Barriers to the Adoption of Data Analytics in Small Businesses

This research explores the barriers to the adoption of data analytics in small businesses, comparing urban and rural settings. Small businesses face numerous challenges in adopting data analytics, including awareness and perception issues, organizational and technological barriers, human resource constraints, environmental factors, and external pressures. Using a mixed-methods approach, the study combines a cross-sectional survey with both qualitative and quantitative data analysis. Descriptive design, exploratory factor analysis (EFA), and multiple regression analysis are used to examine the factors influencing adoption. The research also incorporates machine learning (ML) applications, such as classification and clustering techniques, to identify patterns and categorize businesses based on their readiness and barriers to adopting data analytics. Findings suggest that financial constraints, lack of infrastructure, and employee skill gaps are significant barriers, with notable differences between urban and rural businesses.