A conceptual framework of barriers to data science implementation: a practitioners' guideline
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.
Keywords
Acknowledgements
The authors would like to thank Dr Biplab Bhattacharjee, Jindal Global Business School, O.P. Jindal Global University, Sonipat, India, for connecting the authors with some industry experts and convincing them to be part of this study. The authors also would like to thank Dr Abhishek Behl, Management Development Institute, Gurgaon, India, and Dr PRC GOPAL, National Institute of Technology Warangal, Telangana, India, for their support and help with clarity on ISM and MICMAC techniques used in this study. The authors would like to thank the editor and the anonymous reviewers of this article for their excellent suggestions and recommendations. Also, the authors are very grateful to all 48 participants for their valuable time and for sharing their viewpoints.
Citation
Reddy, R.C., Mishra, D., Goyal, D.P. and Rana, N.P. (2023), "A conceptual framework of barriers to data science implementation: a practitioners' guideline", Benchmarking: An International Journal, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/BIJ-03-2023-0160
Publisher
:Emerald Publishing Limited
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