The development of the data science capability maturity model: a survey-based research

GÖKALP M. O., Gokalp E., Kayabay K., KOÇYİĞİT A., EREN P. E.

ONLINE INFORMATION REVIEW, vol.46, no.3, pp.547-567, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 46 Issue: 3
  • Publication Date: 2022
  • Doi Number: 10.1108/oir-10-2020-0469
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, FRANCIS, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, CINAHL, Communication Abstracts, Compendex, Computer & Applied Sciences, EBSCO Education Source, Education Abstracts, Information Science and Technology Abstracts, INSPEC, Library and Information Science Abstracts, Library Literature and Information Science, Library, Information Science & Technology Abstracts (LISTA), Metadex, vLex, DIALNET, Civil Engineering Abstracts
  • Page Numbers: pp.547-567
  • Keywords: Data science, Big data, Digital transformation, Data analytics, Data-driven organization, Maturity assessment, Business analytics, BUSINESS INTELLIGENCE MATURITY, BIG DATA, ANALYTICS, MANAGEMENT, FRAMEWORK
  • Hacettepe University Affiliated: Yes


Purpose The purpose of this paper is to investigate social and technical drivers of data science practices and develop a standard model for assisting organizations in their digital transformation by providing data science capability/maturity level assessment, deriving a gap analysis, and creating a comprehensive roadmap for improvement in a standardized way. Design/methodology/approach This paper systematically reviews and synthesizes the existing literature-related to data science and 183 practitioners' considerations by employing a survey-based research method. By blending the findings of this research with a well-established process capability maturity model standard, International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 330xx, and following a methodological maturity development framework, a theoretically grounded model, entitled as the data science capability maturity model (DSCMM) was developed. Findings It was found that organizations seek a capability/maturity model standard to evaluate and improve their current data science capabilities. To close this research gap, the DSCMM is developed. It consists of six capability maturity levels and twenty-seven processes categorized under five process areas: organization, strategy management, data analytics, data governance and technology management. Originality/value This paper validates the need for a process capability maturity model for the data science domain and develops the DSCMM by integrating literature findings and practitioners' considerations into a well-accepted process capability maturity model standard to continuously assess and improve the maturity of data science capabilities of organizations.