Journal of the Korean Society for information Management (정보관리학회지)

Korean Society for Information Management (한국정보관리학회)
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A Study on Development of Digital Curation Maturity Models and Indicators: Focusing on KISTI

디지털 큐레이션 성숙도 모델 및 지표 개발에 관한 연구: 한국과학기술정보연구원 디지털큐레이션센터를 중심으로

  • 김성훈 (성균관대학교 문헌정보학과) ;
  • 도슬기 (성균관대학교 문헌정보학과 ) ;
  • 한상은 (카이스트 디지털인문사회과학센터 ) ;
  • 김재훈 (KISTI 디지털큐레이션센터 데이터표준화팀 ) ;
  • 임석종 (KISTI 디지털큐레이션센터 데이터표준화팀 ) ;
  • 박진호 (한성대학교 크리에이티브 인문학부)
  • Received : 2022.11.20
  • Accepted : 2022.12.13
  • Published : 2022.12.30
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Abstract

This study aimed to develop indicators that can measure the digital transformation performance of science and technology information construction and sharing systems by utilizing the Digital Curation Maturity Models. For digital transformation, it is necessary to consider not only simple service improvement but also organizational and business changes. In this study, we aimed to develop a model for measuring the digital transformation of KISTI, Korea's representative science and technology information service organization. KISTI has already carried out BPR work for digital transformation and borrowed the concept of a maturity model. However, in BPR, there is no method to measure the result. Therefore, in this paper, we developed an index to measure digital transformation based on the maturity model. Indicator development was carried out in two ways: model development and evaluation. Cases for model construction were made through a comprehensive review of existing KISTI and various domestic and foreign cases. The models before verification were technology (37), data (45), strategy (18), organization (36), and (social)influence (14) based on the major categories. After verification using confirmatory factor analysis, the model is classified as technology (20 / 17 indicators dropped), data (36 / 9 indicators dropped), strategy (18 / maintenance), organization(30 / 6 indicators dropped), and (social) influence (13 indicators / 1 indicator dropped).

본 연구는 성숙도 모델 개념을 활용하여 디지털 전환 성과를 측정할 수 있는 지표 개발을 시도하였다. 디지털 전환을 위해서는 단순한 서비스 개선이 아니라 조직, 업무 변화까지를 고려할 필요가 있다. 여기서는 우리나라의 대표적인 과학기술정보서비스 기관인 KISTI의 디지털 전환 측정을 위한 모델 개발을 목표로 하였다. KSITI는 이미 디지털 전환을 위한 BPR 작업을 수행한 바 있으며, 성숙도 모델 개념을 차용하였다. 단, BPR에서는 해당 결과를 측정할 수 있는 방법은 존재하지 않는다. 본 논문에서는 성숙모 모델을 기반으로 디지털 전환을 측정할 수 있는 지표를 개발하였다. 지표개발은 모델 개발과 평가 두 가지 방법으로 수행하였다. 모델 구성을 위한 사례는 기존 KISTI에서 수행한 관련 연구, 다양한 국내·외 사례를 통해 이루어졌다. 검증 전 모델은 대분류를 기준으로 기술(37개), 데이터(45개), 전략(18개), 조직(인력)(36개), (사회적)영향력(14개)이었다. 검증 후에 최종 모델은 기술(20개/17개 지표 탈락), 데이터(36개/9개 지표 탈락), 전략(18개/유지), 조직(인력)(30개/6개 지표 탈락), (사회적)영향력(13개/1개 지표 탈락)으로 구성되었다.

Keywords

Acknowledgement

본 연구는 2022년도 한국과학기술정보연구원(KISTI) 기본사업 과제 "지능형 과학기술정보 큐레이션 체제 구축" (K-22-L01-C01-S01)으로 수행되었음.

References

  1. Data Quality Management Requirements for Supervised Learning. TTAK.KO-10.1339:2021
  2. Heo, Myung-Sook & Cheon, Myun-Joong (2021). A study on the digital transformation readiness through developing and applying digital maturity diagnosis model: focused on the case of a s company in oil and chemical industry. Korean Management Review, 50(1), 81-114. http://doi.org/10.17287/kmr.2021.50.1.81
  3. Hong, Sung-Woo, Choi, Yun-Hee, & Kim, Gwang-Yong (2019). A study of development of digital transformation capacity. Journal of The Korea Society of Information Technology Policy & Management, 11(5), 1371-1381.
  4. Jung, Hye-Jung (2007). A study of the data qualituy evaluation. Journal of Internet Computing and Services, 8(4), 119-128.
  5. Kim, Min-Jun & Lim, Min-Seong (2020). Data quality control for data dams. TTA Journal, 192, 34-40.
  6. Kim, Sun-Ho, Lee, Chang-Soo, & Kim, Hak-Chul (2015). Public data quality management level evaluation model. Proceedings of the Fall Conference of the Industrial Engineering Society of Korea, 2417-2422. http://doi.org/10.22693/NIAIP.2017.24.1.030
  7. Kim, Sun-Ho, Lee, Chang-Soo, & Kim, Hak-Chul (2017). An organizational maturity assessment model for public data quality management. Proceedings of the Fall Conference of the Industrial Engineering Society of Korea, 2417-2422. http://doi.org/10.22693/NIAIP.2017.24.1.030
  8. Korea Education and Research Information Service (2021). A Plan to Establish Inclusive Future Education Governance in Response to Digital Transformation. Daegu Metropolitan City: Korea Education and Research Information Service.
  9. Korea Institute of Public Administration (2021). Development and Utilization of Digital Level Diagnosis Model in Public Sector. Seoul: Korea Institute of Public Administration.
  10. National Information Society Agency (2018). Public Data Quality Management Manual v2.0. Daegu Metropolitan City: Korea Information Society Agency.
  11. National Information Society Agency (2021). Data Quality Management Guidelines for AI Training v1.0
  12. National Information Society Agency (2022). Data Quality Management Guidelines for AI Training v2.0
  13. National Library of Korea (2021. 09. 28.). A National Library Leading the Digital Transformation. Source: https://www.nl.go.kr/NL/contents/N50603000000.do?schM=view&id=40107&schBcid=normal0302
  14. National Science and Technology Data Center Content Curation Center (2020). Establishment of Science and Technology Content Curation System. Daejeon: Korea Institute of Science and Technology Information.
  15. Noh, Kyungseop (2019). The Proper Methods of Statistical Analysis for Dissertation. Seoul: Hanbit Academy.
  16. Park, Sung-Soon & Cho, Kwang-Seop (2021). The Successful Start of Digital Transformation. Samsung SDS Insight Report. Avaliable: https://www.samsungsds.com/kr/insights/dta.html
  17. Rhee, Gyu-Yurb, Park, Sang-Chul, & Ryoo, Sung Yul (2020). Performance measurement model for open big data platform. Knowledge Management Review, 21(4), 243-263. http://doi.org/10.15813/kmr.2020.21.4.013
  18. Shin, Junho (2021). Data quality verification method for artificial intelligence learning. Journal of the Electronic Engineering Society, 48(7), 28-34.
  19. Woo, Jong-Pil (2012). Professor Jong-pil Woo's Concept and Understanding of Structural Equations. Seoul: Hannarae Publishing House.
  20. Data quality - Part 150: Data Quality Management: Roles And Responsibilities. ISO 8000-150:2022
  21. Data quality - Part 61: Data Quality Management: Process Reference Model. ISO 8000-61:2016
  22. DataOne [n.d.]. DataONE Data Management Primer. Available: https://repository.oceanbestpractices.org/bitstream/handle/11329/502/DataONE_BP_Primer_020212.pdf
  23. Gartner [n.d.]. Digital Government Urgency, Readiness and Maturity. Avaliable: https://surveys.gartner.com/s/DigitalGovernmentMaturity
  24. IMD World Competitiveness Center (2021). IMD World Digital Competitiveness Ranking 2021. Available: https://imd.cld.bz/Digital-Ranking-Report-2021
  25. IMPULS [n.d.]. IMPULS Industrie 4.0-Readiness Online-Selbst-Check fur Unternehmen. Available: https://www.industrie40-readiness.de
  26. Lanin, I. (2008). Capability Maturity Model Integrity (CMMI). Avaliable: https://pt.slideshare.net/ivanlanin/capability-maturity-model-integrity-cmmi/6
  27. OECD (2019). Measuring the Digital Transformation A Roadmap for the Future. Available: https://www.oecd.org/digital/measurement-roadmap.pdf
  28. Parasuraman, A., Zeithaml, V. A., & Berry, L. (1988). Servqual: a multiple-item scale for measuring consumer perceptions of service quality. 1988, 64(1), 12-40.
  29. Portulans Institute (2021). Network Readiness Index 2021. Available: https://networkreadinessindex.org/nri-2021-edition-press-release/
  30. Principe, P., Manghi, P., Bardi, A., Vieira, A., Schirrwagen, J., & Pierrakos, D. (2019). A User Journey in OpenAIRE Services Through the Lens of Repository Managers.
  31. Quality management systems - Requirements. ISO 9001:2015
  32. RDA FAIR Data Maturity Model WG (2020). FAIR Data Maturity Model; Specification and Guidelines. Available: http://www.rd-alliance.org/groups/fair-data-maturity-model-wg
  33. Singapore Economic Development Board (2020). The Smart Industry Readiness Index. Available: https://www.edb.gov.sg/en/about-edb/media-releases-publications/advanced-manufacturing-release.html
  34. Software engineering - Product quality - Part 1: Quality Model. ISO/IEC 9126-1:2001
  35. Software engineering - Software product Quality Requirements and Evaluation (SQuaRE) - Data Quality Model. ISO/IEC 25012:2008.
  36. Stuart, D., Baynes, G., Hrynaszkiewicz, I., Allin, K., Penny, D., Lucraft, M., & Astell, M. (2018). Whitepaper: Practical Challenges for Researchers in Data Sharing (Version 1)