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- 저자 : 진성만(Seongman Jin) (검색결과 2 건)
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황석원(Seogwon Hwang),양승우(SeungWoo Yang),안두현(Doo Hyun Ahn),이세준(Sejun Lee),신은정(Eunjung Shin),양현채(Hyeonchae Yang),한웅규(Ungkyu Han),강희종(Hee Jong Kang),이다은(Daeun Lee),진성만(Seongman Jin),송충한(ChoongHan Song),김해도 과학기술정책연구원 2020 정책연구 Vol.- No.-
In the era of digital transformation, the government is pushing for R&D system integration. The goals of this study are as follows. It presents theories and methodologies for innovation in data-driven R&D management. Second, we propose an innovation policy plan for data-driven R&D management. According to previous studies, data-driven R&D has recently become important due to evidence-based science and technology policy, science of science policy, the 4th industrial revolution, and big data. In 2013, the OECD announced Data-driven Innovation. Korea is linking and integrating R&D databases (e.g. NTIS ; National Science & Technology Information Service). The vision and framework of this study is as follows. The vision of this research is to build a ‘national science and technology data platform’ for data-driven R&D. The ‘national science and technology data platform’ is the most important to meet the needs of data users. Therefore, the framework of this study identifies the user needs of the R&D process. The R&D process consists of five fields (planning / selection / execution / performance evaluation / performance utilization / infrastructure). First, Planning Innovation for Data-driven R&D. R&D planning refers to an activity that prepares efficient means to achieve R&D goals. R&D planning methods is largely divided into Qualitative Method and Quantitative Method. The Qualitative Method is a method of utilizing the experience and opinion of experts. Quantitative Method is a method that utilizes statistics and trend analysis. Recent R&D Planning uses a mixture of Qualitative Method and Quantitative Method (e.g. technology roadmap, DelpHi, Technology Life Cycle, Cross Impact Analysis, etc.). The planning innovation policy task for data-driven R&D suggests building a platform for integrating and providing R&D planning information, constant monitoring, establishing a BERA (Big-data based Early-stage R&D Analysis) centers, and providing consulting services. Second, Selection and Implementation Innovation for Data-driven R&D. As a result of the survey, there is a problem with fairness in the selection and implementation of R&D. To solve the problem of selection and implementation, it is necessary to build a big data platform. The selection and implementation innovation policy task suggests step-by-step intelligence, the establishment of an R&D big data ecosystem, and the use of advanced technologies. Third, Performance Evaluation Innovation for Data-driven R&D. Performance Evaluation refers to an activity that evaluates R&D according to indicators. Performance evaluation lacks autonomy, strategy, expertise, and openness. Therefore, the Performance Evaluation Innovation policy tasked to suggest accumulation, linkage, and sharing with evaluation information, utilization of evaluation results, and establishment of a policy monitoring system. Fourth, Research Utilization Innovation for data-driven R&D. Research Utilization lacks user supported services (supplier-centered management), connectivity/openness, technical/policy basis, information protection standards, and compensation system. Therefore, the Research Utilization Innovation policy task was to propose user-oriented information provision, activation of DB-linked services, reinforcement of access to research data, and establishment of rules for using research information. Fifth, Data Infrastructure Innovation for Data-driven R&D. Data Infrastructure is the most important to collect data that can be utilized. Therefore, it should be reorganized centering on the users (organizations) used. In conclusion, this study proposes the establishment of a ‘national science and technology data platform’ for the development of data-driven R&D innovation policies. The ‘national science and technology data platform’ should be developed step by step (1st to 3rd steps).
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