Data analysis constitutes a critical component of organizational decision-making, involving the interpretation of complex datasets to generate meaningful and actionable insights. A central challenge in this process lies in effectively integrating subjective judgment with analytical rigor. This balance is particularly important in the field of Competitive Intelligence, where professionals must operate under conditions of uncertainty, incomplete information, and rapidly changing environments, requiring both analytical precision and contextual adaptability. This study examines how balancing subjective judgment and data-driven analysis in Competitive Intelligence influences decision-making competence and confidence. The findings reveal a moderate positive association between subjective judgment use and data-driven analysis use (r = .45, p < .001), indicating that decision-makers tend to employ both cognitive modes in parallel rather than as alternatives. A strong positive relationship was found between the perceived importance of balancing subjective and analytical approaches and decision-making confidence (r = .70, p < .001), identifying balance as the most influential factor in perceived competence. Training exposure was also positively associated with decision-making confidence (r = .55, p < .001), and regression analysis confirmed that perceived balance (β = .49), training (β = .30), and both subjective (β = .19) and analytical (β = .22) processing significantly predict decision-making confidence (R² = .57). The study demonstrates that effective CI practice depends not only on data collection and management but on the ability to interpret, triangulate, and contextualize information, combining analytical tools with experiential insight. The findings highlight the importance of training programs that explicitly develop the capability to balance subjective and objective inputs, thereby enhancing decision-making confidence and strategic competence.
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Cekuls et al. (2026) studied this question.
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