Ma Yun has repeatedly said in his speech that the future is the era of DT (data technology). With the rapid development of big data in recent years, both enterprises and government agencies are aware of the value and business changes brought by data.



For government agencies, the application of big data has natural advantages and urgency. Ma Jiantang, director of the National Bureau of statistics, once said: "big data will become the information foundation of macro-control, national governance and social management." The government itself has a large number of data resources, and can initiate the mobilization of big data resources in other fields, so mining the value behind these resources. Yonghong technology has advanced data technology support, perfect service system and rich experience accumulated for many years to provide effective big data solutions for the government industry, so as to promote China's economic development, strengthen and improve social governance, and improve the government's service and regulatory capacity.



Industry status



In order to adapt to this trend, our government proposes to carry out government reform, build e-government, and achieve the goal of government informatization. In recent years, the construction of government informatization has achieved remarkable results. It has played an important role in promoting the transformation of government functions and strengthening the services to enterprises, institutions and the public. With the gradual launch of E-government in government departments, compared with the past, the development of government business is more efficient, communication is more convenient, and public satisfaction has been greatly improved. But at the same time of improving the efficiency of government work, massive and fragmented business data makes the "information island" dilemma more and more obvious.



It has become the general trend to improve the governance capacity of the government with big data. We should use big data to improve the modernization level of national governance, promote e-government, build a smart city, and take data concentration and sharing as the way to promote technology integration, business integration and data integration, break through information barriers, and form a data sharing platform covering the whole country, making overall use of and unified access. To build a national information resource sharing system and realize cross level, cross region, cross system, cross department and cross business collaborative management and service.



Current problems



How much do you know about the digital development of smart government?



Government data is "supported" but difficult to "use", government platform is "unified" but not "connected", resource data is "collected" but difficult to "wisdom", and system mechanism is "revitalized" but difficult to "new".



1. The development of mass data E-government has penetrated into the fields of government social management, public service, market supervision, macro-control and so on. Each government system has produced mass data and the data growth rate is faster and faster, which has greatly reduced the efficiency of data query, report generation and the accuracy of business decision-making.



2. With the gradual improvement of government information, government management, convenient service, emergency safety and other systems have gradually begun to play a practical role. However, the information systems of each department are basically based on units, coexist in the form of separate systems, and lack of a unified platform to associate and integrate data, which makes it difficult to effectively integrate all aspects of business information into data applications to show a panorama of business, and to fully release the true value of data.



3. When making regulatory decision support management decisions, we can't see the problems from the perspective of integration. Users of different departments and levels have totally different needs for business data analysis. At present, the reports that can be provided are mainly based on tables, with single analysis dimension and simple and solid form. We can't show the decision basis and decision-making rules on the data analysis platform in a centralized way and divide them. The effectiveness of demand response is poor, which can not provide effective support for users at all levels to make decisions.