A synergetic intuitionistic fuzzy model combining AHP, entropy, and ELECTRE for data fabric solution selection

Amidst the ongoing digital transformation, enterprises face the challenge of managing ever-expanding volumes of data from multiple sources and diverse structures. Semantic data fabric emerges as a promising solution, offering an innovative approach to integrate data resources from various channels and produce meaningful insights. The selection of an appropriate data fabric solution has become a focal point amidst burgeoning data lakes and silos, garnering international attention. This research aims to precisely evaluate potential data fabric solutions using an innovative synergetic intuitionistic fuzzy evaluation model. We propose a hybrid approach, IF-AHP-Entropy-ELECTRE, which integrates the analytic hierarchy process (AHP), entropy, and elimination et choix traduisant la réalité (ELECTRE) techniques within the framework of intuitionistic fuzzy (IF) sets. This model is utilized to a data fabric solution selection (DFSS) issue for an appliance company, identifying the optimal solution based on its superior performance in foundational technology, real-time analytics, and customizable features. The effectiveness and adaptability of this approach stem from a novel hierarchical evaluative criteria system encompassing technology, capability, cost, and security. The criteria weights, derived from IF-AHP-Entropy, reflect both subjective and objective judgments of decision-makers, while the ranking generated by IF-ELECTRE employs a piecewise scoring function and a unique distance measure, factoring in optimistic perspectives and cross-information. Through sensitivity and comparative analyses, our approach demonstrates enhanced robustness, precision, and adaptability in dynamic DFSS contexts when compared to traditional multicriteria decision-making methods, such as IF-WSM, IF-TOPSIS, and IF-ELECTRE. Specifically, our model provides a decision support system that combines extensive functionality with a user-friendly design, making it highly effective for DFSS challenges. This approach not only establishes a solid foundation for data integration in data management but also enhances business competitiveness and supports sustained growth in the digital economy.