In today’s data-driven landscape, businesses increasingly rely on both data science and process science to enhance operational efficiency and strategic decision-making. However, a significant gap exists between these two disciplines, hindering the realization of their full potential synergy. This paper proposes a novel approach to bridge this gap through the integration of business analytics and process analytics within a Data Warehouse (DW) system. We present a meta-model, the Business and Process Analytics Data Warehouse Meta-Model (B&PADWM), designed to optimize DWs for business and process analysis. This meta-model enables organizations to gain insights into both business performance and process efficiency simultaneously. By leveraging Object-Centric Event Logs (OCEL) derived from Process Mining, our approach facilitates a detailed understanding of business processes, allowing for the identification of bottlenecks, inefficiencies, and opportunities for improvement. Drawing from previous research in business process management and data analysis, we demonstrate the efficacy of our approach through a demonstration case study focused on order management processes.