Assessment and Prioritization of Data Analysis Challenges Using Fuzzy Multi-Criteria Decision-Making

Kamal Hossain Gazi; Fariba Azizzadeh; Aditi Biswas; Prodip Bhaduri; Sankar Prasad Mondal

doi:10.67334/cds31202632

Authors

Kamal Hossain Gazi Department of Applied Mathematics, Maulana Abul Kalam Azad University of Technology, West Bengal, Haringhata, West Bengal, India Author https://orcid.org/0000-0001-7179-984X
Fariba Azizzadeh Department of Management, Islamic Azad University, Piranshahr (Urmia), Iran Author https://orcid.org/0000-0002-1288-1605
Aditi Biswas Department of Applied Mathematics, Maulana Abul Kalam Azad University of Technology, West Bengal, Haringhata, West Bengal, India Author https://orcid.org/0009-0001-1972-6536
Prodip Bhaduri Department of Applied Mathematics, Maulana Abul Kalam Azad University of Technology, West Bengal, Haringhata, West Bengal, India Author https://orcid.org/0009-0003-5455-8202
Sankar Prasad Mondal Department of Applied Mathematics, Maulana Abul Kalam Azad University of Technology, West Bengal, Haringhata, West Bengal, India Author https://orcid.org/0000-0003-4690-2598

DOI:

https://doi.org/10.67334/cds31202632

Keywords:

Data analysis, Challenges of adopting data analysis, Trapezoidal intuitionistic fuzzy number (TrIFN), Entropy weighted method, VIKOR

Abstract

Data analysis using different techniques has become increasingly important across a wide range of application domains. However, several challenges arise when datasets are collected, processed, and analysed. This chapter aims to identify and evaluate the most significant challenges associated with data analysis. The proposed methodology is based on multi-criteria decision-making (MCDM). The identified challenges are classified into ten categories: Data Quality Issues, Data Integration, Data Volume, Data Variety, Data Privacy and Security, Bias and Misinterpretation, Skill Gap, Real-Time Analysis, Cost and Infrastructure, and Communicating Results. Two MCDM methods are employed for the numerical evaluation. The data are collected in the form of trapezoidal intuitionistic fuzzy numbers (TrIFNs) to represent the uncertainty and ambiguity inherent in expert assessments. Finally, a comparative analysis is conducted to evaluate the reliability and flexibility of the proposed model.

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