Publication Details
Abstract
The data science sector has experienced a radical change in the last 10 years, shifting from less advanced and less dynamic traditional reporting to more robust adaptive predictive systems, which can aid decisions in real time. The present paper focuses on paradigm shifts defining this evolution, tracing the path of descriptive analytics to diagnostic, predictive, and prescriptive models (reaching adaptive predictive modeling models, which are driven by machine learning and artificial intelligence). The study deals with the theoretical foundations of each stage, the facilitating technologies that have led to the changes between stages, and the organizational and technical challenges that are associated with these changes. Based on the extensive literature review of recent literature, the paper brings together evidence about the latest advances in healthcare, industrial systems, business intelligence, and social sciences to demonstrate how adaptive modeling is changing what data-driven decision-making is capable of. The results imply that the development towards adaptive systems cannot be uniform and progression across sectors, and that the only way to achieve success in adoption is to ensure that technical infrastructure is coordinated with strategic organizational intent. The paper provides a formal analytical framework on how the given industries are at this point of this evolutionary continuum and the point where the greatest potential improvements can be realized.