Original address: https://www.sohu.com/a/238844423_99983415
Abstract: [Purpose/Meaning] Find out the internal logic of my country's promotion of the development and application of government big data from the issued policy documents, and provide countermeasures and suggestions for optimizing future policy paths. [Methods/Process] Collect 189 effective policy texts through government portals, comprehensively use word frequency analysis software and manual methods to extract keywords, and use CiteSpace 5.0 to analyze the time distribution of policy texts, keyword co-occurrence networks, high-frequency keywords and their co-occurrence Present relationship. [Results/Conclusions] The process of promoting the development and application of government big data in my country can be divided into three stages: local preliminary exploration, top-level design, and comprehensive exploration; hot areas include government services and streamlining administration, social governance and public services, innovation and entrepreneurship, and industry Development; the value orientation is guided by innovation and development, with data convergence as the main line, and security as the premise. In the future development, we should create more public value for specific scenarios; give full play to the technical advantages of big data, deeply explore hidden patterns; attach importance to property ownership and privacy protection, and build a good application environment.
0 Preface
Government big data has become a new driving force and a new way to improve the government's governance capabilities and reconstruct the public service system. The "Outline of Action for Promoting the Development of Big Data" released in 2015 proposed the establishment of a management mechanism of "using data to speak, using data to make decisions, using data to manage, and using data to innovate"; the "13th Five-Year Plan for National Informatization" released in 2016 It is pointed out that "accelerate the sharing and sharing of cross-departmental and cross-level data resources"; the "New Generation Artificial Intelligence Development Plan" released in 2017 further requires the strengthening of the integration of government data resources and the development of artificial intelligence platforms suitable for government services and decision-making. Related industry departments and local governments have also issued a series of policy documents to promote the development and application of government big data, encouraging corresponding practical exploration.
Regarding the development and application of big data in government affairs, scholars have put forward a series of important points based on solid academic analysis, explaining possible paths, major challenges and advancing strategies; for the issued policy documents, they have carried out detailed and accurate policy interpretations. , Policy text analysis and research, and comparative research with related policies of other countries; in-depth analysis and summary of advanced experience in the development and application of government big data in some regions and specific fields. Unfortunately, there is no research based on the policy documents that have been issued to reveal the internal logic of my country's promotion of the development and application of big data in government affairs, and the promotion logic reflected in the policy documents has not been compared with the results of theoretical analysis and case analysis. Existing shortcomings. Therefore, this article proposes and attempts to answer the following three questions: (1) What is the internal logic of my country's promotion of the development and application of government big data in the policy documents that have been issued? (2) The use of theoretical analysis results and case analysis findings, through comprehensive What are the shortcomings that can be found by examining the above-mentioned promotion logic? (3) In view of the future development path of government big data, what aspects should be optimized?
1. Literature review
1.1 Application path research
As the owner of public data, the government should take the lead in developing and leading big data applications. The application paths of government big data mainly include: first, obtaining more comprehensive data from more channels and gathering more comprehensive data to assist the government in scientific decision-making; second, linking and comparing different data and information of the same object to achieve precise governance and services; third, using big data Data analysis technology generates scientific, accurate, and intuitive forecasting solutions; fourth is to connect many participants to condense and stimulate group wisdom; fifth is to create a transparent, open, fair and just governance environment; sixth is to expand and deepen the data value chain and create More public value. To promote the development and application of big data in government affairs, there are many challenges in institutional mechanisms, policies and regulations, technology, systems, data, personnel, etc.; countermeasures and suggestions include deepening system reform, improving laws and regulations, doing a good job in top-level design, improving policy implementation, and making breakthroughs Key technologies, promoting industrial development, strengthening public participation, ensuring data security, etc.
1.2 Policy analysis and research
It mainly includes three aspects: policy interpretation, policy text analysis, and policy comparison. Policy interpretation studies mainly focus on policy documents issued at the national level, systematically explain the importance of big data applications in improving government governance capabilities and possible challenges in policy implementation, and propose countermeasures for implementation. There are still relatively few policy text analysis studies, and only one article was retrieved: Li Yue and others conducted a qualitative study on the big data strategy text released by the local government, and summarized the time and regional characteristics of the strategy release. Zhang Yongjin and Wang Jingxuan compared and analyzed the big data policies of major developed countries from the dimensions of strategic planning, technological capability improvement, application and management; Yu Shiyang et al., based on the review of the application of domestic and foreign government affairs big data, pointed out that the development of government affairs big data should adhere to the overall There are three basic directions: sexual government, transparent government and service-oriented government.
1.3 Typical case study
Starting from two aspects of region and field, the former focuses on the development and application of government big data in a certain area, while the latter focuses on specific application fields. Typical regional cases come from Beijing, Shanghai, Shenzhen, Wuhan, Guizhou and other provinces and cities. Chen Zhichang took the Dongcheng District of Beijing as an example to propose a big data application framework for modernization of government governance; Shenzhen Pingshan New District Development Research Center analyzed the district’s Data governance” model; Wang Liujun and Xiao Yingshuang systematically analyzed the status of big data construction in Wuhan. Typical field cases come from social stability risk assessment, intelligent provision of public services, big data-driven precision poverty alleviation and tourism forecasting, etc., analyzing the coupling between big data applications and business scenarios, and big data-driven business process reconstruction.
It can be seen that the existing research on the application path of government big data is relatively systematic, and the analysis of policies and cases is also relatively in-depth, but there is still room for in-depth. For example, there is no systematic analysis of the “government big data policy”, but the government’s big data is focused on analyzing the “big data policy” issued by the government. As the core area of big data application, government big data has received a lot of attention, and the related policy documents have reached a certain number, from which the promotion logic can be found; at the same time, it is also necessary to reflect the promotion logic and theory in the policy documents The research finds comparative analysis, in-depth consideration of the shortcomings in the promotion process, and corresponding optimization suggestions for future development.
2. Research and design
2.1 Policy text collection
The data sources are the policy texts related to "big data" in the Chinese government website and the government portals of 31 provinces (autonomous regions and municipalities) except Hong Kong, Macau and Taiwan. The specific collection method is as follows: Search for all policy texts containing the word "big data" in the "topic" or "content" in the "Policy Documents" section of the website; the search will be completed from September 30 to October 2, 2017 ; Then, the policy texts that are not directly related to the topic of “government big data”, such as the specific industrial arrangements for big data and the list of big data team members are eliminated, and 189 effective policy texts are finally obtained.
2.2 Text analysis method
Use Cite Space 5.0 software to draw the corresponding knowledge map to analyze the policy text, including three aspects: the promotion process, hot areas, and value orientation. Since Cite Space 5.0 is mainly for academic literature, not policy text. Academic literature has standardized metadata annotations; when applied to policy text analysis, metadata annotations should be done first, including fields such as title, keywords, source institution, and institution type. Fields other than keywords are marked manually.
Keyword labeling is comprehensively extracted using automatic and manual methods. For policy texts where “big data” is a high-frequency keyword in the full text, the “Tuyue” word frequency analysis software is used to conduct a preliminary word frequency analysis, and then a keyword list is obtained based on the comprehensive evaluation of word frequency and weight. For example, for the "Opinions of the General Office of Chongqing Municipal People's Government on Using Big Data to Strengthen Service and Supervision of Market Entities", first use word frequency analysis software to conduct preliminary word frequency analysis, and get the "government", "supervision", "Chongqing", "online" "report The word frequency and weight list of 150 keywords such as ""; according to the actual meaning and degree of relevance of the words, words such as "Chongqing" and "end of the month" are excluded, and finally 10 keywords such as "government" and "regulation" are obtained.
For policy texts where “big data” appears frequently in a particular paragraph, manual extraction is performed based on three indicators: the subject of the paragraph content, the actual meaning of the words, and the degree of relevance to the word “big data”. For example, in the "Administrative Measures for the Prevention and Treatment of Illegal Fund Raising in Beijing", "Big Data" is the key word in the paragraph of Article 5 "Strengthening Big Data Monitoring and Early Warning, Strengthening Industry Data Integration and Information Sharing" rather than the full text. Words, using manual reading methods to mark 6 keywords such as "illegal fundraising", "big data" and "information analysis".
When extracting keywords, follow the principle that the concept connotation is as specific as possible, such as "innovation-driven", "innovative services", and "technological innovation" as individual keywords instead of labeling "innovation" in general. 189 policy texts extract a total of 1566 keyword items in a keyword list consisting of 122 different keywords. The keywords with similar connotations are not merged, but are aggregated in the analysis of keyword co-occurrence networks. Conceptual connotation.
3. Advance the logic
3.1 Promotion process
Figure 1 shows the time distribution of the 189 central government and provincial government policy texts. Combining the content of the policy text and iconic policy documents, the process of promoting the development and application of government big data in my country can be divided into three stages:
Figure 1 Time distribution of the central government and provincial government policy texts
(1) Local preliminary exploration stage (July 2013-June 2015): starting with Chongqing's release of the Chongqing Big Data Action Plan in July 2013; Guizhou issued the Guizhou Province Big Data Industry Development and Application Planning Outline (2014 —2020)", "Opinions on Several Policies for Accelerating the Development and Application of Big Data Industry", etc., to carry out substantive explorations on the application of big data in government affairs; Zhejiang, Jiangsu, Fujian, Beijing and other places also introduced "big data" in policy documents the concept of. The core feature of this stage is that the number of policy documents is small, and most of them are issued by local governments, and there are few policy documents at the national level.
(2) Top-level design stage (July 2015-December 2016): In July 2015, the State Council issued the "Guiding Opinions on Actively Promoting the "Internet +" Action" and the General Office of the State Council issued the "On the use of big data to strengthen the Several Opinions on the Service and Supervision of Market Entities as the beginning; starting with the State Council’s “Notice on Issuing the Outline of Action for Promoting the Development of Big Data” in September 2015, and the “Guidelines on Accelerating the Promotion of “Internet + Government Services” in September 2016 Opinions are a sign; representative policy documents also include the "Thirteenth Five-Year" National Informatization Plan, the Interim Measures for the Administration of the Sharing of Governmental Information Resources, and the Implementation Plan for Promoting the "Internet + Governmental Services" Pilot Information Benefits for the People ", "Guiding Opinions on Promoting and Standardizing the Application and Development of Big Data in Health Care", etc. The core feature of this stage is that the national and local governments have issued a large number of policy documents, and most of them are top-level design. The national level has issued programmatic documents for the development and application of government big data, given a specific road map, proposed major strategic areas and key construction projects; local policies have issued corresponding plans and action plans based on the national top-level design.
(3) Comprehensive exploration stage (January 2017 -): In January 2017, the General Office of the State Council issued the "Notice on the Construction of the "Internet + Government Service" Technical System" and the "Guizhou Provincial Public Service Management Office" was renamed "Guizhou The Provincial Big Data Development Administration” is the beginning; representative policy documents also include the “Implementation Plan for the Integration and Sharing of Government Information System”, “New Generation Artificial Intelligence Development Plan”, and “Government Website Development Guidelines”. The core feature of this stage is to focus on how to make the development and application of government big data more in-depth, which can more satisfy the refined needs of the field, and the policy documents themselves are more operational. At the same time, the big data of government affairs in Shanghai, Guizhou and other places has entered a stage of rapid development, and a characteristic promotion model is being formed.
3.2 Hot areas
The keyword co-occurrence network generated by Cite Space is shown in Figure 2, which consists of 122 nodes and 322 connections. Through further analysis, it can be found that the hot areas of my country's promotion of government big data mainly include three aspects:
(1) Government services and streamlining administration and delegating power. "Internet + government services" and "simplification of administration and delegation of power" form two very obvious sub-networks. The "Internet + government service" sub-network contains keywords including cross-level, cross-department, government information resources, collaborative sharing, big data resource system, innovative services, active services, personalized services, etc. The innovation of government services based on big data is mainly to promote cross-level and cross-departmental coordination of government services by strengthening the sharing of government information resources and the integration of government information systems; through the innovation of government service concepts and models, changing passive services into active services and changing the government’s “end” The "dishes" supply service is a personalized service of "order dishes" for the masses. The key words contained in the "simplification of administration and delegation of power" sub-network include decentralization, platform, function, intelligent supervision, government services, market entities, market exit mechanism, etc. The reform of “delegating management and service” based on big data promotes the intelligentization of market supervision and government services by building a big data platform, which in turn promotes the transformation of government functions.
Figure 2 Keyword co-occurrence network of policy text
(2) Social governance and public services. Key words related to social governance are mainly distributed in the left position in the middle of Figure 2, including social credit system, monitoring, products, public resources, government governance, big data platform, etc. Social governance innovation based on big data places great emphasis on the construction of a social credit system, by promoting the sharing of credit information and the development of credit information systems, gathering and developing basic credit information; by building a big data platform to achieve dynamic monitoring of public resources, market operations, product quality, etc. And rapid analysis to continuously improve the government’s social governance capabilities. Key words related to public services are distributed in the middle right of Figure 2, including resources, industries, fields, health, sanitation, medical care, norms, cloud computing, Internet, e-government, etc. Public service innovation based on big data centers on the optimization of public resource allocation, and promotes the optimal allocation of public service resources by promoting the flow of data resources, and realizes the full and balanced development of public services.
(3) Innovation and entrepreneurship and industrial transformation. Related keywords are distributed at the top of Figure 2, including industrial transformation, technological innovation, innovation platform, "Internet + industry", tourism, transportation, innovation drive, pilot zone, public economy, innovation, etc. Promoting industrial development and economic transformation based on government big data places great emphasis on the concept of "innovative development", and promotes economic transformation and upgrading through technological innovation, industrial innovation, and institutional innovation. In this process, the government should build an innovation platform to help industry development.
3.3 Value orientation
The high-frequency keywords in the policy text can reflect the value orientation of the policy to a greater extent. See Table 1 for the centrality of the 30 keywords with the highest frequency in all policy texts and their corresponding nodes. The frequency of occurrence refers to how many policy texts the corresponding keyword appears in, reflecting the popularity of the keyword; the point degree centrality refers to how many other nodes the node corresponding to the keyword is connected to in the keyword co-occurrence network , Reflect the degree of relevance of this keyword to other keywords. Further analysis can find that my country has three obvious value orientations in promoting the development and application of government big data:
Table 1 High-frequency keywords in the policy text and the point degree centrality of their corresponding nodes
(1) Leading by innovation and development. Reflected in 12 keywords, including "innovation", "innovative service", "active service", "personalized service", "intelligent supervision", "precision", "technological innovation", and "innovation driven". The keyword "innovation" has the highest frequency (up to 37), and the point center degree of the corresponding node is also as high as 15. Promoting economic transformation and upgrading, government governance, and public service innovation are the fundamental goals of the development and application of government big data; at the same time, it pays great attention to concepts, systems, mechanisms, technologies, platforms, processes, and models in the process of promoting the development and application of government big data Innovation.
(2) Take data aggregation as the main line. Reflected in 17 keywords, including "sharing", "opening", "collaboration", "cross-department", "cross-level", "cross-regional", "information sharing", "exchange sharing", and "intensive construction". This category has the largest number of keywords, occupies 6 of the top 10 high-frequency keywords, and the point centrality of the corresponding nodes of the 6 keywords is also very high. Attempts to promote cross-level, cross-departmental, and cross-regional sharing of government information resources through the integration of government information systems, intensive construction and overall management, so as to achieve the aggregation of data from different sources and form sufficiently "large" data resources; at the same time, it is also trying to promote The aggregated data resources are open and shared to the society, and their public value can be brought into full play to a greater extent.
(3) Take safety guarantee as the premise. Reflected in the keyword "security". Although there is only this keyword, its frequency of occurrence is very high (ranked 3rd, with a frequency of 29), and the point center degree of the corresponding node is the highest. The establishment of the Central Cyber Security and Informatization Leading Group on February 27, 2014 and the promulgation of the Cyber Security Law on November 7, 2016 marked that my country's informatization work has entered a new stage, and the development and application of government big data must be guaranteed by security As the premise.
3.4 Further analysis of the three-stage advancement logic
According to the three value orientation categories, the word frequency distribution of 30 high-frequency keywords is further refined into three stages of advancement, as shown in Table 2. The following two findings can be obtained through analysis:
(1) Three value orientations were formed in the top-level design stage. The local preliminary exploration stage only involves a high-frequency keyword "innovation", mainly because the number of policy texts at this stage is relatively small, and there is no more concentrated theme; at the same time, it reflects that the paths of exploration in different regions are also different. At the top-level design stage, a large number of high-frequency keywords appeared concentratedly, and reflected all the value orientations, which is the formation stage of the three value orientations. The focus of the value orientation is not only due to the successive release of corresponding top-level design documents at the national level, but also due to the fact that some provinces and cities have quickly followed up with the national strategy and issued corresponding policy documents.
(2) The comprehensive exploration stage further deepens three value orientations. After entering the comprehensive exploration stage, on the one hand, some high-frequency keywords and their appearance frequencies are more consistent with those in the top-level design stage, such as "intelligent supervision", "inter-departmental", "cross-level", etc., mainly due to more local governments And industry departments have issued policy documents on corresponding topics based on national strategies; on the other hand, some new high-frequency keywords with more directional connotations appear or the frequency of existing high-frequency keywords has increased, such as "active service" and "personalization" "Service", "precision", "intensive construction", etc., indicate that some regions or departments have begun to explore more targeted promotion strategies. It can also be seen that local innovative exploration is helpful to the real implementation of top-level design.
Table 2 The distribution of high-frequency keywords in the three stages
4. The way forward
The fundamental purpose of the development and application of big data in government affairs is to create public value based on meaningful patterns derived from data analysis. Both the big data value chain and ecological theory point out that the value creation of big data is realized through a series of activities, including data acquisition, data storage, data analysis, data application and other stages; the activities of each stage will affect the degree of big data value creation At the same time, the realization of the value created by big data needs to build a good external environment to promote the support and active participation of stakeholders. Based on relevant theories and case analysis, this paper proposes a value chain model for the development and application of my country's government big data, as shown in Figure 3. Comparing and analyzing the current promotion logic, we found that there are the following problems: First, it emphasizes innovation and development, but pays less attention to the possibility and degree of value creation of government big data; second, it pays great attention to the aggregation of data, but does not pay enough attention to the analysis and application of data. In particular, in-depth analysis techniques are rarely used; third, great attention is paid to the issue of network security, but the efforts to promote the definition of data ownership and the protection of personal privacy are insufficient and progress is slow. For future development, this article makes the following suggestions:
Figure 3 The value chain model of my country's government big data development and application
First, create more public value for specific scenarios. The value creation of big data has a high degree of domain dependence. Only after the data and specific application scenarios are truly combined can "unimaginable" value be created. The forms of big data value creation include: first, value generation, that is, directly generating public value through big data analysis, such as the establishment of a poverty alleviation fund allocation strategy based on accurate analysis; second, value enhancement, that is, improving public value through big data analysis, such as based on The flow of medical data promotes the flow of high-quality medical resources to the grassroots community, and enhances the level of public value creation of high-quality medical resources; the third is value conversion, that is, through intelligent supervision to encourage relevant entities to play public value, for example, based on the construction of a social credit system, guide enterprises to become more Pay attention to environmental protection issues; the fourth is value diffusion, that is, through institutional innovation, promote the widespread adoption of new platforms based on big data analysis by relevant entities, such as the "Internet + government service" construction that is being fully promoted. In policy formulation and improvement, it is necessary to emphasize and promote the creation of public value on the basis of focusing on innovation and development; organize and research field-oriented big data application performance evaluation programs as soon as possible, and adjust and optimize the construction content of government big data projects based on the results of performance evaluation And capital investment.
Second, give full play to the technical advantages of big data and dig deep into hidden patterns. Converging data is only the basic work for the development and application of government big data, and in-depth big data analysis can truly realize the creation of public value of government big data. Relying on our institutional advantages, we have taken solid steps to share government information resources across departments, departments, regions, and systems, and made important progress in some departments and places. However, from the perspective of existing practical explorations, the current data analysis still mainly stays on shallow analysis, mainly to play the role of data consistency and timeliness. The analysis techniques used mainly include correlation comparison, discriminant analysis, and spatial distribution analysis. , Time series analysis, etc.; less use of in-depth analysis techniques, such as social computing, knowledge computing, machine learning, group interactive decision-making, etc. In terms of data aggregation, the main focus is on internal government data and external data opening, but the collection and use of external government data is not enough. At present, a large amount of user data is also gathered on commercial platforms, and these data are of great value to improving government affairs. Therefore, policy formulation should encourage the application of in-depth analysis techniques; build a two-way flow mechanism for internal and external data within the government to give full play to the public value of various types of big data.
Third, attach importance to property ownership and privacy protection, and build a good application environment. A good government big data application environment is premised on network security, but it also requires clear ownership and firm privacy protection as a guarantee. Unclear definition of the property rights of data resources is already one of the main obstacles hindering the development and application of big data in government affairs. When the government departments exchange, share, and open the data they own, they are always "succumbing to gains and losses" and "looking forward to the future", which seriously affects the degree and efficiency of the flow of government big data; some departments are even worried about the trouble they may encounter after the data is released, such as data Consistency and authenticity issues. On the other hand, the endless personal information leakage incidents have also caused a serious negative impact on the development and application of government big data. It should be recognized that "security" and "privacy" are closely related, but they are not the same. Security is the prerequisite for privacy protection, but the protection of security does not mean that privacy is effectively protected; a lot of solid work is needed to protect privacy. The ownership of property rights and privacy protection issues cannot be dragged on forever, but should be acted as soon as possible; at the same time, these issues must ultimately be resolved under the legal framework. Therefore, we should summarize and refine the successful experience of departments and localities as soon as possible, and use them in the top-level design at the national level.
5 Conclusion
The development and application of big data in government affairs has been elevated to a national strategy, and full-scale advancement can effectively promote the modernization of national governance. Based on the analysis of 189 policy texts issued by the national and local governments, this paper reveals the internal logic of my country's promotion of the development and application of big data in government affairs; combined with the big data value chain, ecological theory, and the results of case analysis, it has established the country's government affairs department. The value chain model of data development and application points out the current shortcomings and the future development direction, which has a certain driving effect on theoretical research and practical exploration. In future research, relevant policy texts will be collected for specific application fields, and in-depth analysis of the development and application paths of government big data in corresponding fields.
WeChat public account: e-government think tank
References: Slightly
Source: "Information Magazine"
Author: Zhang Huiping Guo Xi Ning Tang Kai
Disclaimer: This article is transferred from online public channels and aims to provide users with the latest and most complete information. It does not provide any express or implied guarantees for the accuracy, reliability or completeness of the contained content. The copyright of the reprinted manuscript belongs to the original author or institution. If there is any infringement, please contact to delete it. All disputes caused by copying this article to other channels have nothing to do with this platform