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

Abstract

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.

Citation

Gökalp, M.O., Gökalp, E., Kayabay, K., Koçyiğit, A. and Eren, P.E. (2022), "The development of the data science capability maturity model: a survey-based research", Online Information Review, Vol. 46 No. 3, pp. 547-567. https://doi.org/10.1108/OIR-10-2020-0469