Control-M Reviews

Like any other batch scheduler, Control-M is what we're really using for the various workflows that we have to either do on a daily or hourly or whatever basis. We run them all through there.

I've worked with other ones, and I think the certain simplicity to how Control-M lets you set up a schedule or workflow is excellent. It's very easy to build everything together.

Most of my complaints have ever been with support for Control-M, not with the product itself. I can't really think of anything that I strongly dislike about the product.

Sometimes my gripes with the support relate to them being a little slow to get back on either a query or a problem issue. That's my biggest gripe on the support side. There are some people in the support area that are fantastic, and then there are others that feel like I'm being pushed off to the side. I don't know what's going on in the back shop, so I can't say for certain.

I'm currently fighting with support on one issue right now with Control-M, but it's not the overall product, it's just one sub-piece that's being problematic. It's the SAP interface with Control-M.

My usage has definitely been more than ten years. I go back to release three or something.

I've never really seen much of a problem with scalability for Control-M. I'm sure there is an upper limit to how many agents and how many jobs you could run, but I haven't hit that yet. Where I am right now, we load probably about three thousand jobs a day, but we run a lot more than that because you can load a job and run it hundreds of times.

I'm going to give Control-M's support an eight on the scale from one to ten, with ten being the highest. That rating is mostly because of the reasons I've already mentioned.

I've used plenty of alternatives for Control-M in the past. Though some of them don't exist anymore or they probably still do. One of my former employers was a big CA-7 shop on the mainframe side, and on the distributed side, I used the IBM tool Workload Scheduler.

The setup for Control-M is not horrible. Once you get the server built out correctly initially, it installs relatively easily. There are some things in the update format that I don't favor. They used to have an update and migrate model, which would allow you to move from one system to a new system if you needed to upgrade your operating system or switch to a different platform. Going from Windows to Linux, or from SUSE Linux to RHEL Linux was easy to do. That migrate model was very good, but they went to an upgrade in place model. You can't do it that easily anymore because what you have to do is upgrade in place where you are and then build a new box and then move the data over, which is a bit of a pain. It was one-stop shopping previously. They stopped that at version 9.18.

The implementation is handled by me and maybe one other person who are specifically doing the deployment and maintenance of Control-M.

I don't really have any thoughts about the pricing for Control-M. I don't deal with that, or I haven't in a very long time. I know one of the pricing models is based on workload, specifically how many tasks you load in a day. I don't know if there are any other pricing models other than that. That's the only one I'm familiar with.

Control-M does require some maintenance on your end, as with any software. There are fix packs released or patches, and you need to be aware of that. BMC is very good about notifying you when they post out new patches or fix packs. It usually requires a downtime, but it's a very short downtime. It takes longer to take the product down than to apply the patch for Control-M. You have all the processes to shut down, apply the patch, and bring them all back up, and the up and down takes longer than the actual patch application sometimes. I would rate this review a nine out of ten.

I have been working with Control-M for about six months as part of my role. During this time, I have mainly used it for monitoring and managing batch jobs and automated workflows.

Our main use case for Control-M is managing and monitoring batch workflows across different systems. We use it to schedule jobs that run scripts, database processes, and ETL-related tasks. We make sure they execute in the correct order based on dependencies. On a daily basis, I mostly work with monitoring job runs, checking job statuses, and troubleshooting failures when a job does not complete successfully. We also review logs, rerun jobs when needed, and make sure the workflows complete within the expected time windows. Control-M helps centralize all this so we can track and manage automation more efficiently instead of handling tasks manually.

One of the examples for centralized monitoring in Control-M is the ability to view the status of all the scheduled jobs from a single dashboard. Instead of checking multiple systems individually, we can see whether jobs are running, completed successfully, or failed in one place. For example, if a job that runs a database script fails during the night schedule, we can quickly identify the failure from the monitoring interface, review the logs, and rerun the job if needed. This helps the team respond faster and keep the workflow running smoothly.

One of the best features of Control-M is its ability to manage complex job scheduling and dependencies across different systems from a single platform. It makes it much easier to automate workflows and monitor job execution in real-time. The centralized monitoring and alerting help us quickly identify failures and take action, which improves reliability and reduces manual effort.

Control-M has had a positive impact by improving the automation and reliability in our batch processing workflows. It helps ensure that jobs run in the correct sequence and reduces the need for manual intervention. The monitoring and alerting features have also made it easier to detect failures early and resolve issues quickly, so it helps keep our scheduled processes running smoothly.

One area that could be improved is the user interface. While the platform is very powerful, the UI can sometimes feel complex for new users, and it may take time to become familiar with all the features. Improving it and making the navigation more intuitive would help teams adopt it more quickly.

Another improvement could be simplifying the initial setup configuration process for organizations that are implementing Control-M for the first time.

I have been employed for almost four years, approximately three years and ten months.

Control-M is definitely stable. In my experience, once workflows and jobs are properly configured, it runs reliably and handles scheduled processes consistently. Most issues we encountered are usually related to the jobs themselves rather than the platform, and the monitoring tool helps identify and resolve them quickly.

Control-M is quite scalable in my experience. It can handle a large number of jobs and workflows across different systems without major performance issues. As the workload grows, it is possible to expand the environment and manage additional processes while still maintaining centralized monitoring and control.

Customer support has generally been good. The support team is responsive and provides helpful guidance when issues arise. The documentation and knowledge base resources are also useful for troubleshooting common problems.

Before using Control-M, some workflows were handled through basic scheduling tools and manual scripts. Moving to Control-M helped centralize job scheduling and monitoring, making it easier to manage dependencies and automate processes more reliably.

The biggest lesson I learned while working with Control-M is the importance of properly defining job dependencies and workflows during the initial setup. When dependencies are clearly configured, the automation runs smoothly and requires less manual intervention. It has also highlighted how valuable centralized monitoring is because it allows teams to quickly identify and resolve issues before they impact downstream processes.

We are just a user.

For return on investment, we have experienced improved automation and reduced manual effort. In terms of operational efficiency, automation through Control-M has reduced manual overhead by around twenty to thirty percent, especially for routine batch job monitoring and scheduled tasks.

I have no idea how the pricing, setup, and cost licensing is done. It is done by the finance department.

We saw the working methodology of Control-M, how batch jobs are handled, and how the automation works, so we just went for Control-M.

With job scheduling and workflow automation, this automation has increased the scheduling time by fifty percent. The monitoring task has been reduced by twenty to thirty percent. Instead of going on multiple tabs, we can view it at once. Workflow management through the technologies is a bit of a complex task. As we have used this, we can implement it. For new users, it might be a bit complex.

Currently, we have been using this for the past six months. We are seeing good, positive results. The automation workflow is also good, and the batch scheduling jobs are definitely good. We will still want to try it on different platforms and then decide on any further usage or increase in usage of Control-M.

In production, this workflow is mainly through the monitoring and reporting features in Control-M. We check the job status to make sure the scheduled process completes successfully within the expected time window. If the job fails or is delayed, we review the logs again, analyze the dependency chain, and rerun or troubleshoot the job if needed. This helps ensure that the overall production workflow continues without impacting downstream processes.

One piece of advice I would give is to spend time planning the job dependencies and workflows carefully during the initial stages. If the workflow is well-structured, Control-M can automate processes very efficiently and reduce manual intervention repeatedly.

Overall, Control-M has been a reliable solution for managing automated workflows and scheduled jobs. It provides good visibility into job execution and helps teams maintain operational stability. I gave Control-M a rating of eight because it is a very reliable solution for scheduling jobs and automating workflows, and it helps me manage complex job dependencies and provides good monitoring capabilities, which makes it easier to track and troubleshoot batch processing. The reason I did not rate it higher is that the interface can feel complex for new users, and the initial setup and learning curve could be improved. With some improvements to the user interface and onboarding experience for new users, it could become even more effective.

Control-M is used for scheduling, specifically scheduling application jobs. We have always been using Control-M, as we inherited it. We work with all technologies within the business using Control-M, including WebMethods, Axway, and IBM MQ. In the managed file transfer space, using Control-M, I find that it is not as user-friendly as other products available for MFT.

The best features in Control-M are its ease of use and robustness; it is very feature-rich, which is what we use it for—scheduling. It is easy to integrate Control-M with technologies for our DataOps and DevOps processes as things change. The integration aspects of Control-M are out of the box, so they are straightforward to use. From using Control-M, I have seen improvements as they grow with the versions; with the newer versions come enhanced functionalities, such as the latest version allowing access through a web GUI instead of the client GUI that you have to install, which is quite helpful because you can access it from anywhere.

We are not on the latest version of Control-M; we currently have version 20 and 21.

I have been using Control-M for 10 years.

Regarding stability with Control-M, I would rate it nine out of ten for downtime, bugs, and glitches.

Control-M is very scalable, and I would rate it a nine.

I would rate the vendor's technical support for Control-M as a nine.

Positive

We have tried to look at other products, but there is no other product out there that does what it needs to do as Control-M does, which is why it is still around—it simply does what it needs to do.

The deployment of Control-M was quite complex to get in, but once it is in, it does what it needs to do. It took weeks to get Control-M deployed.

Control-M requires maintenance from our end, as we have to perform patching whenever needed, but that does not happen very often—probably once a quarter. Patching for Control-M is easy.

I have definitely seen a return on investment with Control-M in terms of time and resources, but I cannot speak to the financial aspect—I do not know. I would estimate that I have saved perhaps 20% to 30% on time and resources using Control-M.

From what I know about pricing, I would probably put Control-M in the expensive category, but you do pay for what you get; you are paying for a premium product.

We looked at AWS products for scheduling with Control-M; I cannot remember the actual name of the scheduling tool at AWS, but it is nowhere near as advanced as what we need.

Approximately 100 users work with Control-M. They are not all Australia-based; some users are from India, so we have offshore resources as well. My relationship with BMC is both transformative and transactional. Regarding the transformative aspect, I have impressions of BMC as both because we partner with them; we might not be strategic partners, but we value their advice all the time—not just product advice, but sales as well. We consider ourselves a partner with the vendor, but I do not know if we are officially a signed partner; we are a partner at the base level. When I refer to our relationship with the vendor, we are considered an entry level partner; we are not a reseller, so we cannot resell their products, but we are partners and we consult while using their products internally and with customers to support their customer base. Our clients are generally enterprise businesses. I would recommend Control-M to other users; I know they are going all web-based, which they believe is the best way to go, but I can tell you that everybody I have spoken to has said the contrary—they actually prefer the client GUI that you can install, which is easier to use and more feature-rich, while the web GUI is very hard and difficult to navigate through. I have rated this review a nine out of ten overall.

My main use case for Control-M is building a data platform that brings data from multiple sources from disparate systems. I use Control-M as a data orchestration tool for data monitoring and scheduling jobs for observability purposes, with a focus on AI-powered data observability and job monitoring.

I manage the integration and sequencing of data processing across diverse data platforms and cloud environments by setting up the predecessors and successors in the workflows.

My organization is already using Control-M to support AI acceleration by bringing data from disparate sources into our data platform. Control-M has facilitated the integration of Kubernetes and cloud-native workflows with traditional batch processing by utilizing DevOps for CI/CD, which is effective during deployment and production migration.

For compliance requirements such as SOX and audit trails, I implement role-based access control as part of our data governance with Control-M.

Control-M has positively impacted my organization as it stands out as one of the best tools compared to competitors in the market, particularly in terms of cost-effectiveness, reliability, and ease of maintenance, along with a user-friendly interface.

I have noticed significant time saved, particularly with mean time to rectify issues since using Control-M.

The best features Control-M offers for me are an easy way of scheduling jobs and monitoring, setting up predecessors and successors, and providing good control to the user who is monitoring for the data engineer.

Out of those features, I rely most on setting up predecessors and successors and maintaining the graph of which job has failed and how to rectify it as the most significant aspect of my workflow.

The observability features are very good, especially the job monitoring feature.

Control-M can be improved by enhancing data flow monitoring capabilities and addressing some data engineering needs.

I would also like to see improvements in user experience via mobile and support for iOS and Android with a nice UI interface.

Regarding Control-M's AI capabilities, I think implementing a role-based access mechanism for governance and security would be perfect.

One improvement I need is to introduce an AI-powered agent, which would make Control-M unique compared to others in the digital world.

I have been using Control-M for around seven years.

Control-M is stable.

Control-M's scalability is very good; adding nodes and servers is easy on the fly.

Customer support for Control-M is excellent. If I face any issues, I can easily reach the support team and get resolutions within the defined timelines set at the initial purchasing time. I would rate customer support a nine, definitely.

Prior to Control-M, I used a different solution, AWS Workflows.

I have seen a huge return on investment with significant time savings by investing in AI-powered solutions and integrating with the cloud.

My experience with Control-M's pricing, setup cost, and licensing has been positive, as I find it a good product with excellent features that add value for customers, and the cost is reasonable compared to others.

Before choosing Control-M, I evaluated options such as Ansys and cron.

On a scale of one to ten, I would definitely give Control-M a ten because it is a very good tool from BMC. I rate it a ten because it is such a reliable data pipeline monitoring tool that I have seen in the market. The quality of data, particularly in setting up predecessor and successor jobs in the pipelines, makes a significant difference in any data pipeline's success. My advice for others looking into using Control-M is that it is one of the best reliable products in the market for on-premise, private cloud, and public cloud deployments.

My main use case for Control-M is the Control-M infrastructure. In my day-to-day work, I use the web and GUI console for Control-M infrastructure. With the web and GUI console in Control-M, I manage processes such as scheduling, monitoring jobs, and handling upgrade migrations. I handle tasks with Control-M including upgrades, disaster recovery, testing jobs, and scheduling jobs.

I manage the integration and sequencing of data processing across diverse data platforms and cloud environments using Control-M Enterprise Management. My organization is currently planning to use Control-M to support AI or ML workloads, and we are willing to implement AI with ML workloads.

Regarding Control-M facilitating the integration of Kubernetes or cloud-native workflows with traditional batch processing, I do not see any difficulties while configuring. We will be using AI, but we have not reached that point yet, although we are willing to do so. I use Control-M to address compliance requirements such as SOX, ensuring that we meet audit trails and regulatory reporting.

Control-M has positively impacted my organization with significant improvements. We have many customers and perform production work, with numerous services being utilized by customers. Specific improvements noted since using Control-M include our ability to perform extensive automation, especially with scripts across the organization, as well as numerous agent installations and software installations that we manage with Control-M.

The best features that Control-M offers are Data Assurance and MFT. Data Assurance and MFT stand out to me in Control-M because we manage files for transfer and ensure Data Assurance. Besides those features, we also handle agent installations, agent automation, and remote agents for data grouping in Control-M.

I am satisfied with Control-M's governance and security capabilities. In terms of Control-M's AI capabilities, I believe its accuracy and reliability of output depend on the inputs and data values, which allows us to rely on that output.

I believe Control-M could be improved in customer support and vendor support, as we desire improvements there, as well as advancements in Agentic AI and upgrades to newer versions of Control-M that support multiple web fixes. Customer support should offer better immediate solutions whenever we contact them about an issue.

I rate Control-M nine out of ten because I believe customer support and vendor support require improvement, making the user experience less interactive.

I have been using Control-M for five years.

Control-M is stable.

Regarding Control-M's scalability, we have scalability based on the license, allowing us to scale up.

We have great customer support with Control-M.

I previously used Autosys before switching to Control-M.

We have seen a return on investment. There is money saved, and we have reduced the amount of manual jobs, allowing Control-M to save us significant resources, especially on human intervention.

I am very satisfied with the pricing and the cost of licensing for Control-M.

Before choosing Control-M, I evaluated CA-UWS among other options.

I rate Control-M a nine on a scale of one to ten. In my organization, we deploy Control-M using a combination of private cloud as well as hybrid cloud. For our private and hybrid cloud deployments, we use IBM as well as Azure as our cloud providers. My advice to others looking into using Control-M is that it is one of the best tools available in the market today, enabling you to perform extensive automation and workload orchestration.

My main use case for Control-M is to schedule jobs to run particular things, to load files from one path to another path, which I do daily. To load some data into the cloud, I utilize particular jobs inside Control-M, where I usually create the jobs and monitor their execution.

A quick example of a job I schedule or monitor in Control-M is defining a job name, job details, weekly schedule, monthly schedule, and daily schedule, according to which I schedule the jobs.

In my day-to-day operation role, Control-M is mainly used to monitor, manage, and troubleshoot bad jobs. For example, for monitoring job execution, I check running jobs in the monitoring domain and verify if the job's completed status is ended okay or if any failures occurred. If it is executing for a long time, I can mark it as long-running.

Analysis of the job failure involves opening a job output or logs to analyze the failure within Control-M and checking for application errors, script errors, database issues, or file-level issues to coordinate with the application team if needed. These are the aspects I usually analyze when job failures occur. When rerunning a job, I check and find the issue; if I find the issue in the Control-M logs, that is fine. If I cannot find any issue, I log into the server and check the application logs, then debug and rerun the job. I also monitor the SLAs.

Currently, I am not using Control-M to support AI or ML workloads; I primarily use it for batch monitoring and loading data to customers.

I do not utilize Control-M much for compliance requirements; I mainly use the production environment for loading and monitoring jobs only.

Control-M positively impacts my organization by automatically handling batch processing, reducing manual efforts, improving reliability, and ensuring critical business workflows are completed on time. It provides centralized monitoring, automated scheduling, dependency management, and proactive alerting for missing elements and also generates reports.

For example, the File Watcher prevents jobs from failing due to input file delays, leading to better SLA compliance for critical batches like claims, payrolls, or reporting processes. The visibility provided by a single dashboard allows for end-to-end monitoring of jobs and workflows. Resource automation through reduced manual scheduling and monitoring lets teams focus on higher value activities.

In my environment, Control-M helps monitor daily batch streams, identify failures early, track downstream impacts, rerun jobs after resolving issues, and ensure successful completion of business-critical processes, reducing downtime and improving service reliability for business users.

The best features Control-M offers include centralized job scheduling, allowing me to schedule any kind of jobs based on Windows, Linux, Unix, database, Informatica, Hadoop, and cloud applications without needing to log into multiple servers. Additionally, Control-M automatically manages job dependencies, such as extracting data, transferring data, loading data, and report generation, while also providing real-time monitoring for operations to use for running jobs and quick identification of production issues.

Automatic alerts and notifications are set for job failures, missing files, SLA risks, or long-running jobs, and I can trigger these alerts inside Control-M and send notifications via email, ServiceNow, Teams, or other platforms. The tool also allows easy recovery and reruns of jobs, including holding, freeing, or post okay, and ordering jobs if needed. Moreover, features like File Watchers help avoid job failures due to missing files, and calendar-based scheduling allows me to set times for jobs daily, weekly, or monthly. Cross-application workflows like MoveIT file transfer, Unix scripts, and email notifications are integrated. Finally, auditing and reporting capabilities manage execution history, user actions, job logs, and compliance records.

The File Watchers are really valuable features in Control-M that I find helpful. For instance, I had dependencies for files where I need to get the raw data from them, and once the file is present in the specific path, the job executes. However, if the file is missing at the scheduled time, the job fails. This is particularly problematic since delays can impact production. The File Watcher helps prevent job failure due to files missing, especially when delays happen, such as vendor delays or issues in file transfers. It significantly reduces manual monitoring and improves SLA compliance by ensuring jobs start immediately after the file is received. If the file is missing throughout the scheduling day, an alert is generated.

The integration across different platforms and cloud environments is performing very well, and I never receive complaints regarding this aspect.

Control-M is a powerful workload automation tool, but there are areas for enhancement. The user interface can be more intuitive and interactive, particularly for new operators. Implementing AI-driven predictive analysis cloud capabilities could help in predicting job failures before they occur. Enhanced cloud-native integrations and more dedicated root cause analysis dashboards would further improve the operational effort and incident response time.

Control-M job documentation, audit logs, and everything related are currently satisfactory; I do not see necessary improvements here. However, I could integrate more tools and introduce AI agents in Control-M to assist new users in understanding better and guiding them in creating jobs or monitoring actions.

I have been using Control-M for the last three and a half years.

On a scale of one to ten, I can rate Control-M around an eight because it is a powerful tool that I have previously used.

I would rate Control-M an eight out of ten. It is excellent for workload automation, dependencies, SLAs, and everything in between. I did not give it a nine because troubleshooting complex workloads sometimes requires specialized knowledge, and the interface can feel less interactive for new users. Moreover, there is still a dependency on the underlying application and interface; for instance, if a database, network, or application issue occurs, Control-M can detect and report the failure but cannot always resolve the root cause automatically. Overall, it is a strong enterprise scheduler but has room for usability and self-handling improvements.

Regarding Control-M's AI capabilities, I believe the value comes from faster operations, but governance and security must remain top priorities in enterprise environments such as banking, healthcare, insurance, and retail. From a governance perspective, Control-M should offer recommendations rather than autonomous actions on critical production workflows while maintaining an audit trail that tracks changes, reruns, and approvals. On the security side, important measures include ensuring AI only accesses job metadata and log scheduling, without gaining access to customer data or sensitive information.

The accuracy and reliability of Control-M's AI output meet my expectations. I find its performance very reliable, and I appreciate the AI features, although there is still room for improvement in certain areas.

I definitely advise others to consider using Control-M because of its simplicity and cleanliness.

My current use cases for Control-M are mostly for workflow modernization, including internal workflows related to OS type executions, file transfer executions, Azure pipeline, data ingestion, and BDNA workflows.

What I like most about Control-M is the modernized API, which allows me to integrate with many other applications to create job flows using JSON. Application teams can own their own responsibility to create jobs instead of relying on a Control-M admin to create a job every time they need it.

Control-M should evolve into a more web-based SaaS level product, which they are looking into. They should improve the features that were available in Control-M localized installations. For example, if I have the server on-premises, more features are with the client, whereas with the web-based version they moved to now, many features have been missing that we expect should be coming as well. Configuration and CCM are missing most of the features that are in the on-premises version. Those features should be coming very soon, and that would be the best improvement they could bring.

Additionally, auto deployments and patch management always require manual work, so those should be coming through auto deployment, which would help significantly. Software upgrades and similar processes should be more optimized.

I have been using Control-M since at least 2014, which is about 13 years now.

Regarding stability, I would say it is based on the environment. For my bank, I never had an issue with crashing or anything; the performance was good.

Scalability is easy for me. It is all based on the plan regarding how many jobs I am going to use. I do not see any complications with scalability.

I have contacted technical support and we raise cases with them before every single upgrade or migration. We have a method of procedure plan where we check with them about recommendations. Most of the time, BMC does not recommend against proceeding, and they say I am good to go with it.

When there is an issue, the quality of support for a P1 case is good, but the solution they provide can be complicated sometimes, which takes time for them to investigate. Then they have to engage their developer team. Sometimes I would have already known that this is not solvable and only the developer team can advise on it, but they do not take it immediately. They take their own time to provide the solution, meaning they take one or two additional hours. If I were to put support on a scale from one to ten, I would give Control-M support a score of seven.

I have used alternatives like Redwood and some other internal tool called Insights software. Comparing Control-M to Redwood, Control-M is better; however, Redwood has a simpler tier level architecture than Control-M.

I did not migrate from Redwood to BMC; I worked on that software at my previous company where both software were used. AutoSys has also been used there. I have seen developers compare AutoSys to Control-M.

Migrating from other software does not require much work from my side; it is easier even for the developers. Control-M requires a little understanding of how these things work. For example, when the application person reaches out to me, they need to understand first how job creation works and how their job executes on their agent, and it is not more transparent for them. I have to conduct a learning session to help them understand these things, and those aspects are complicated in Control-M. However, Control-M is more optimized in that part. When they understand the tool, they prefer Control-M to any other solution.

When I joined my current bank, Control-M was already deployed but with a very old version. I took care of upgrading it to the very latest version. We were ten years behind, and there was no support, but I managed to get it upgraded to the latest version.

The upgrade was challenging at first. The OS flavors were very old, which Control-M does not support with the latest version. We had to plan carefully, thinking about how we would move to which version, and then we did a parallel build in Red Hat to move it. It was a very comprehensive plan we had to develop. However, it went well. When I am more experienced with the tool, I know how to execute the migration properly.

When it comes to deployment and maintenance, maintenance stays with the administration team, which is my team. We currently work as a core of five people for the deployments. When it comes from the application team to create a job, workflows, or whatever they need planned, it involves four to five people.

Regarding pricing, BMC is a little costlier. They need to come down with execution-based pricing. For my understanding, BMC charges based on executions, whereas Redwood charges or AutoSys charges based on successful completion. BMC is a little higher on that.

Integrating with technologies for my DataOps and DevOps processes as things change is easy. We use DevOps and data pipelines related to Azure data pipelines, and it has been easy for us because the latest version does have the support. We use our own application integrator developed by ourselves, so it was easier for us since we know the tool. My review rating for Control-M is ten out of ten.

My main use case for Control-M comes from when I was an operations analyst, using it for scheduling, reporting, and others, and now as a supervisor ensuring that our people are up to date on all the procedures and practices for the product and ensuring that the product is working well for our needs.

Currently, my team uses Control-M for maintaining, processing, and scheduling our nightly batch cycles, and we are in the midst of a conversion from an MVS system to another using Control-M as its primary application for our scheduling and batch processing needs.

I manage the team that actually uses Control-M, so it is very intuitive, and our people seem to catch on to it rather quickly. They appreciate the product, they appreciate the functionality, and so far everything is working well.

Control-M has impacted our organization positively, and we appreciate it so much that we moved from using Tivoli on the mainframe. It is more intuitive with more robust features, and from my experience using Control-M at previous places, we were very pleased with it, so I am sure this newer version is robust, user-friendly, and intuitive.

I do see specific outcomes of reduced errors and faster processing since switching, although I don't observe the metrics of it as I am more of a supervisor of a group of users, but I am sure that it is happening because overall, it is very effective.

In my opinion, the best features that Control-M offers are ease of use and predictability. It is very easy to use and very intuitive, it does not take much to find errors in our processing, and the flexibility to restart and hold job streams is very good. Our team seems to pick it up very well and even if they had not used this product previously, they are appreciating it now.

The flexibility of Control-M has helped my team in practice, especially when we run our batch cycle throughout the night. Our operations analysts work only until about 8:00 p.m. or maybe 9:00 p.m., and during the evening, we have our operations staff doing the monitoring and ensuring that if there are errors with the processes, we can contact the programmers on call. Our staff does not need to be as knowledgeable as our operations analyst staff, making holding and restarting jobs easy for them.

Nothing comes to my attention regarding improvements needed for Control-M. There was a discussion of a stop button where one simple click would stop a whole process, which would be useful if we encounter an error, but I am not sure if that was suggested or implemented.

I have been using Control-M since 2000.

I would rate Control-M a 10 overall. From all the different users and groups involved with this project—including the programming staff—everyone appreciates the functionality, and I feel confident that I could step in there to restart or hold jobs if needed, indicating that anyone could handle the basics in case of absences or illnesses.

I chose a rating of 10 because it is something that everyone finds very intuitive. People enjoy using it for its simplicity, and the options we get in its use are very helpful and tailored to our needs, with different divisions and groups involved all being pleased with its process.

We have a security team that scrutinizes all our applications, and I have not been privy to any information from that department.

I do not believe my team has had issues with Control-M's accuracy and reliability of output, as I am aware that AI is a big focus, but unfortunately, I do not have any detailed information on its uses and capabilities.

My advice to others looking into using Control-M is to start using it and not be afraid to try it. I would feel that everyone would be very satisfied with the product since it is very user-friendly and a good fit for any large or small company regarding scheduling and processing monitoring needs.

My main use case for Control-M is for back-end coding and infrastructure. A specific example of how I use Control-M in my back-end coding and infrastructure work is that we use Control-M to help write the code and debug the code. It helps to deploy the code to the server for easy deployment, in comparison to before. Control-M has reduced a lot of our effort in comparison to doing it manually.

To manage the integration and sequencing of data processing across diverse data platforms and cloud environments, I create instances, and they provide security like AWS and Jenkins. They provide a way to easily manage simple storage service and the S3 bucket to create the deployment, the application, and the replica of the application.

My organization has been using Control-M for the last two years to support AI and ML workloads. Control-M has facilitated the integration of Kubernetes and cloud-native workflows with traditional batch processing in my organization by being used for Kafka messaging purposes to send large data in bulk processes. We use Kafka and Pub/Sub for data transmission, and we use authentication and authorization for security purposes. Based on our knowledge, they provide features like bucket creation related to our service.

My organization uses Control-M to address compliance requirements, such as SOX, audit trails, and regulatory reporting, by involving security features like authorization for things like data migration.

Control-M has positively impacted my organization by reducing the effort and time required, allowing us to deploy the application easily and finish our work within a particular deadline. The specific outcomes we achieve with Control-M include saving a lot of time in comparison to manual processes, as it has reduced more than five to six hours in comparison to other tools.

Control-M offers many valuable features, including innovation and extensive server management. The server management features provide DevOps tools and continuous integration and continuous deployment through Control-M, which helps the Jenkins process deploy the application easily. Control-M is very good in comparison to other company tools in terms of features.

Regarding Control-M's AI capabilities, they provide security through features like authentication, authorization, and OAuth, and they also provide JWT. I find Control-M's accuracy and reliability of output to be lightweight.

Control-M can be improved with additional features, as we use monitoring tools for deployment like New Relic and Grafana, and we need to monitor the application because if there is a debug issue or any application downtime, we need to use those tools. Everything else is good with Control-M, and there is no need for improvement right now.

I have been using Control-M for the last two years.

Control-M is stable.

The scalability of Control-M in comparison to other tools is good and scalable. The maintenance cost is also good in comparison to others, and it is scalable to use the tools easily.

Customer support for Control-M is very good. I would rate the customer support a ten on a scale of one to ten.

I previously used a different solution and needed to switch because the technology is changing day by day. It is now the era of AI and automated code in comparison to previously when we had to write the code manually as developers. Now, we use AI tools, which help a lot, and then we use Control-M.

I have seen a return on investment in terms of both money saved and time saved.

The experience with pricing, setup cost, and licensing is that the setup cost is less in comparison to other tools, and the overall experience was very good.

I gave Control-M an eight out of ten because it is user-friendly and helps the developer to deploy the application easily. The advice I would give to others looking into using Control-M is that it provides automated features in comparison to other tools, and they save cost and time.

My main use case for Control-M involves job scheduling and enrolling jobs. We assign job schedulers and receive tickets to work on them. This is the primary area where I use Control-M.

In different data platforms and cloud environments, we mainly use Google Cloud, and my experience has been very smooth and effective, thanks to its performance, user-friendliness, and quick responses that help meet user expectations. The automation aids significantly as well.

In our organization, we utilize Control-M to address strategic compliance requirements such as SOX audits and regulatory reporting by maintaining transparent, end-to-end audit trails for our batches, workflows, and specific jobs. We employ Control-M's built-in audit and history tracking to record every job event, along with automated reports and role-based access control (RBAC) to ensure that only authorized personnel modify critical financial and regulatory schedules. This supports compliance verification and audit readiness, which is particularly helpful in the financial sector for maintaining secure data.

Control-M has positively impacted my organization by alleviating the difficulty of assigning job schedulers and working on tasks. After its introduction, it has greatly assisted teamwork and reduced the working effort significantly, ensuring we deliver excellent output to our clients.

Manual tasks have decreased significantly, saving approximately four to five hours daily and providing substantial productive hours for the employees.

Control-M has facilitated the integration of Kubernetes and cloud-native workflows with traditional batch processing by scheduling operations to run in discrete SQL blocks over time, grouping and routing high volumes of tasks such as end-to-end reports or billing. These areas required considerable time before Control-M was introduced. Once implemented, Control-M made it easy for people to understand and work on these aspects while reducing time. Failed batches can now be quickly identified, corrected, and rerun without disrupting ongoing workflows.

The best features that Control-M offers are scheduling and reporting, as I use Control-M for both purposes. I find scheduling more valuable, as I work on that daily in my day-to-day tasks, scheduling operations and addressing the bugs that I encounter.

Control-M's governance and security capabilities are excellent, as they help many people understand processes and keep data in a secure mode. Control-M is consistent for me, with very good accuracy and understandable output.

Control-M is good at this time, and there is no upgraded version that I can work on since I need to explore Control-M more, as my access is limited. Whatever access I have is very comfortable and user-friendly, providing a very good environment to work in with no need for any developments or changes.

I have not faced any challenges or difficulties while integrating Kubernetes or cloud-native workflows with traditional batch processing using Control-M, as it is very smooth and efficient.

I have been using Control-M for two years.

Control-M is stable, and after using it for the last two years, I see no issues with it.

Control-M does not handle scalability well.

The customer support is very good, as I received phenomenal assistance from the customer support team. I would rate the customer support a nine out of ten.

Control-M is my first solution in this category.

I have seen a return on investment primarily in time saved. As an employee of the company, I can attest that significant time savings allow me to work on many other things.

Regarding pricing, setup cost, and licensing for Control-M, it is minimal. Compared to all competitors in the market, it is reliable and user-friendly.

On a scale of one to ten, I would rate Control-M an eight. I give it an eight because it is a phenomenal rating that is very reasonable. While there might be some changes to make it more user-friendly, I have not explored those yet, but if given the opportunity, I will definitely explore the complete Control-M. Control-M in my organization is deployed on a private cloud, not a public one, and specific clients are using it, which is very helpful for security purposes with very limited users.

In our organization, we use multiple cloud providers depending on the project level. Some use AWS, and some use Google Cloud, so this varies. I would definitely recommend others to use Control-M because it is user-friendly, allowing for very easy and simple daily actions.