초록 ▼

· 본 연구는 국가 과학기술 빅데이터 주관기관으로서 한국과학기술정보연구원(KISTI)이 빅데이터 분야에 선도적인 연구 역량을 확보하기 위하여 실시한 기본 연구로서, 공공부문의 빅데이터 최신 이슈들 파악하고 분석하여 KISTI 3개 연구센터의 협력연구과제 도출을 위한 시사점을 제시하는 것을 주요 목적으로 연구를 수행하였으며, 연구결과 도출 및 검토된 중요 내용은 다음과 같음
· 공공부문 빅데이터의 개념을 정리하고 생태계 개념을 도입하여, 공공부문의 빅데이터의 주요성공요인(CSF)와 핵심 연구영역을 빅데이터 생태계의 거버넌스적인

· 본 연구는 국가 과학기술 빅데이터 주관기관으로서 한국과학기술정보연구원(KISTI)이 빅데이터 분야에 선도적인 연구 역량을 확보하기 위하여 실시한 기본 연구로서, 공공부문의 빅데이터 최신 이슈들 파악하고 분석하여 KISTI 3개 연구센터의 협력연구과제 도출을 위한 시사점을 제시하는 것을 주요 목적으로 연구를 수행하였으며, 연구결과 도출 및 검토된 중요 내용은 다음과 같음
· 공공부문 빅데이터의 개념을 정리하고 생태계 개념을 도입하여, 공공부문의 빅데이터의 주요성공요인(CSF)와 핵심 연구영역을 빅데이터 생태계의 거버넌스적인 면과 기술적인 면을 고려하여 아래와 같이 4개의 핵심 연구영역으로 결정하였음
① 사업관리 ② 환경조성 영역 ③ 빅데이터 서비스 영역 ④ 기술/플랫폼 영역
· 이상의 4개의 각 핵심 연구영역에 해당되는 상세이슈들을 찾아내고 프레임워크에 기반하여 이슈트리를 구성하였으며, 최종적으로 4개 분야, 15개 주요이슈, 총 55개의 세부이슈들을 도출하였는데, 최종적으로 빅데이터 환경조성이 11개, 빅데이터 서비스가 19개, 빅데이터기술/풀랫폼이 14개, 그리고 빅데이터 사업관리가 11개로 파악되어 빅데이터 서비스와 기술/플랫폼에 가장 많은 이슈들이 포함되었음
· 국내 전문가 집단을 대상으로 공공부문 빅데이터 각 상세이슈들에 대하여 중요도와 시급도를 설문 조사하여 공공차원에서 어느 분야에 역량을 집중하고, 신규 연구영역을 발굴하여 진행하는 것이 효과적일 수 있는가에 대한 잠재적 수요를 파악함
· 각 상세 이슈들에 대한 설문조사 분석과 아울러 주요이슈들에 대한 분석과 핵심 연구영역에 대한 비교 분석을 실시하면서 다음과 같은 상위수준의 시사점들을 도출함
(1) 환경조성에 대한 구체적인 접근, (2) 시범사업과 연계, (3) 소프트웨어 플랫폼 구축 사업필요, (3-1) 공공부문 빅데이터 플랫폼용 독립 엔진의 개발, (3-2) 플랫폼용 관리/도구의 필요, (3-3) 분산/병렬 처리 기술개발 프로젝트의 실시, (4) 중요성이 높은 이슈들을 조속히 추진, (5) 중요성이 높은 이슈들은 대부분 융합 R&D로 접근 가능, (6) 시범사업에 신중, (7) 일관된 프레임워크, (8) 현업 담당자에게 빅데이터 교육, (9) 예측분석에 대한 체계적 접근이 필요함, (10) 빅데이터를 스타트업(벤처) 기업 양성의 토대로 삼아야 함

Abstract ▼

3. Result contents and results
1) Definition of Public Sector Big Data and Ecology Model
○ In this research, we propose the extended concept of big data in public sector, which drives from the generally accepted big data concept that includes the value-added aspects of government service and t

3. Result contents and results
1) Definition of Public Sector Big Data and Ecology Model
○ In this research, we propose the extended concept of big data in public sector, which drives from the generally accepted big data concept that includes the value-added aspects of government service and the infrastructure for the sharing/utilization of big data
○ Then big data ecology is analyzed to determine the scope of the big data in public sector. As we are in the initial stage of big data, it is critical to build the infrastructure for ecology and to manage the big data service projects that are already undergoing in the government institutions.
The governance of big data is closely examined from the following perspectives: government policy, role, and value chain.
And from the technological perspective, we reviewed the software platform, the structure of the big data solution, and the tools for big data analytics.
○ These findings have significant implications on developing framework for organizing the issue tree and on detecting the detailed issues
2) The determination of big data issues in public sector
○ The process of developing the issues tree is as follows:
We first describe the objective of issue tree which was drawn from the main goal of this research, and then organize the structure of issue tree by using the framework described above. Finally, the detail issues are confirmed by reviewing the big data expert group.
○ The first level of issue tree applies to the main areas which is a critical success factors(CSF) for achieving the objective of issue tree. main areas are as follows:
Management of big data project: focuses on budget, infrastructure and scope of projects related with effectiveness and efficiency.
Environments for big data: focuses on education and legal system related with successful implementation.
Big data service: focuses on service areas, responsibility lies, business commercialization related with consideration for providing the service.
Technology and Platform: focuses on data acquisition/cleansing, data processing, forecasting/prediction, platform construction related with service providing and system implementation during the big data life cycle.
○ We decide the final issues tree to consist of: 4 main areas, 15 issue branches, 55 detailed issues (Environments for big data 11, Big data service 19, Technology and Platform 14, Management of big data project 11). Following Table demonstrates the developed issue tree.
3) Public Sector Big Data Issues Survey and Results
○ The survey objectives are to prioritize in terms of the importance and urgency for the detailed issues found at early stage. They would give us a guideline to recognize which areas are to be focused.
○ Questionnaire survey respondents are people who are expert on big data working at areas such as business enterprise, research institution, consulting firms, government and academy.
The following analyzes the nature (affiliation/occuption) of 65 participants based on their answers on the occupation part. Among all participants, 78.5% are between 40-50 years of age, 49.2% have a Ph.D degree, and 26.2% have a Masters degree.
○ IBM SPSS Statistics Ver19.0 tool was used perform One-Way Anova and frequency analysis. Both survey analysis methods examined the variance in responses from the occupation part, while recognizing the importance and the urgency of the survey participants.
○ We evaluated the importance on all detailed issues, and the urgency is statistically analyzed in terms of the upper-level branch issue--all to be used as the basis of deriving the implication.
4) Deriving the implication through analyzing the issue
○ We present the following implications by analyzing the critical success factors for big data ecology in public sector. Table shows the importance of upper-level issue.
Implication 1. Systemic approach for building the big data environment in public sector.
Implication 2. Developing and sharing big data technologies related to pilot project
Implication 3. Need for constructing a software platform dedicated to public big data
Implication 3-1. Development of analytics software components applicable to S/W platform
Implication 3-2. Development of platform management components, UI(user interface) and tools
Implication 3-3. Need a research project for development of distribution/paralle computing technology applicable to big data platform.
Implication 4. High scored importancy issues should be considered as soon as possible.
Implication 5. Most high scored importancy issues needs convergence R&D
Implication 6. Careful considerations for selecting and progressing the pilot project
Implication 7. Maintaining the consistent standard framework in government level
Implication 8. In current status, it is important to train the government workers related big data business in a hurry, and bring up data scientist should be considered in the long term.
Implication 9. Need a systemic approach for learning and applying predictive analytics
Implication 10. Need a strategy to activate the public big data based business strart-up