A novel spherical fuzzy-based decision model for assessing data management maturity in governmental institutions

The capability of government institutions to manage data effectively is fundamental to their operational efficiency and innovation potential. Governments face unique challenges, including rapid data generation, evolving regulations, and demands for quality services and transparency. This necessitates a tailored approach to data governance, given the complexities of balancing public interests with data privacy. This study aims to establish a robust framework for evaluating the data management maturity of Government Entities by developing an evaluative metric that reflects their data management maturity. The research approach involved gathering and synthesizing dispersed principles from existing literature into a set of definitive criteria. The criteria were subjectively weighted by an expert panel (SME) to reflect the significance of each criterion in a government setting. For methodology, the study pioneers the hybridization of spherical fuzzy sets (SFSs) built on the criteria importance through intercriteria correlation (CRITIC) and the evaluation based on distance from average solution (EDAS) model. The criteria weighting was methodically calculated using the CRITIC method, and the subsequent evaluation of the alternatives was ascertained through EDAS. This combination of methodologies effectively reduced subjective bias, yielding a more reliable foundation for the rankings. A sensitivity analysis was conducted to confirm the robustness of the presented methodology when subjected to variations. To verify the validity of the developed method, we compared the SF- CRITIC and SF-EDAS approach with the SF-AHP and SF-EDAS, SF-CRITIC and SF-TOPSIS, the SF-CRITIC and SF-WPM, and the SF-CRITIC and SF-MARCOS. The results showcased a spectrum of maturity levels across the evaluated entities, pinpointing both commendable proficiencies and key areas for growth. This research presents a strategic asset for government bodies, aiding in the targeted enhancement of their data management systems. The broader implications of our findings serve as a strategic compass for governmental organizations, steering them toward achieving a higher echelon of data management sophistication.