Our main use case for BMC AMI Data is to manage and optimize our database environment, mainly DB2. We use it for database performance tracking, data management, data monitoring, and automating some parts of the database.A very recent example of how we used BMC AMI Data for database performance tracking involved noticing that our database workload was taking longer than normal during a very busy period. We used BMC AMI Data to examine the database activity and identify where the performance issue was coming from. This helped us find the problem faster than before and make the required changes before it became a bigger issue.
My primary use case for BMC AMI Data is application performance monitoring and troubleshooting. I use it to analyze transaction behavior, identify performance bottlenecks, monitor system health, and quickly diagnose issues before they impact end users. It also helps me with capacity planning, trend analysis, and generating operational reports, giving better visibility into overall mainframe performance and application efficiency. One instance that stands out was when one of our critical batch jobs started taking much longer than usual to complete after a deployment. Using BMC AMI Data, I compared the performance metrics with previous runs and noticed a significant increase in CPU utilization and DB2 wait times for a particular transaction. That helped me narrow the issue down to an inefficient SQL query introduced in the latest release. After the deployment team optimized the query and added the appropriate job execution, time dropped back to normal. Without the insight from BMC AMI Data, it would have taken much longer to isolate the root cause.
BMC AMI Data is primarily used to increase data resiliency and availability, with the main focus on automating backup and recovery for business continuity. We had multiple databases across multiple servers and critical backups required for all databases. This has been automated so that backups are taken automatically, and whenever restoration is required, the automated process handles that as well. During DR and BCP scenarios, the restoration is used for testing. BMC AMI Data enabled automation of everything required from day one. No manual backups were taken, and as soon as new servers and databases were spun up, automation was set up from day one.
My main use case for BMC AMI Data is to monitor the data and the workflow for the compliance and reporting part, auditing and all. For compliance or auditing, BMC AMI Data diagnoses and resolves the problems, summarizes, and gives me a proper report. Since my industry is under banking and finance, the reporting should be in a proper manner. It manages the data, which actually protects and optimizes it and provides me the output according to it. I do not have anything else to add about my main use case or how BMC AMI Data fits into my daily workflow.
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.
BMC AMI Data enhances mainframe environments by delivering robust data management tools, optimizing performance, and ensuring data integrity.
BMC AMI Data provides comprehensive solutions tailored for the mainframe, focusing on performance optimization and risk reduction. It offers data management capabilities that streamline operations and improve efficiency for enterprise systems, supporting mission-critical applications.
What are the essential features of BMC AMI Data?
Data Integrity:...
Our main use case for BMC AMI Data is to manage and optimize our database environment, mainly DB2. We use it for database performance tracking, data management, data monitoring, and automating some parts of the database.A very recent example of how we used BMC AMI Data for database performance tracking involved noticing that our database workload was taking longer than normal during a very busy period. We used BMC AMI Data to examine the database activity and identify where the performance issue was coming from. This helped us find the problem faster than before and make the required changes before it became a bigger issue.
My primary use case for BMC AMI Data is application performance monitoring and troubleshooting. I use it to analyze transaction behavior, identify performance bottlenecks, monitor system health, and quickly diagnose issues before they impact end users. It also helps me with capacity planning, trend analysis, and generating operational reports, giving better visibility into overall mainframe performance and application efficiency. One instance that stands out was when one of our critical batch jobs started taking much longer than usual to complete after a deployment. Using BMC AMI Data, I compared the performance metrics with previous runs and noticed a significant increase in CPU utilization and DB2 wait times for a particular transaction. That helped me narrow the issue down to an inefficient SQL query introduced in the latest release. After the deployment team optimized the query and added the appropriate job execution, time dropped back to normal. Without the insight from BMC AMI Data, it would have taken much longer to isolate the root cause.
BMC AMI Data is primarily used to increase data resiliency and availability, with the main focus on automating backup and recovery for business continuity. We had multiple databases across multiple servers and critical backups required for all databases. This has been automated so that backups are taken automatically, and whenever restoration is required, the automated process handles that as well. During DR and BCP scenarios, the restoration is used for testing. BMC AMI Data enabled automation of everything required from day one. No manual backups were taken, and as soon as new servers and databases were spun up, automation was set up from day one.
My main use case for BMC AMI Data is to monitor the data and the workflow for the compliance and reporting part, auditing and all. For compliance or auditing, BMC AMI Data diagnoses and resolves the problems, summarizes, and gives me a proper report. Since my industry is under banking and finance, the reporting should be in a proper manner. It manages the data, which actually protects and optimizes it and provides me the output according to it. I do not have anything else to add about my main use case or how BMC AMI Data fits into my daily workflow.
My main use case for BMC AMI Data is managing and analyzing mainframe data to ensure operational efficiency and compliance.
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.