Human Centric Analytics: Collaborative Design for Mathematics, AI, and Data-Driven Insights

Abstract This paper introduces Human Centric Analytics (HCA) as a design paradigm for integrating mathematical models, data, and human judgement into management decision-making. HCA addresses situations in which analytics must be understood, adapted, and used within complex organisational settings, rather than treated as standalone technical artefacts. While Human-Centred Design (HCD) offers useful principles for usability and participation, HCA addresses a different design problem. It focuses on the practical design and adaptation of analytical artefacts, techniques, and processes, including the negotiation of model assumptions and appropriate levels of granularity. Drawing on a longitudinal case study in a pharmaceutical supply chain, the paper examines a series of analytics interventions involving forecasting, simulation, statistical analysis, visualisation, and data blending. Analysis of both successful and unsuccessful interventions shows how mathematical tools gained traction when they were developed through iterative engagement with users’ work, expertise, and constraints. The paper presents HCA as an empirically derived and theoretically grounded design paradigm, supported by an umbrella framework organised around four recurring design activities: Structuring Perceptions, Structuring Empirical Data, Overcoming Resistance, and Evolving Solutions. The framework supports flexible combinations of tools and techniques, enabling analytics to become technically grounded, contextually meaningful, and integrated into organisational practice. As data-driven systems and AI become increasingly embedded in management, HCA offers an approach for designing analytics that augment human practice rather than bypass it.