Purpose: Higher education institutions increasingly emphasize data analytics education, yet curricula based solely on competency-based frameworks may overlook industry's process-driven approach. This study examines the process deficit in data analytics education and its impact on workforce readiness. It explores strategies to better align curricula with structured workflows and applied problem-solving in analytics roles. Design/methodology/approach: This study applies comparative semantic analysis to evaluate the alignment between ACM-DS (academic) and CRISP-DM (industry), two leading data-focused frameworks. Using natural language processing (NLP) and density-based clustering, it examines conceptual differences to assess how well curricula reflect structured workflows and applied problem-solving in analytics practice. Findings: The analysis reveals that ACM-DS emphasizes technical competencies but does not explicitly integrate structured workflows, creating gaps in applied problem-solving. In contrast, CRISP-DM embeds analytics within iterative, project-based workflows that better reflect industry practice. To address these gaps, this study proposes integrating process-oriented learning strategies, including Work-Integrated Learning, Process-Based Learning and applied capstone experiences, to enhance workforce readiness. Practical implications: Findings provide actionable insights for curriculum designers and higher education policymakers, supporting efforts to integrate work-integrated learning, employer partnerships, and structured, process-driven instruction into data analytics education to enhance workforce readiness. Originality/value: This study provides an empirical comparison of ACM-DS and CRISP-DM, demonstrating that academic curricula may lack structured, process-oriented learning. It contributes to curriculum design by identifying strategies to enhance applied problem-solving and align educational outcomes with industry workflows, ensuring graduates are better prepared for analytics roles.