than the total volume of all previous years. 

Currently, people produces over PB data every day. In the industries of Internet, e-commerce, production and manufacture, transportation and logistics, finance and insurance, medical and health care, geographic information and government institutions, large amount of data are created every day. Big data has became the important feature of transition from industrial economy to knowledge economy, which has became the most significant production element and product form in the new era.

Google, Yahoo and Facebook has become the driving force of this revolution. Meanwhile, new enterprises emerge one after another. In the Business Intelligence (BI) field, once AsterData, Greenplum, Vertica stand out prominently, the traditional IT magnates EMC, IBM and HP just respectively pocket them in. Through the absorption and integration to the big data technology of new companies, the traditional IT magnates soon launch their own big data products and services. 

After the database era, with the continuous accumulation of available data, the leading enterprises in each industry start the discovery journey of data value. The Business Intelligent System at this stage generally focuses on Data Warehouse +OLAP. In a general way, the traditional Data Warehouse can store the big data, but not provides the analysis and statistical functions targeted at big data. Therefore, when developing the data application like OLAP, first the user's demands  of  analysis and statistics  should be put forward, then the result of these subjective analysis and Statistics should be predicted, finally the real-time interaction of OLAP system can be ensured. Nevertheless, The combination of Data Warehouse and OLAP has innate defects, which may be a minor change in the perspective of end-users, but might need a long response time. The overall operation and management level of enterprises continue to improve within the industry, the competitive situation is constantly reinforced, which all brings great challenges to every enterprise, the leading enterprise in particular. 

To better cope with the challenge, and keep the dominant position in the industry, higher demands fare required by the enterprise for the Business Intelligent System. Yonghong believes that the data modeling technology of direct import of detailed data transform the relation between data and application from tight coupling to loose coupling, which does not generate any change at data level for most analysis application; The Business Intelligent System based on MPP structure can directly carry out high performance analysis to the detailed data. In this way, the user can quickly develop data application, and conduct real-time analysis at once. Construct the discovery, self-service business intelligent system on demand.