Mainframe modernization architecture at a tech vendor with 10,001+ employees
Real User
Top 20
May 18, 2026
I use BMC AMI Data for detecting abnormal batch behavior, finding DB2 deadlocks, intelligent alert correlation, production support automation, mainframe performance monitoring, capacity planning, AI-based anomaly detection, faster root cause analysis, developer productivity, and finding audit compliance and change tracking. I primarily use BMC AMI Data for mainframe performance monitoring. In real time, I gain visibility of CICS regions, MQs, DB2 threads, CPU usage, storage, JES initiators, and long running jobs. Our batch processes used to run longer during the nights, so for better monitoring purposes, we implemented this solution. We also experience peak hours during that time. We depend on the CICS regions and ensure they are up and running. Due to the holiday season, this solution helped us process bulk orders without any issues. Another main use case for me is detecting abnormal batch behaviors. Some jobs were running more than three hours long, some jobs were processing duplicates, and some were creating routes with delays. I compared current execution versus historical patterns using BMC AMI Data and identified unusual CPU and I/O and wait spikes. Additionally, I was able to identify a DB2 object that was causing the slowdown. I was able to find the root cause in minutes instead of hours. I have used BMC AMI Data many times in my company for day-to-day DB2 data management activities to reduce manual DBA efforts, including space monitoring, reorg recommendations, statistics collection, performance analysis, utility scheduling, and copy and recovery management. SQL tuning insights can also be automated proactively and monitored instead of relying on manual analysis. With AI-driven anomaly detection, I was able to analyze historical operational patterns and identify abnormal behavior such as sudden batch time runtime increases, unusual DB2 locking and deadlocks, abnormal CICS transaction spikes, unexpected MQ queue buildups, CPU anomalies, and repeating job failures. This helped my operations and support teams detect these issues earlier, reduce the outage impact, and accelerate root cause analysis before business users are affected.
Database Management Systems efficiently store, retrieve, and manage data for various applications. They provide reliable solutions for structured and unstructured data, ensuring privacy and integrity.Modern Database Management Systems are designed to simplify data management tasks while offering robust functionalities that support both transactional and analytical applications. Their adaptability across industries has been driven by the increasing demand for scalable, secure data solutions....
I use BMC AMI Data for detecting abnormal batch behavior, finding DB2 deadlocks, intelligent alert correlation, production support automation, mainframe performance monitoring, capacity planning, AI-based anomaly detection, faster root cause analysis, developer productivity, and finding audit compliance and change tracking. I primarily use BMC AMI Data for mainframe performance monitoring. In real time, I gain visibility of CICS regions, MQs, DB2 threads, CPU usage, storage, JES initiators, and long running jobs. Our batch processes used to run longer during the nights, so for better monitoring purposes, we implemented this solution. We also experience peak hours during that time. We depend on the CICS regions and ensure they are up and running. Due to the holiday season, this solution helped us process bulk orders without any issues. Another main use case for me is detecting abnormal batch behaviors. Some jobs were running more than three hours long, some jobs were processing duplicates, and some were creating routes with delays. I compared current execution versus historical patterns using BMC AMI Data and identified unusual CPU and I/O and wait spikes. Additionally, I was able to identify a DB2 object that was causing the slowdown. I was able to find the root cause in minutes instead of hours. I have used BMC AMI Data many times in my company for day-to-day DB2 data management activities to reduce manual DBA efforts, including space monitoring, reorg recommendations, statistics collection, performance analysis, utility scheduling, and copy and recovery management. SQL tuning insights can also be automated proactively and monitored instead of relying on manual analysis. With AI-driven anomaly detection, I was able to analyze historical operational patterns and identify abnormal behavior such as sudden batch time runtime increases, unusual DB2 locking and deadlocks, abnormal CICS transaction spikes, unexpected MQ queue buildups, CPU anomalies, and repeating job failures. This helped my operations and support teams detect these issues earlier, reduce the outage impact, and accelerate root cause analysis before business users are affected.