This thesis explores how SMEs (small and medium enterprises) can leverage data collection and applied data analysis to increase their value creation and therefore their profitability. In a world where technology and data have an ever-growing role in our lives, we have to realize that not only big companies now have the possibility to implement data-based business strategies. This study will investigate individual case studies of applied data analysis use in SMEs. The purpose of this research is to look at how this technology can develop the ability to predict customer behavior and discover relations between products or services. It further looks at how data analysis can enable optimized resource allocation by identifying areas of inefficiency. The paper will employ a mixed-methods approach, involving quantitative analysis through Python data processing and analytics with qualitative deductions of said data processing results. Further content analysis will reveal whether such a data-based approach to value creation is indeed efficient and effective and will identify potential success factors. These findings should then provide both practical and theoretical guidelines to small and medium business owners on implementing data analysis into their business strategy development.
Baractari et al. (Tue,) studied this question.