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.

中文翻译：

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数据科学能力成熟度模型的发展：基于调查的研究

目的

本文的目的是调查数据科学实践的社会和技术驱动因素，并开发一个标准模型，通过提供数据科学能力/成熟度水平评估、得出差距分析和创建一个全面的改进路线图来帮助组织进行数字化转型以标准化的方式。

设计/方法/方法

本文采用基于调查的研究方法，系统地回顾和综合了现有与数据科学相关的文献和 183 位从业者的思考。通过将这项研究的结果与成熟的过程能力成熟度模型标准国际标准化组织/国际电工委员会 (ISO/IEC) 330xx 相结合，并遵循方法成熟度开发框架，一个名为数据的基于理论的模型开发了科学能力成熟度模型（DSCMM）。

发现

结果发现，组织寻求能力/成熟度模型标准来评估和改进其当前的数据科学能力。为了弥补这一研究空白，开发了 DSCMM。它由六个能力成熟度级别和 27 个流程组成，分为五个流程领域：组织、战略管理、数据分析、数据治理和技术管理。

原创性/价值

本文验证了数据科学领域对过程能力成熟度模型的需求，并通过将文献发现和从业者的考虑整合到一个广为接受的过程能力成熟度模型标准中来开发 DSCMM，以持续评估和提高数据科学能力的成熟度。组织。