DOI QR코드

DOI QR Code

빅데이터 품질이 기업의 경영성과에 미치는 영향에 관한 연구

A study on the Effect of Big Data Quality on Corporate Management Performance

  • 이충형 (고려대학교 기술경영전문대학원) ;
  • 김영준 (고려대학교 기술경영전문대학원)
  • Lee, Choong-Hyong (Graduate School of Management of Technology, Korea University) ;
  • Kim, YoungJun (Graduate School of Management of Technology, Korea University)
  • 투고 : 2021.06.13
  • 심사 : 2021.08.20
  • 발행 : 2021.08.28

초록

4차산업혁명시대에 정보통신기술의 비약적인 발전, 고객구매 성향의 다양함, 복잡함은 산업 전체적으로 데이터의 양적 중가를 가져와 '빅데이터' 시대를 맞이하게 되었다. 빅데이터 시대는 데이터를 분석, 활용하여 기업의 전략적 의사결정에 활용하는 것이 기업의 핵심 역량으로 자리 잡게 되었다. 하지만 현재 빅데이터 연구들은 기술적 이슈와 미래 잠재 가치 중심이었다. 반면 기업이 보유한 내.외부 고객 빅데이터의 품질 및 활용 수준관리에 대한 연구와 논의는 부족하였다. 본 연구에서는 기업의 내.외부 빅데이터 품질관리 정보시스템 측면와 품질경영 측면으로 인식하여 영향요인을 도출하였다. 또한 빅데이터 품질관리, 빅데이터 활용 및 수준관리가 기업의 업무 효율화와 기업 경영성과에 유의한 영향을 미치는지 204명의 임직원 설문을 통해 조사하였고, 가설을 설정하여 검증하였다. 연구결과 경영층의 지원, 개인 혁신성, 경영환경변화, 빅데이터 품질활용 지표관리, 빅데이터 거버넌스 체계 마련이 기업 경영성과에 유의한 영향을 미쳤다.

The Fourth Industrial Revolution brought the quantitative value of data across the industry and entered the era of 'Big Data'. This is due to both the rapid development of information & communication technology and the diversity & complexity of customer purchasing tendencies. An enterprise's core competence in the Big Data Era is to analyze and utilize the data to make strategic decisions for enterprise. However, most of traditional studies on Big Data have focused on technical issues and future potential values. In addition, these studies lacked interest in managing the quality and utilization levels of internal & external customer Big Data held by the entity. To overcome these shortages, this study attempted to derive influential factors by recognizing the quality management information systems and quality management of the internal & external Big Data. First of all, we conducted a survey of 204 executives & employees to determine whether Big Data quality management, Big Data utilization, and level management have a significant impact on corporate work efficiency & corporate management performance. For the study for this purpose, hypotheses were established, and their verifications were carried out. As a result of these studies, we found that the reasons that significantly affect corporate management performance are support from the management class, individual innovation, changes in the management environment, Big Data quality utilization metrics, and Big Data governance system.

키워드

과제정보

This paper was supported by Korea University Research Grant in 2021.

참고문헌

  1. C. H. Lee. (2021) A data extension technique to handle incomplete data. Journal of the Korea Convergence Society, 2(12), 7-13. DOI : 10.15207/JKCS.2021.12.2.007
  2. S. H. Shin & S. J. Lee. (2019) The Key Factors of Big Data Utilization for Improvement of Management Quality of Companies in terms of Technology, Organization and Environment. Journal of Information Technology Services, 1(18), 91-112, DOI : 10.9716/KITS.2019.18.1.091
  3. D. P. Seo & B. S. Kim. (2020). The Effect of Government's Support Policy and Experience on the Performance of SMEs. Journal of the Korea Convergence Society, 8(11), 195-201. DOI : 10.15207 /JKCS.2020.11.8.195 https://doi.org/10.15207/JKCS.2020.11.8.195
  4. J. S. Park, S. Y. Kim, & J. H. Lee. (2017) Applying Service Quality to Big Data Quality. The Korea Journal of BigData, 2(2), 87-93, DOI : 10.36498/kbigdt.2017.2.2.87
  5. H. J. Ahn & H. S. Kim. (2015). A Business Performance Study of Data Quality Management for Big Data Adoption. KOOKMIN University. Seoul
  6. H. S. Koo. (2019). A Study on Factors Affecting the Intention to Use Big Data in Businesses.. SOONGSIL University, Seoul
  7. J. S. Lee. (2017). An empirical study on the effect of innovation financing on technology innovation competency and firm performance of SMEs. KOREA University, Seoul
  8. S. K. Kim & M. K. Kim. (2017). A Study on the Effect of Financial Company Intention to Use and Business Performance in Big Data. Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology, 7(9), 31-41, DOI : 10.35873/ajmahs.2017.7.9.004
  9. J. H. Lee. (2017). A Study on Automation of Big Data Quality Diagnosis Using Machine Learning. The Korea Journal of BigData, 2(2), 78-86, DOI : 10.36498/kbigdt.2017.2.2.75
  10. C. S. Cho, N. K. Lee & Y. K. Ham. (2020) 'Methodology for Evaluating Big Data Platforms Performance in the Domestic Electronic Power Industry. The Korea Journal of BigData, 5(1), 97-108, DOI : 10.36498/kbigdt.2020.5.1.97
  11. S. J. Choi. J. W. Park. J. B. Kim & J. H. Choi. (2014). A Quality Evaluation Model for Distributed Processing Systems of Big Data. Journal of Digital Contents Society, 15(4), 533-545 DOI : 10.9728/dcs.2014.15.4.533
  12. S. H. Kim. Y. K. Seo & B. C. Tak. (2020). A Recommendation Scheme for an Optimal Pre-processing Permutation Towards High-Quality Big Data Analytic. Journal of KIISE (JOK), 47(3), 319-327, DOI : 10.5626/JOK.2020.47.3.319
  13. H. Y. Woong. (2014). A study on the invigorating strategies for open government data. Journal of the Korean Data And Information Science Sociaty, 25(4), 769-777. DOI : 10.7465/jkdi.2014.25.4.769
  14. U. K. Hahm. (2017). Data Integration Strategy in Big Data Era: A Public Sector Case Analysis. Korea Institute of Enterprise Architecture, 14(2), 115-128
  15. J. B. Kim, Y. J. Kim & J. H. Park. (2019). A Study on Auditing Method of Big Data Technology Applied System. The Korea Society of Information Technology Policy & Management, 11(3), 1255-1267
  16. J. K. Bae. (2021). A Study on the Legal and Institutional Factors for Activation of MyData Industry'. Logos Management Review, 19(1), 117-132
  17. D. W. Hyun & S. Y. Lee. (2021). A Study of Big Data Analysis Regarding Smartphone User Satisfaction: Utilizing Sentiment Analysis Based on Social Media Data. Korean Journal of Converging Humanities (KJCH), 9(1), 7-35, DOI : 10.14729/converging.k.2021.9.1.7
  18. J. S. Park. (2018). A Comparative Study of Big Data, Open Data, and My Data'. The Korea Journal of BigData, 3(1), 41-46. DOI : 10.36498/kbigdt.2018.3.1.41
  19. Y. W. Kim. (2014). A study on Convergent & Adaptive Quality Analysis using DQnA model', Journal of the Korea Convergence Society, 5(4), 21-25, UCI : G704-SER000004000.2014.5.4.009 https://doi.org/10.15207/JKCS.2014.5.4.021
  20. D. H. Kwag. (2018). Development of service scale on new perspective by using big data'. Event & Convention Research, 14(4), 101-121, DOI : 10.31927/asec.14.4.6