DOI QR코드

DOI QR Code

A Research on Job Model Development for Data Convergent Talent

데이터 융합인재 직무모형 개발 연구

  • Received : 2024.03.13
  • Accepted : 2024.03.27
  • Published : 2024.03.31

Abstract

Purpose This study aims to develop a job model for data convergent talents to meet the rapidly changing demands of the data industry. To create a job model, we first define and categorize data convergent talents with balanced competencies in data technology and domain knowledge, and then develop a job model by investigating job areas, scope, activities, and competencies. Design/methodology/approach The research is conducted using the following procedures and methodology. First, we conduct a current status survey on data talent demand, data talent policies, data talent programs, and curricula at home and abroad; second, we collect opinions on the jobs and competencies required for data convergent talents and curricula for talent development through in-depth interview with experts; and third, we present the job areas and job activities of data convergent talents derived from the previous status survey and expert opinions based on the National Competency Standards(NCS). Findings The research findings indicate that there are total of six job roles for data convergent talents, including data scientist, data planner, data architect, data developer, data engineer, and data analyst. It was observed that each of these roles requires the development of common competencies within their respective fields, followed by a need for further specialization into specific competencies within each professional domain.

Keywords

References

  1. 박성수, 황호영, 이경근, 전명숙, 채준호, "디지로그 시대의 인적자원관리", 제4판, 서울:박영사, 2016.
  2. 박성환, 이준우, "역량중심 인적자원관리", 제3판, 경기:법문사, 2014.
  3. 장수용, "직무분석 조사기법", 전략기업컨설팅, 2006.
  4. 전영욱, 김진모, "기업체 인적자원개발 담당자의 핵심직무역량모델 개발," 농업교육 과 인적자원개발, 제37권, 제2호, 2005, pp. 111-138.
  5. 정한민, 송사광, "빅데이터 시대의 인재양성 전략," 인터넷정보학회지, 제13권 제3호, 2012, pp. 48-53.
  6. 한국데이터산업진흥원, "2022 데이터산업 현황 조사", 2023.
  7. 한국데이터진흥원, "데이터 전문인력 양성 방안 연구 결과 보고서", 2016
  8. Boyatzis, R. E., "The Competent Manager: A Model for Effective Performance", New York: Wiley, 1982.
  9. Davenport, T. H., "빅데이터@워크", 김진호 옮김, 경기:21세기북스, 2014.
  10. DeAngelis, J. T. and Wolcott, M. D., "A Job Analysis to Define the Role of the Pharmacy Preceptor," American Journal of Pharmaceutical Education, Vol. 83, No. 7, 2019, pp. 1480-1491. https://doi.org/10.5688/ajpe7196
  11. Ho, S. Y. and Frampton, K., "A Competency Model for the Information Technology Workforce: Implications for Training and Selection," Communications of the Association for Information Systems, Vol. 27, No. 1, 2010, pp. 1-21. https://doi.org/10.17705/1CAIS.02705
  12. Hu, H., Luo, Y., Wen, Y. , Ong, Y. S., and Zhang, X., "How to Find a Perfect Data Scientist: A Distance-Metric Learning Approach," IEEE Access, Vol. 6, 2018, pp. 60380- 60395. https://doi.org/10.1109/ACCESS.2018.2870535
  13. Kart, L. and Laney, D., "Emerging Role of the Data Scientist and the Art of Data Science," Gartner Research, 2012, March, https://www.gartner.com/en/documents/1955615/emerging-role-of-the-data-scientist-and-the-art-of-data-.
  14. LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., and Kruschwitz, N., "Big Data, Analytics and the Path from Insights to Value," Sloan Management Review, Vol. 52, No. 2, 2011, pp. 21-32.
  15. McCormick, E. J., "Job and Task Analysis," In M. D. Dunnette (Ed,), Handbook of Industrial and Organizational Psychology, Chicago: Rand McNally, 1976, pp. 651-696.
  16. OECD, "Data-Driven Innovation Big Data for Growth and Well-Being", Chapter 6, 2015, pp. 237-298.
  17. Sanchez, J. I. and Levine, E. L. (2009), "What is (or should be) the Difference between Company Modeling and Traditional Job Analysis?," Human Resource Management Review, Vol. 19, No. 2, 2009, pp. 53-63.
  18. Sanchez, J. I. and Levine, E. L., "The Rise and Fall of Job Analysis and the Future of Work Analysis," Annual Review of Psychology, Vol. 63, 2012, pp. 397-425. https://doi.org/10.1146/annurev-psych-120710-100401