Entrepreneurship is a behavior that influences economic development. There are many effective individual attitudes behind this behavior. According to Global Entrepreneurship Monitor (GEM) reports, the level of individual factors in low-income countries (factor-driven economies) is much higher than high-income countries (innovation-driven economies) which seems to be a contradiction. By applying a method that is developed by statisticians, named the “Dark Data” approach, we attempted to discover an ignored dark data in the GEM’s big dataset. Regarding the wide-ranging experiences of researchers in this field, entrepreneurial motivation was identified as the missing variable that does not be used in the computation of individual factors. Finally, with adding entrepreneurial motivation to the equation of individual factors, as one of the sub-dimensions of individual factors, the result of the relationship between the GDP per capita and individual factors was improved. This study presents a comprehensive method to improve the structure of the Entrepreneurial Motivation Index. According to this study, entrepreneurial motivation can be considered as a specific type of dark data in the GEM dataset (as big data) that ignoring this index in the entrepreneurship studies, knowingly or unknowingly, will impose grave consequences on the results of researches. Ideally, what this study tries to unfold is the importance of dark data that is hidden in many big datasets. Hidden information includes the strengths and weaknesses of a company that needs to be found by applying the methods developed in big data science.