Learn How to Prevent Significant Business Disruptions with Terma Software’s Latest eBook

February 6, 2019 12:14 pm Published by

Terma Software recently developed an eBook with IBM Systems on how to predict major problems and optimize complex mainframe workloads. Fortune 1,000 companies, especially in the financial services industry, struggle to manage complex, mission-critical workloads. They often have multiple workload automation or job scheduling systems from different vendors running sometimes more than 1 million jobs in a single day. These multi-scheduler systems are often integrated and highly dependent on each other, yet there is a lack of clear visibility across jobstreams and no predictive capabilities to identify potential problems before they occur. Without the proper controls and processes to prevent workload-related delays or stoppages, companies are at risk of jeopardizing business processes and, ultimately, their bottom lines.

Fortunately, Terma Software offers a workload analytics solution that leverages large volumes of workload automation data combined with Artificial Intelligence and Machine Learning to provide the visualization, adaptability, and intelligence to successfully manage complex workloads. Terma’s solution has provided a significant ROI for companies with complicated workloads, reducing operating costs by 50 percent in some cases. For example, a large global financial company realized a quick ROI with TermaANALYTICS by identifying where workloads were naturally degrading over time and addressing them before they affected SLAs. Terma’s AIOps platform enabled another large financial firm to avoid SLA violations and fines and see an immediate ROI by reducing batch inefficiencies and reducing the time needed to fix batch issues.

In the eBook, which you can sign up to access here, Terma Software:

  • Details how to unravel workload problems before they negatively impact business
  • Provides expert advice for automation platform migration and consolidation
  • Explains the power of interpreting workload data for improving the bottom line
  • Describes the importance of intelligent visualization through a single-pane-of-glass
  • Suggests ways companies can better manage complex workloads and reduce risk

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