The framework of parametric and nonparametric operational data analytics

Abstract

This paper introduces the general philosophy of the Operational Data Analytics (ODA) framework for data-based decision modeling. The fundamental development of this framework lies in establishing the direct mapping from data to decision by identifying the appropriate class of operational statistics. The efficient decision making relies on a careful balance between data integration and decision validation. Through a canonical decision making problem under uncertainty, we show that the existing approaches (including statistical estimation and then optimization, retrospective optimization, sample average approximation, regularization, robust optimization, and robust satisficing) can all be unified through the lens of the ODA formulation. To make the key concepts accessible, we demonstrate, using a simple running example, how some of the existing approaches may become equivalent under the ODA framework, and how the ODA solution can improve the decision efficiency, especially in the small sample regime.