Analysis of Statistical Learning Integration in Data-Driven Organizational Decision-Making

Dwi Wijaya; Rika Maulana; Yusuf Kurniawan

Authors

Dwi Wijaya Universitas Negeri Jakarta Author
Rika Maulana Universitas Negeri Jakarta Author
Yusuf Kurniawan Universitas Pendidikan Indonesia Author

Keywords:

Statistical learning, Data-driven decision-making, Organizational decision-making, Data literacy, Analytical culture

Abstract

This study aims to analyze the integration of statistical learning in data-driven organizational decision-making by focusing on how organizational actors interpret, trust, and apply analytical outputs in real decision contexts. The study addresses the growing use of statistical models, dashboards, and predictive analytics in organizations, while recognizing that data-driven decisions depend not only on technical accuracy but also on human interpretation, organizational culture, and collaborative practice. A qualitative case study approach was used to explore this phenomenon in an organization that applies data analytics, business intelligence, or statistical learning in strategic and operational decisions. Data were collected through semi-structured interviews, limited participant observation, and document analysis involving managers, data analysts, business intelligence staff, operational employees, and decision makers. Thematic analysis identified four main themes: statistical learning as a tool for reducing uncertainty, trust and interpretability as conditions for adoption, tension between analytical outputs and managerial experience, and organizational culture as a driver or barrier to data-driven decisions. The findings show that statistical learning does not replace human judgment, but supports reflective decision-making when analytical outputs are explained, discussed, and aligned with contextual knowledge. This study contributes to data-driven decision-making literature by positioning statistical learning as a socio-technical and interpretive practice. The findings imply that organizations need stronger data literacy, transparent model governance, cross-functional collaboration, and ethical decision procedures. Future research should compare multiple organizational contexts and examine how explainable artificial intelligence shapes trust in statistical learning.

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