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The development of the data science capability maturity model: a survey-based research.
- Source :
-
Online Information Review . 2022, Vol. 46 Issue 3, p547-567. 21p. - Publication Year :
- 2022
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14684527
- Volume :
- 46
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Online Information Review
- Publication Type :
- Academic Journal
- Accession number :
- 157224779
- Full Text :
- https://doi.org/10.1108/OIR-10-2020-0469