Data Quality in Small and Medium Enterprises: Barriers, Opportunities, and Best Practices

Abstract

Small and medium enterprises face unique data quality challenges that distinguish them from larger organizations. Limited resources, technical capabilities, and specialized expertise constrain the ability of SMEs to implement comprehensive data quality management practices. Yet data quality is increasingly important for SME competitiveness, enabling customer understanding, operational efficiency, and strategic decision-making. This review examines data quality in SMEs, analyzing the barriers that limit quality management and the opportunities that quality improvement presents. We draw on evidence from studies of SMEs across multiple sectors and countries, including research on CRM adoption, business intelligence, and operational management. We identify the key barriers to data quality in SMEs, including limited resources, technical capacity, and organizational maturity. We examine the consequences of poor data quality for SME performance and competitiveness. We analyze approaches that have been effective in SME contexts, including governance frameworks adapted to resource constraints, practical tools and methodologies, and capacity building approaches. We propose a practical framework for SME data quality management that addresses the specific constraints and opportunities of these organizations.