Implementation of Data Mining Techniques in the Private Sector of the Republic of Moldova: Case Studies and Competitive Advantages

Abstract

This paper examines the potential of Data Mining techniques to generate measurable competitive advantages in the private sector of the Republic of Moldova, drawing on three applied case studies conducted by the authors. The main methodological frameworks — CRISP-DM, KDD, SEMMA, TDSP, Scrum and Kanban — are compared, with an emphasis on the prevalence of CRISP-DM. The case studies include: a scoring model for debt collection (−27% ineffective actions), market value estimation for automobiles (R² = 0.96) and apartments (R² = 0.90) at the VIA SCOPE Bureau, and an energy loss forecasting system at REDNord (R² = 0.98; MAPE = 2.23%). The findings show that Data Mining projects structured according to CRISP-DM can generate measurable business value, while the case studies suggest that organisational and analytical capabilities may represent important barriers to wider adoption. CZU: [004.6:005]:334.722(478); JEL: C38, C53, M15