A conceptual framework of barriers to data science implementation: a practitioners' guideline

IF 4.5 Q1 MANAGEMENT
Rajesh Chidananda Reddy, Debasisha Mishra, D.P. Goyal, Nripendra P. Rana
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引用次数: 0

Abstract

Purpose The study explores the potential barriers to data science (DS) implementation in organizations and identifies the key barriers. The identified barriers were explored for their interconnectedness and characteristics. This study aims to help organizations formulate apt DS strategies by providing a close-to-reality DS implementation framework of barriers, in conjunction with extant literature and practitioners' viewpoints. Design/methodology/approach The authors synthesized 100 distinct barriers through systematic literature review (SLR) under the individual, organizational and governmental taxonomies. In discussions with 48 industry experts through semi-structured interviews, 14 key barriers were identified. The selected barriers were explored for their pair-wise relationships using interpretive structural modeling (ISM) and fuzzy Matriced’ Impacts Croise's Multiplication Appliquée a UN Classement (MICMAC) analyses in formulating the hierarchical framework. Findings The lack of awareness and data-related challenges are identified as the most prominent barriers, followed by non-alignment with organizational strategy, lack of competency with vendors and premature governmental arrangements, and classified as independent variables. The non-commitment of top-management team (TMT), significant investment costs, lack of swiftness in change management and a low tolerance for complexity and initial failures are recognized as the linkage variables. Employee reluctance, mid-level managerial resistance, a dearth of adequate skills and knowledge and working in silos depend on the rest of the identified barriers. The perceived threat to society is classified as the autonomous variable. Originality/value The study augments theoretical understanding from the literature with the practical viewpoints of industry experts in enhancing the knowledge of the DS ecosystem. The research offers organizations a generic framework to combat hindrances to DS initiatives strategically.
数据科学实施障碍的概念框架:实践者指南
本研究探讨了组织实施数据科学(DS)的潜在障碍,并确定了主要障碍。对已确定的障碍的相互联系和特征进行了探讨。本研究旨在结合现有文献和从业者的观点,通过提供一个接近现实的DS实施障碍框架,帮助组织制定合适的DS战略。作者通过系统文献综述(SLR),在个人、组织和政府的分类下,综合了100种不同的障碍。通过半结构化访谈与48位行业专家进行讨论,确定了14个主要障碍。在制定层次框架时,使用解释结构建模(ISM)和模糊矩阵影响(MICMAC)分析来探索所选障碍的成对关系。缺乏意识和数据相关的挑战被确定为最突出的障碍,其次是与组织战略不一致,缺乏与供应商的能力和过早的政府安排,并被归类为独立变量。高层管理团队(TMT)的不承诺、重大的投资成本、变更管理缺乏快速性以及对复杂性和初始失败的低容忍度被认为是联系变量。员工的不情愿、中层管理人员的抵制、缺乏足够的技能和知识以及在孤岛中工作,取决于其他已确定的障碍。感知到的对社会的威胁被归类为自主变量。原创性/价值本研究以业界专家的实践观点,扩充了文献的理论认识,增进了对DS生态系统的认识。该研究为组织提供了一个通用框架,以从战略上打击DS计划的障碍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
10.40
自引率
16.10%
发文量
154
期刊介绍: Benchmarking is big news for companies committed to total quality programmes. Its enthusiastic reception by many prominent business figures has created high levels of interest in a technique which promises big rewards for co-operating partners. Yet, like total quality itself, it must be understood in its proper context, and implemented single mindedly if it is to be effective - this journal helps companies to decide if benchmarking is right for them, and shows them how to go about it successfully.
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