Allocating resources in a distributed environment is a fundamental challenge. In this paper, we analyze the scheduling and placement of virtual machines (VMs) in the cloud platform of SAP, the world's largest enterprise resource planning software vendor. Based on data from roughly 1,800 hypervi-sors and 48,000 VMs within a 30-day observation period, we highlight potential improvements for workload management. The data was measured through observability tooling that tracks resource usage and performance metrics across the entire infrastructure. In contrast to existing datasets, ours uniquely offers fine-grained time-series telemetry data of fully virtualized enterprise-level workloads, including long-running and memory-intensive SAP S/4HANA and diverse, general-purpose workloads. Our key findings include several sub-optimal scheduling situations, such as CPU resource contention exceeding 40%, peak CPU ready times of up to around 220 seconds, significantly imbalanced compute hosts with a maximum intra-building block host CPU utilization spread of up to 99%, overprovisioned CPU resources with over 80% of VMs using less than 70% CPU, and underutilized memory with similar utilization levels. Using these findings, we derive requirements for the design and implementation of novel placement and scheduling algorithms and provide guidance to optimize resource allocations. The full dataset used in this study is made publicly available. We hope this will enable future research to conduct a data-driven evaluation of potential scheduling solutions for large-scale, productive cloud infrastructures.