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Abstract
Data marketplace implementation leverages customer data through advanced AI/ML tools to create personalized marketing strategies, integrating diverse data sources for audience segmentation, campaign optimization, and continuous performance measurement to enhance engagement and ROI.This study addresses critical gaps in understanding how organizations overcome data marketplace implementation barriers, including unrealistic vendor expectations, post-implementation management challenges, and internal data utilization inefficiencies, providing strategic insights for improved marketing performance.Primary data from 624 respondents across IT/Software, Manufacturing, Healthcare, Finance/Banking, and Retail sectors was collected using structured questionnaires with five-point Likert scales. The study examined eight dependent variables—development cost suitability, real-time support, market performance improvement, technological uncertainty, data quality, portability, security, and accessibility across various organizational roles, experience levels, firm sizes, and data marketplace models including centralized, decentralized block Chain-based, federated, subscription-based, and pay-per-use systems.Statistical analysis revealed in the reliability analysis, the scale items were found to have acceptable internal consistency (Cronbach's alpha = 0.76), moderate mean perceptions (1.88-2.67), and significant positive correlations among variables, particularly between data quality and market performance improvement (r = 0.492), with all variables achieving statistical significance (p < 0.001).Organizations require comprehensive strategies addressing interconnected factors cost, security, quality, accessibility simultaneously. Decentralized block Chain-based and centralized models dominate adoption. Future research should examine longitudinal patterns and sector-specific success factors for optimizing data marketplace implementation and maximizing ROI.
