Mayukh Gosh

Domain-Driven Analytics: exploring the role of domain Intelligence in predictive and prescriptive analytics

This project explores when and how domain intelligence enhances analytical capabilities in complex, data-constrained environments. Domain intelligence may be explicit, such as formalised knowledge and operational constraints, or implicit, as seen in practitioners’ experience and decisions. However, domain intelligence remains inconsistently defined, incorporated, and evaluated in analytics, with most studies focusing on explicit forms. This project addresses these gaps by clarifying domain-related concepts, evaluating approaches to incorporating domain intelligence, and developing a framework to guide its selection and assessment.

The research investigates these questions through studies in humanitarian, healthcare, and logistics contexts. In partnership with the World Food Program (WFP), it examines how domain intelligence can improve predictions of regions at risk of child wasting. The Amazon last-mile routing study integrates tacit intelligence from drivers’ historical routes into an optimisation model. In contrast, the air-cargo congestion study applies domain-informed simulation to assess operational interventions. In collaboration with AMREF Health Africa, the thesis combines healthcare-demand estimation, routing optimisation, and operational constraints in a decision-support system for mobile-clinic deployment in Kenya. These studies demonstrate how various forms of domain intelligence can be incorporated into analytics based on the problem context.

Building on these insights, the project develops a methodological framework for selecting and assessing domain intelligence. It clarifies the role of domain intelligence in analytical modeling and examines how its incorporation affects model performance and solution relevance. For practitioners, it contributes by developing and evaluating analytics-based tools that translate domain intelligence into actionable solutions.