Analyzing process data at varying levels of granularity is important to derive actionable insights and support informed decision-making. Object-Centric Event Data (OCED) enhances process mining by capturing interactions among multiple objects within events, leading to the discovery of more detailed and realistic yet complex process models. The lack of methods to adjust the granularity of the analysis limits users in leveraging the full potential of Object-Centric Process Mining (OCPM). To address this gap, we propose four OnLine Analytical Processing (OLAP) operations: drill-down, roll-up, unfold, and fold, which enable changing the granularity of analysis when working with Object-Centric Event Log (OCEL). These operations allow analysts to seamlessly transition between detailed and aggregated process models, facilitating the discovery of insights that require varying levels of abstraction. We implemented these operations in an open-source Python library, making it available for researchers and practitioners to use in practice. This approach can empower analysts to perform more flexible and comprehensive process exploration, unlocking actionable insights through adaptable granularity adjustments.