Abstract
Big data analytics is a promising new technique. Yet, its implementation and value extraction roadmaps are unknown. This paper explores the interrelations and interdependencies of BDA enablers in the banking and financial services sector.
Design/Methodology/Approach: Delphi review, interpretive structural modelling, and fuzzy MICMAC method are used to identify enabler interactions and BDA implementation success. Our integrated strategy uses experts' domain experience to get a fresh insight into enabling causal linkages, language analysis of varying mutual affects, and two creative methods for displaying outcomes.
Findings: Our findings emphasise the importance of enabling aspects including skilled and technical labour, financial help, infrastructure preparation, and selecting the right major data solutions. These forces drive many hierarchical enablers. The results provide dependable, straightforward, and robust insights into BDA implementation in banking and monetary service as a whole programme, showing possible affects of all interconnected vital parts.
Originality/Value: This paper examines the main BDA enablers in banking and financial services. Operating and dependency degrees indicate element relationships. This study shows managers how to apply BDA well.
Keyword
Banking & finance industry, Optimum decision for business promotion, descriptive, predictive, and prescriptive analytics, Business analytics, Competitive advantage for banks.
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