Data Science in Managerial Decision-Making Systems: Methodology, Practice, and Transformation

Abstract

This article provides a comprehensive analysis of the integration of Data Science into contemporary managerial decision-making systems. It outlines the conceptual and methodological foundations of data-driven management and presents applied examples from various sectors, including business, finance, public administration, logistics, education, and human resources. The authors emphasize that Data Science is no longer a purely technical domain – it constitutes a strategic asset and a transformative force capable of reshaping how organizations perceive, analyze, and act upon information.

The study presents a comparative analysis of key methodologies that define the analytical cycle: CRISP-DM (Cross Industry Standard Process for Data Mining), KDD (Knowledge Discovery in Databases), SEMMA (Sample, Explore, Modify, Model, Assess), TDSP (Team Data Science Process), and ISO/IEC 20546:2019 (Big Data – Overview and Vocabulary). Each model is assessed in terms of its structure, adaptability, sectoral relevance, and potential for implementation within dynamic decision-making environments. The authors underscore the flexibility of CRISP-DM, the technical rigor of SEMMA, the teamwork orientation of TDSP, and the conceptual clarity of ISO standards as essential components of modern data governance.

A core focus of the article is the transformative potential of analytics across three interrelated levels: strategic (where data support rational decision-making), operational (where automation enhances efficiency), and cultural (where decentralization fosters participatory governance and data literacy). These levels interact to produce a new managerial paradigm in which data function as assets, analytics as instruments of change, and emerging roles – such as Chief Data & Analytics Officers – serve as agents of organizational evolution.

In addition to methodological synthesis, the article explores critical challenges related to ethical data use, algorithmic bias, transparency, and the interpretability of machine learning models. The transition to a data-driven culture requires not only digital infrastructure and technical expertise but also institutional readiness, responsible leadership, and the development of analytical competence across managerial teams. The authors advocate for investments in statistical education, open data, regulatory standards, and cross-sectoral collaboration as prerequisites for sustainable analytics adoption.

Ultimately, the article concludes that Data Science is not simply a tool for optimization but a foundational element of a redefined decision-making logic—one that enables prediction, proactivity, and resilience in a complex, uncertain, and digitalized world.