Source | raincent_com
Urban big data refers to the data generated or obtained in the process of urban operation. It is an organic system composed of activity elements related to the ability of information collection, processing, utilization and communication. It is an important strategic resource for national economic and social development. The easy-to-understand formula can be expressed as: urban big data = urban data+big data technology+urban functions.
The data resources of urban big data are rich and diverse, and widely exist in various fields and departments of economy and society, which is the sum of all kinds of data such as government affairs, industries and enterprises. At the same time, the heterogeneity of urban big data is remarkable, with rich data types, huge quantity, rapid growth, fast processing speed and high real-time requirements, and it has the characteristics of cross-departmental and cross-industry flow.
According to different data sources and data attribution, urban big data can be divided into government big data, industrial big data and social welfare big data. Government big data refers to all kinds of information resources such as documents, materials, charts, data, etc., which are generated or obtained by government departments in the process of performing their duties, and recorded and saved in a certain form. Industrial big data refers to relevant data generated in economic development, including industrial data and service industry data.
There are also some social welfare big data. At present, most of urban big data are government big data and industrial big data, so the main promoters of urban big data should be a city's government and related enterprises with a certain data scale.
In order to ensure the safe and efficient operation of cities, the construction of smart cities needs to collect, integrate, store and analyze massive data resources, and use big data technologies such as intelligent perception, distributed storage, data mining and real-time dynamic visualization to realize the rational allocation of resources. Therefore, urban big data is the key support to realize urban intelligence and an important engine to promote "political communication, benefiting the people and developing the industry".
The development of new smart cities faces challenges. The development of new smart cities driven by data faces many problems. The white paper points out that although local governments and enterprises at all levels are actively exploring the construction of smart cities, there are still problems such as unclear characteristics, poor experience and insufficient enjoyment of * * *. The fundamental reason lies in the failure to achieve a good integration of urban big data resources and urban business.
Specifically, the challenge includes three aspects: first, there are many chimneys in the information system, which hinders the enjoyment of data; Second, data governance is generally weak and its value is greatly reduced; Third, the level of data management is different, lacking overall linkage.
How to deal with the difficulties and challenges in the construction of new smart cities? The white paper believes that the construction of urban big data platform can play an active role, which is embodied in three aspects.
First, speed up the integration and application of information resources through data collection.
First, the urban big data platform has established a unified standard for data governance and improved the efficiency of data management. By unifying standards, problems such as data confusion and conflict, one number and multiple sources can be avoided. Through centralized processing, the "validity period" of data can be extended, and multi-angle data attributes can be quickly mined for analysis and application.
Through quality management, problems such as uneven data quality, data redundancy and missing data value can be found and solved in time. Second, the urban big data platform standardizes the * * * sharing and circulation of data between business systems, and promotes the full release of data value. Through overall management, we can eliminate the "privatization" of information resources in various departments and the mutual restriction between departments, enhance the awareness of data sharing and improve the motivation of data opening. Improve the utilization level of data resources through effective integration.
Second, improve the government's public service level through accurate analysis.
In the field of transportation, through real-time traffic monitoring such as satellite analysis and open cloud platform, we can perceive traffic conditions and help citizens optimize their travel plans; In the field of safe cities, through the centralized monitoring and analysis of behavior trajectory, social relations and public opinion, it provides strong support for the command and decision-making and intelligence judgment of public security departments.
In the field of government services, relying on a unified Internet e-government data service platform, we can achieve "more data to walk, less people to run errands"; In the medical and health field, data exchange between health records and electronic medical records can not only improve the quality of medical services, but also monitor the epidemic situation in time and reduce the medical risks of citizens.
Third, promote the development of urban digital economy through data opening.
Opening the big data platform will promote the two-way docking of government and enterprise data and stimulate social forces to participate in urban construction. On the one hand, enterprises can obtain more urban data, tap business value and improve their business level.
On the other hand, the data of enterprises and organizations help to establish a unified big data platform, which can "feed back" government data, support the refined management of cities, and further promote modern urban governance.
The White Paper on Promoting Platform Construction in Six Aspects holds that the construction of urban big data platforms in China is still in its infancy, and each place has its own advantages and disadvantages in terms of management mechanism, business structure and technical capabilities, which is not conducive to the long-term development of urban big data platforms. The white paper puts forward six suggestions on the specific path of building an urban big data platform.
The first is to strengthen the top-level design of the platform.
Scientific and reasonable top-level design is the key to the construction of urban big data platform. Starting from the implementation of national macro policies, combined with local actual needs, we should make overall consideration of platform objectives, data sovereignty, key technologies, legal environment, realization functions and other aspects, and carry out the top-level platform design of "high starting point, high positioning, and steady landing" to ensure that the construction of urban big data platform is continuously promoted with goals, directions, paths and rhythms. According to the progress of the project,
The second is to improve the platform supporting guarantee mechanism.
The construction and operation of urban big data platform must have corresponding support mechanism, and give full play to the guiding and supporting role of the support mechanism to ensure the coordination of platform planning and construction and the realization of overall benefits of the platform.
For example, establish a management mechanism for urban big data resources, and clarify the centralized management department, data collection unit and * * * open mode of data content; Establish the operation and management mechanism of urban big data platform, clarify the data, processes, security and other contents and management standards in the use of the platform, and ensure the sustained and stable operation of the platform.
Third, strengthen data management.
Strengthen the management of urban big data and realize the standardized management of the whole process from data collection to data capitalization. Clear data ownership and benefit distribution, as well as personal information protection and management responsibilities in the data life cycle. Clarify the classification and hierarchical management of data resources and improve the management standards of data resources.
Classification refers to the accurate description of government basic data types through multidimensional data characteristics; Classification refers to determining the sensitivity of all kinds of data, formulating corresponding strategies for the opening and * * * enjoyment of different types of data, improving standards for data collection, management, exchange, architecture, evaluation and authentication, and promoting the publication of basic norms and standards for data opening and * * * *.
Taking the compilation of resource catalogue, resource integration and convergence, and * * * sharing platform exchange as three standard steps, adhering to the principle of "one source for one number" and multi-checks, we will make overall plans to build a catalogue system of government information resources and a * * * sharing exchange system. Establish a scientific and reasonable data classification system, integrate data in different fields and in various formats, and facilitate users to find and use data content through various retrieval channels, analysis tools and applications.
Fourth, develop platform construction and operation according to local conditions.
The construction and application of urban big data platform should be combined to avoid the phenomenon that platform construction is more important than platform use. The data resources of government, industry and city are extremely complex, so it is necessary to clarify the right attribute of platform data resources and ensure the ownership of data.
The government has the ownership of government data resources, and Internet companies often have advanced data technology and professional teams with Internet thinking. Local enterprises have a clearer and more accurate understanding of local human resources, market environment, industrial development and other factors, and need to fully activate the resources of the government, Internet companies, local enterprises and other parties to participate in the construction and operation of the platform.
The data governance and operation system of urban big data platform is quite complex, and there is no fixed model for the mode and path of platform construction. It is necessary to give full play to the subjective initiative of all parties, tap local advantages according to local conditions, highlight local characteristics, and provide strong support for urban big data decision-making.
Verb (abbreviation of verb) carries out comprehensive evaluation of urban big data.
Departments in charge of big data in all provinces and cities should formulate long-term operation mechanism and evaluation methods of the platform, establish a sound reporting, inspection and evaluation mechanism, design quantitative evaluation contents and standards, strengthen data quality control of the platform, and manage and use the city big data platform well.
Strengthen the post-project evaluation and project inspection of urban big data platform, and strengthen the audit supervision of data resource construction, data access, data quality and safety. Scientifically build a comprehensive evaluation index system for urban big data platforms, carry out comprehensive evaluation of the effectiveness of urban big data platform construction, guide the construction of urban big data platforms in various places, and continuously improve the application effectiveness of urban big data platforms.
Sixth, strengthen platform data security.
Urban big data platform contains a large number of government and industry data, involving national interests, public safety, trade secrets and personal privacy, and is highly sensitive. Therefore, it is necessary to strengthen the capacity building of platform data security.
Implement basic systems such as level protection, security assessment, electronic authentication and emergency management, establish security assessment mechanisms such as data collection, transmission, storage, use and opening, and clarify the protection scope, subject, responsibility and measures of data security. Study and formulate data rights standards, data benefit distribution mechanism, data circulation and trading rules, clarify the subject of data responsibility, and increase the protection of technology patents, digital copyrights, digital content products and personal privacy.