大数据时代公共管理应用决策4M思维:理论思考与实践探索
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摘要
近年来,大数据在公共管理领域的应用日趋增加,但由于缺乏理论指导,其应用决策往往无一定之规。相比于技术层面创新,思维方式变革在大数据时代更为重要。本质上,公共大数据具有"广泛记录"和"有限描述"特性。为保证其利用的科学性、合理性和可靠性,在公共管理应用决策中,应建立4M思维:即通过微观探究(Microscope),发掘数据的内在价值;凭借复合利用(Mixability),提升数据的知识挖掘广度;以语义为导向(Meaning),实现数据与应用的有效结合;采用多维分析(Multidimension),保证数据应用结论的有效性。在分析的基础上,结合北京市交通治理的实践,对大数据的应用思维进一步诠释。
In recent years, more and more big data applications have arisen in the field of public management. However, due to the lack of systematic theory guidance, the decision-making process of government always faces many challenges. Comparing with the innovation of technology, the revolution of thinking mode is actually much more important. In essence, the public big data is characterized by the "extensive records" and "limited description". To ensure the scientificity, rationality, and reliability of big data applications, it is significant to establish the brand new 4 M thoughts in our work. First, with the "Microscope" thought, the value of data is able to be fully explored; second, with the "Mixability" thought, a wide range of knowledge could be discovered from data; third, with the "Meaning" thought, the data and applications could be well integrated; fourth, with the "Multidimension" thought, the correctness and effectiveness of data analysis results could be guaranteed. On the basis of all the theoretical discussions, the newly proposed 4 M thinking mode for big data applications is further explained with the exploration and practices of traffic governance in Beijing.
In recent years, more and more big data applications have arisen in the field of public management. However, due to the lack of systematic theory guidance, the decision-making process of government always faces many challenges. Comparing with the innovation of technology, the revolution of thinking mode is actually much more important. In essence, the public big data is characterized by the "extensive records" and "limited description". To ensure the scientificity, rationality, and reliability of big data applications, it is significant to establish the brand new 4 M thoughts in our work. First, with the "Microscope" thought, the value of data is able to be fully explored; second, with the "Mixability" thought, a wide range of knowledge could be discovered from data; third, with the "Meaning" thought, the data and applications could be well integrated; fourth, with the "Multidimension" thought, the correctness and effectiveness of data analysis results could be guaranteed. On the basis of all the theoretical discussions, the newly proposed 4 M thinking mode for big data applications is further explained with the exploration and practices of traffic governance in Beijing.
引文
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[32] Li A S, Soh B, Bhatti M I, et al. Data-centric view of knowledge in information systems literature review[J]. International Journal of Services, Economics and Management, 2016, 7(2-4):111-123.
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[36] Berman J J. Principles of big data:preparing, sharing, and analyzing complex information[M]. San Francisco:Morgan Kaufmann Publishers Inc., 2013.
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[39] Wu X, Zhu X, Wu G Q, et al. Data mining with big data[J]. IEEE transactions on knowledge and data engineering, 2014,26(1):97-107.
[40] Kostyuchenko, Y. V., Movchan, D., Kopachevsky, I.,&Bilous, Y. Robust algorithm of multi-source data analysis for evaluation of social vulnerability in risk assessment tasks[A]. In SAI Intelligent Systems Conference(IntelliSys)[C]. London:IEEE, 2015.
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[2] McAfee A, Brynjolfsson E. Big data:the management revolution[J]. Harvard business review, 2012, 90(10):60-68.
[3] George G, Haas M R, Pentland A. Big data and management[J]. Academy of Management Journal, 2014, 57(2):321-326.
[4]徐宗本,冯芷艳,郭迅华等.大数据驱动的管理与决策前沿课题[J].管理世界, 2014(11):158-163.
[5] Kim G H, Trimi S, Chung J H. Big-data applications in the government sector[J]. Communications of the ACM, 2014, 57(3):78-85.
[6]陈之常.应用大数据推进政府治理能力现代化——以北京市东城区为例[J].中国行政管理, 2015(2):38-42.
[7]张勇进,王璟璇.主要发达国家大数据政策比较研究[J].中国行政管理, 2014(12):113-117.
[8]米加宁,章昌平,李大宇,林涛.第四研究范式:大数据驱动的社会科学研究转型[J].学海, 2018(02):11-27.
[9] Wamba S, Akter S, Edwards A, et al. How'big data'can make big impact:Findings from a systematic review and a longitudinal case study[J]. International Journal of Production Economics, 2015, 165:234-246.
[10]李国杰,程学旗.大数据研究:未来科技及经济社会发展的重大战略领域--大数据的研究现状与科学思考[J].中国科学院院刊, 2012, 27(6):5-15.
[11] Lazer D, Kennedy R, King G, et al. The parable of Google Flu:traps in big data analysis[J]. Science, 2014, 343(6176):1203-1205.
[12] Nair B. Reaping ROI from Big Data[J]. CSI Communications, 2014, 38(3):29-30.
[13] Zuiderwijk A, Janssen M. Open data policies, their implementation and impact:A framework for comparison[J]. Government Information Quarterly, 2014, 31(1):17-29.
[14] Tang, N. Big data cleaning[A]. In Asia-Pacific Web Conference[C]. Berlin Heidelberg:Springer-Verlag, 2014.
[15] Jagadish H V, Gehrke J, Labrinidis A, et al. Big data and its technical challenges[J]. Communications of the ACM, 2014, 57(7):86-94.
[16] Provost F, Fawcett T. Data science and its relationship to big data and data-driven decision making[J]. Big Data, 2013, 1(1):51-59.
[17] Shi Q, Abdel-Aty M. Big data applications in real-time traffic operation and safety monitoring and improvement on urban expressways[J]. Transportation Research Part C:Emerging Technologies, 2015, 58:380-394.
[18] Tan W, Blake M B, Saleh I, et al. Social-network-sourced big data analytics[J]. IEEE Internet Computing, 2013, 17(5):62-69.
[19] Schintler L A, Kulkarni R. Big data for policy analysis:The good, the bad, and the ugly[J]. Review of Policy Research, 2014,31(4):343-348.
[20] Fan W, Bifet A. Mining big data:current status, and forecast to the future[J]. ACM SIGKDD Explorations Newsletter, 2013,14(2):1-5.
[21] Kantardzic M. Data mining:concepts, models, methods, and algorithms[M]. New Jersey:John Wiley&Sons, 2011.
[22]孙轩,孙涛.基于大数据的城市可视化治理:辅助决策模型与应用[J],公共管理学报, 2018, 15,(2):120-129.
[23] LaValle S, Lesser E, Shockley R, et al. Big data, analytics and the path from insights to value[J]. MIT sloan management review, 2011, 52(2):21-31.
[24] Teisman G R, Klijn E H. Complexity theory and public management:An introduction[J]. Public Management Review, 2008,10(3):287-297.
[25] Heidorn P B. Shedding light on the dark data in the long tail of science[J]. Library Trends, 2008, 57(2):280-299.
[26] Davenport T H, Barth P, Bean R. How big data is different[J]. MIT Sloan Management Review, 2012, 54(1):22-24.
[27] Bozeman B, Pandey S K. Public management decision making:Effects of decision content[J]. Public Administration Review,2004, 64(5):553-565.
[28] Light, Paul, and George Butterworth(eds). Context and cognition:Ways of learning and knowing[M]. New York:Routledge,2016.
[29] Girard Y, Hett F, Schunk D. How individual characteristics shape the structure of social networks[J]. Journal of Economic Behavior&Organization, 2015, 115:197-216.
[30] Hassan, S., Antunes, L.,&Arroyo, M. Deepening the demographic mechanisms in a data-driven social simulation of moral values evolution[A]. In International Workshop on Multi-Agent Systems and Agent-Based Simulation[C]. Berlin Heidelberg:SpringerVerlag, 2008.
[31] Fan W, Bifet A. Mining big data:current status, and forecast to the future[J]. ACM SIGKDD Explorations Newsletter, 2013,14(2):1-5.
[32] Li A S, Soh B, Bhatti M I, et al. Data-centric view of knowledge in information systems literature review[J]. International Journal of Services, Economics and Management, 2016, 7(2-4):111-123.
[33] Madden S. From databases to big data[J]. IEEE Internet Computing, 2012, 16(3):4-6.
[34]大数据战略重点实验室.块数据[M].北京:中信出版社, 2015.
[35] Frankel F, Reid R. Big data:Distilling meaning from data[J]. Nature, 2008, 455(7209):30-30.
[36] Berman J J. Principles of big data:preparing, sharing, and analyzing complex information[M]. San Francisco:Morgan Kaufmann Publishers Inc., 2013.
[37]王天思.大数据中的因果关系及其哲学内涵[J].中国社会科学, 2016,(5):22-42.
[38}温有奎,乔晓东.大数据下智慧发现服务平台研究[J].情报学报, 2015, 34,(11):1181-1189.
[39] Wu X, Zhu X, Wu G Q, et al. Data mining with big data[J]. IEEE transactions on knowledge and data engineering, 2014,26(1):97-107.
[40] Kostyuchenko, Y. V., Movchan, D., Kopachevsky, I.,&Bilous, Y. Robust algorithm of multi-source data analysis for evaluation of social vulnerability in risk assessment tasks[A]. In SAI Intelligent Systems Conference(IntelliSys)[C]. London:IEEE, 2015.
[41] R覿ssler S. Data fusion:identification problems, validity, and multiple imputation[J]. Austrian Journal of Statistics, 2016, 33(1&2):153-171.
[42] Olshannikova E, Ometov A, Koucheryavy Y, et al. Visualizing Big Data with augmented and virtual reality:challenges and research agenda[J]. Journal of Big Data, 2015, 2(1):1-27.