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

2021-09-01
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.
ONLINE INFORMATION REVIEW

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Citation Formats
M. O. Gökalp, K. Kayabay, A. Koçyiğit, and P. E. Eren, “The development of the data science capability maturity model: a survey-based research,” ONLINE INFORMATION REVIEW, pp. 0–0, 2021, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/93781.