Compare Canonical Landscape vs. IntelliMagic Vision for z/OS

Landscape effectively streamlines the management and oversight of extensive and expanding networks that include desktops, servers, and cloud infrastructures, making it a highly economical choice. It significantly alleviates the daily management burdens on your team, allowing for the smooth administration of various deployments across desktop, server, and cloud environments. One instance can efficiently manage up to 40,000 devices, providing an impressive scalability option. Users can create tailored profiles to oversee diverse classes of machines, which simplifies the processes of software installation, updates, and removals. Additionally, policies can be established for automated updates and security patches, ensuring that systems remain secure with minimal manual intervention. The ability to list all active processes on a device and remotely terminate any malicious ones enhances operational security. Users can also visualize trends in temperature, disk space, memory usage, and overall system load through customizable graphs. This flexibility is further complemented by the feature to design graphs using specific metrics that matter most to your operations. Regulatory compliance becomes much easier through custom reporting, which includes automated audit logging and compliance documentation. Furthermore, you can efficiently monitor and retrieve comprehensive software package details for all registered machines, facilitating better oversight and management across the network.

Traditional RMF/SMF data analysis often relies on static reports that are not only costly and challenging to maintain but also take years to master, leading to the creation of knowledge silos within teams. In contrast, IntelliMagic Vision offers customizable reports paired with an exceptional level of built-in z/OS intelligence, enabling analysts to uncover hidden risks, optimize workloads for substantial cost reductions, enhance the contributions of experts, and speed up the training and efficiency of new employees. By leveraging AI-driven analytics, organizations can optimize performance management of z/OS systems, allowing for proactive monitoring and management of the environment, which helps avert disruptions, minimize expenses, and maintain the reliability and availability synonymous with mainframes. Additionally, by providing deep insights into cost drivers such as suboptimal CPU usage, configuration challenges, workload imbalances across hardware, and potential consolidation opportunities, organizations can achieve significant cost savings. This comprehensive approach not only enhances operational efficiency but also fosters a more collaborative and knowledgeable workforce.