Critical success factors for analytics and data mining projects: a harmonized taxonomy and practitioner assessment

Abstract Analytics and data mining projects are of strategic importance, yet understanding the essential success factors determining their outcomes remains fragmented and rarely aligned with practitioner priorities. This study proposes and empirically assesses a cohesive taxonomy of critical success factors of analytics and data mining projects. Five dimensions were developed following a systematic literature review of 32 primary studies and a survey was conducted with practitioners to determine the levels of importance, implementation and gap analysis. The results suggest that strategic, organizational, people, project-level and technical conditions are important factors in the success of analytics and data mining projects as perceived by practitioners. Clear system requirements, data security/privacy, compliance, effective data management, clear business vision and team competency were among the top factors. The structure of the results was refined using exploratory factor analysis of the importance ratings into a three-factor empirical structure. Based on this study, a harmonized and practitioner-informed taxonomy of conditions associated with analytics and data mining project success is proposed. The present study contributes to SDG 9 (Industry, Innovation and Infrastructure) by supporting the implementation of data-driven innovation and analytics initiatives.