产业智库大数据方法体系及其应用场景建设

Construction of Big Data Method System and Its Application Scenes for Industrial Think Tanks

摘要:

为促使我国产业智库大数据服务向更加前沿、主动、高效、智能的方向发展，尤其是将大数据计算能力更好地转化为战略决策支持能力，赋能我国产业数字化转型升级和国际竞争力提升，研究如何借助大数据方法实现产业智库研究模式与流程变革。产业智库在战略目标、管理重点、服务对象、社会功能等方面具有特殊性，通过回顾大数据方法在产业智库中的相关研究，得出产业智库大数据独特的方法论逻辑：从方法论的角度来看，围绕“出政策、出思想、出声音”的智库功能要求，以产业竞争情报、大数据方法以及智库研究领域的理论为指导，涵盖数据、分析与决策三方面的研究方法与工具；从作为具体方法的角度来看，概括为数据方法、分析方法与决策方法3种，为产业链构建及竞争情报需求发掘、产业技术路线图开发及技术评估、产业政策分析及政策主题识别、企业竞争力评价及创新战略布局、产业市场分析及发展前景预测等应用场景提供有力支撑。同时结合中国产业智库大数据中心的案例，以低碳产业为例阐释产业智库大数据方法的应用场景。最后为进一步优化产业智库产品供给质量、提升智库决策服务效率，提出从创新大数据方法思维、提升大数据构建和分析能力、培育大数据人才、优化大数据环境等方面重点完善产业智库大数据建设。

Abstract:

In order to promote more frontal, proactive, efficient and intelligent big data services of industrial think tanks in China, especially to better transform big data computing power into strategic decision support capability, and to empower the digital transformation and upgrading of China's industry and the enhancement of international competitiveness, this paper studies how to realize the research mode and process reform of industrial think tanks by means of big data methods. Industrial think tanks have particularities in strategic objectives, management priorities, service objects and social functions, this paper reviews the related research of big data methods for industrial think tanks, and concludes the unique methodology logic of big data for industrial think tanks. From the perspective of methodology, big data for industrial think tanks is based on the functional requirements with policies, ideas and voices, guided by the basic theories of industrial competitive intelligence, big data methods and think tank research, which covers the research methods and tools of data, analysis and decision-making. From the perspective of specific methods, the big data methods for industrial think tanks provide strong supports in these application scenes such as industrial chain construction and competitive intelligence demand discovery, industrial technology roadmap development and technology evaluation, industrial policy analysis and policy theme identification, enterprise competitiveness evaluation and innovation strategy layout, industrial market analysis and development prospect prediction. At the same time, combined with the case of China's industrial think tank big data center, this paper takes low-carbon industry as an example to explain the application scenes of big data methods for industrial think tanks. Finally, in order to further optimize the products supply quality and improve the decision-making services efficiency of industrial think tanks, it is proposed to focus on improving the big data construction of industrial think tanks from the aspects of innovating big data methods thinking, improving big data construction and analysis ability, cultivating big data talents, and optimizing big data environment.