‎enhancing big data governance framework implementation using novel fuzzy frank operators‎: ‎an application to madm process

In today's data-driven landscape‎, ‎to ensure continuous survival and betterment‎, ‎the implementation of a robust big data governance framework (bdgf) is imperative for organizations to effectively manage and harness the potential of their vast data resources‎. ‎the bdgf serves no purpose when implemented in a random manner‎. ‎this article delves into the complex decision-making challenges that emerge in the context of implementation of the bdgf under uncertain conditions‎. ‎specifically‎, ‎we aim to analyze and evaluate the bdgf performance using the multi-attribute decision-making (madm) techniques aiming to address the intricacies of big data governance uncertainties‎. ‎to achieve our objectives‎, ‎we explore the application of frank operators within the framework of complex picture fuzzy (cpf) sets (cpfs)‎. ‎we introduce complex picture fuzzy frank weighted averaging (cpffwa) and complex picture fuzzy frank ordered weighted averaging (cpffowa) operators to enable more accurate implementation of the bdgf‎. ‎additionally‎, ‎we rigorously examine the reliability of these newly proposed fuzzy frank (ff) operators (ffaos)‎, ‎taking into consideration essential properties such as idempotency‎, ‎monotonicity‎, ‎and boundedness‎. ‎to illustrate the practical applicability of our approach‎, ‎we present a case study that highlights the decision-making challenges encountered in the implementation of the bdgf‎. ‎subsequently‎, ‎we conduct a comprehensive numerical example to assess various bdgf implementation options using the madm technique based on complex picture fuzzy frank aggregation (cpffa) operators‎. ‎furthermore‎, ‎we perform a comprehensive comparative assessment of our proposed methodology‎, ‎emphasizing the significance of the novel insights and results derived‎. ‎in conclusion‎, ‎this research article offers a unique and innovative perspective on decision-making within the realm of the bdgf‎. ‎by employing the cpffwa and the cpffowa operators‎, ‎organizations can make well-informed decisions to optimize their bdgf implementations‎, ‎mitigate uncertainties‎, ‎and harness the full potential of their data assets in an ever-evolving data landscape‎. ‎this work contributes to the advancement of decision support systems for big data governance (bdg)‎, ‎providing valuable insights for practitioners and scholars alike‎.