Author | Internet Big Data
Source | raincent_com
Urban big data refers to the data generated or obtained during the operation of the city, and is related to the collection of information It is an organic system composed of activity elements related to processing, utilization and communication capabilities. It is an important strategic resource for national economic and social development. The simple and easy-to-understand formula can be expressed as: urban big data = urban data + big data technology + urban functions.
The data sources of urban big data are rich and diverse, widely existing in various fields and departments of the economy and society, and are the sum of various types of data such as government affairs, industries, and enterprises. At the same time, urban big data has significant heterogeneous characteristics, with rich data types, large quantities, rapid growth, high processing speed and real-time requirements, and the characteristics of cross-department and cross-industry flow.
According to different data sources and data ownership, urban big data can be divided into government big data, industrial big data and social welfare big data. Government big data refers to various information resources such as documents, materials, charts and data that are produced or obtained by government departments in the course 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, service industry data, etc.
In addition, there are some social welfare big data. Currently, most urban big data is government affairs 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 safety and efficiency of urban operation, smart city construction requires the collection, integration, storage and analysis of massive data resources, and the use of intelligent sensing, distributed storage, data mining, real-time dynamic visualization and other major technologies. Data technology enables rational allocation of resources. Therefore, urban big data is a key support for realizing urban intelligence and an important engine for promoting "government connectivity, benefiting the people, and developing industry." The development of new smart cities faces challenges
The development of new data-driven smart cities faces many problems. The white paper believes that although local governments and enterprises at all levels are currently actively exploring the construction of smart cities, there are still problems such as unclear features, poor experience, and insufficient enjoyment. The root cause lies in the failure to achieve good integration of urban big data resources and urban business.
Specifically, the challenges include three aspects: first, there are many chimneys in the information system, hindering the sharing of data; second, data governance is generally weak, and the value is greatly reduced; third, data management levels are different. Lack of 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 platforms can play a positive role, specifically in three aspects.
1. Accelerate the integration and application of information resources through data collection
First, the urban big data platform establishes unified standards for data governance and improves data management efficiency. Through unified standards, problems such as data confusion and conflicts, one data from multiple sources, etc. can be avoided. Through centralized processing, the "validity period" of data is extended, and multi-angle data attributes are quickly mined for analysis and application.
Through quality management, problems such as uneven data quality, data redundancy, and missing data values ??can be discovered and resolved in a timely manner. Second, the urban big data platform standardizes the sharing and circulation of data among various business systems and promotes the full release of data value. Through overall management, we can eliminate the "privatization" of information resources within various departments and mutual constraints between departments, enhance the awareness of data sharing, and improve the motivation for data openness. Improve the utilization level of data resources through effective integration.
2. Improve the level of government public services through accurate analysis
In the transportation field, through real-time traffic monitoring such as satellite analysis and open cloud platforms, we can sense traffic conditions and help citizens optimize Travel plans; in the field of safe cities, through centralized monitoring and analysis of behavioral trajectories, social relations, public opinion, etc., it provides strong support for the public security department’s command and decision-making and intelligence research and judgment.
In the field of government services, relying on a unified Internet e-government data service platform can achieve "more data and less errands for the masses"; in the field of medical and health, through data exchange such as health files and electronic medical records, both Improving the quality of medical services can also promptly monitor the epidemic and reduce citizens’ medical risks.
3. Promote the development of urban digital economy through data openness
An open and shared big data platform will promote the two-way connection 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 commercial value, and improve their business levels.
On the other hand, the data contributed by enterprises and organizations to a unified big data platform can "feed back" government data, support refined urban management, and further promote modern urban governance. Promote platform construction in six aspects
The white paper believes that the current construction of urban big data platforms in my country is still in its infancy, and each region has its own advantages and disadvantages in management mechanisms, business structures, and technical capabilities, which is not conducive to cities. The long-term development of big data platforms. Regarding the specific path for building an urban big data platform, the white paper puts forward six suggestions.
1. Strengthen the top-level design of the platform
Scientific and reasonable top-level design is the key to the construction of urban big data platforms. It needs to be based on the implementation of national macro policies, combined with actual local needs, and overall consideration of the platform In terms of goals, data sovereignty, key technologies, legal environment, implementation functions, etc., the top-level design of the platform is carried out with a "high starting point, high positioning, and stable implementation" to ensure that the construction of the urban big data platform has goals, directions, paths, and Continue to advance in a rhythmic manner, and continue to iteratively update and introduce new ones according to the progress of the project.
2. Improve platform supporting guarantee mechanisms
The construction and operation of urban big data platforms must have corresponding supporting guarantee mechanisms, and give full play to the guiding and supporting functions of the guarantee mechanisms to ensure The coordination of platform planning and construction and the realization of overall platform effectiveness.
For example, establish a city big data resource management mechanism, clarify the centralized management department of data content, data collection unit and public sharing and open mode; establish a city big data platform operation management mechanism, clarify the data in the use of the platform , processes, security and other contents and management standards to ensure the continued and stable operation of the platform.
3. Strengthen data management
Strengthen urban big data management and realize standardized management of the entire process from data collection to data assetization. Clarify data ownership and distribution of interests, as well as personal information protection and management responsibilities throughout the data life cycle. Clarify the classification and hierarchical management of data resources and improve data resource management standards.
Classification refers to accurately describing the types of basic government data through multi-dimensional data characteristics; grading refers to determining the sensitivity of various types of data, formulating corresponding strategies for the opening and sharing of different types of data, and improving data Standards for collection, management, exchange, architecture, evaluation and certification, etc., to promote the introduction of basic norms and standards for data sharing and openness.
With the three standard steps of resource catalog compilation, resource integration and collection, and exchange and sharing platform, we adhere to "one number, one source" and multiple verifications, and coordinate the construction of a government information resource catalog system and database. *Enjoy the exchange system. Establish a scientific and reasonable data classification system to integrate data in different fields and multiple formats, and facilitate users to find and utilize data content through multiple search methods, analysis tools and applications.
4. Carry out platform construction and operation according to local conditions
The construction and application of urban big data platforms must be combined to avoid the phenomenon of focusing on platform construction and neglecting platform use. The data resources of governments, industries and cities are extremely complex. It is necessary to clarify the ownership attributes of platform data resources and ensure the ownership of data.
The government owns the ownership of government data resources. Internet companies often have advanced data technology and professional teams with Internet thinking. Local companies have a clearer understanding of local human resources, market environment, industrial development and other factors. For a more accurate understanding, it is necessary to fully mobilize the resources of the government, Internet companies, local companies and other parties to participate in the construction and operation of the platform.
The data governance and operation system of the urban big data platform is quite complex. There is no fixed model and path for platform construction. It is necessary to give full play to the subjective initiative of all parties, adapt to local conditions, tap local advantages, highlight local characteristics, and provide cities with Big data provides strong support for decision-making.
5. Carry out comprehensive evaluation of urban big data
The big data authorities in each province and city should formulate long-term operation mechanisms and evaluation methods for the platform, establish a complete reporting, inspection, and evaluation mechanism, and design Quantitative assessment content and standards, strengthen platform data quality control, and properly manage and use urban big data platforms.
Strengthen the post-evaluation and project inspection of urban big data platform projects, and strengthen the audit supervision of data resource construction, data sharing and openness, data quality and security. Scientifically construct 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 regions, and continuously improve the effectiveness of urban big data platform construction and application.
6. Strengthen platform data security
The urban big data platform contains a large amount of government and industrial data, involving national interests, public security, business secrets, and personal privacy, and has a high degree of sensitivity, so it is necessary to strengthen the platform's data security capabilities.
Implement basic systems such as level protection, security assessment, electronic certification, and emergency management, establish a security assessment mechanism for data collection, transmission, storage, use, opening, etc., and clarify the scope and subject of data security protection. , responsibilities and measures. Research and formulate data rights guidelines, data benefit distribution mechanisms, and data circulation and transaction rules, clarify the data responsible parties, and increase the protection of technology patents, digital copyrights, digital content products, and personal privacy.