Control-M Reviews

The main use case for Control-M in my daily work is to monitor scheduled ETL batch jobs and check whether they have completed successfully. For example, if a data load job fails or gets delayed, I check the job status and error details, coordinate with the support team to resolve the issue, and verify that the job completes successfully after the rerun. This helps ensure data is available on time for validation and reduces manual monitoring.

I want to add a few use cases for Control-M in my day-to-day work. Apart from monitoring ETL jobs, I use Control-M to check job dependencies, track scheduled executions, and identify failures or delays. For example, I verify that source jobs are completed before target jobs start, and I follow up with the support team if the job fails. It helps improve coordination between different teams, reduces manual effort, and ensures data loads are completely reliable and on time.

Regarding cloud management, I use Control-M to coordinate and sequence data processing jobs across different platforms. It helps manage job dependencies, schedule workflows, and monitor execution status from a centralized view. For example, a source execution job must complete successfully before the ETL transmission and target loading jobs begin. If a job fails or is delayed, I check the status and error details, coordinate with the support team, and verify successful completion after the issue is resolved. This helps maintain the correct processing sequence and improves the reliability of data pipelines.

To my knowledge, my current use of Control-M is primarily focused on ETL job scheduling, data pipeline monitoring, and data processing.

Control-M has helped improve my organization's efficiency by making ETL job monitoring and failure identification easier. For example, when a scheduled job fails or gets delayed, I can identify the issue quickly, coordinate with the support team, and track the job until it completes successfully. It also helps improve teamwork because everyone has better visibility into job status and dependencies. This reduces manual monitoring, helps prevent delays in downstream data validation, and improves the reliability of my data processing workflows.

I have noticed practical improvements in day-to-day operations. For example, Control-M helps us identify failed ETL jobs more quickly, reducing the time spent on manual checks. It also makes it easier to track job dependencies and coordinate with the support team when issues occur. This helps us resolve problems faster, minimize delays in downstream data validation, and improve the reliability of the data loads.

There are many special features in Control-M. The features that stand out most to me are Control-M's job monitoring, automated scheduling, dependency management, and failure notifications. I rely most on job monitoring because it helps me quickly identify failed or delayed ETL jobs. For example, I can check a failed job's status and error details and coordinate with the support team to resolve the issue. Dependency management also helps ensure that source jobs finish before downstream target jobs begin. These features reduce manual monitoring, improve communication between teams, and help ensure data processing is completed on time.

An additional feature I use in Control-M is the visibility it provides into the overall workflow and job dependencies. It helps us identify if a process has failed and understand when downstream jobs might be affected. For example, if a source job fails, I can identify dependent jobs early. I think more people should know how this visibility can help with proactive troubleshooting, better team coordination, and reducing delays in data processing.

From my perspective, secure job execution is an important aspect of Control-M. Role-based access helps ensure that users have permissions appropriate to their responsibilities. For example, some users may only monitor job status, while other authorized support teams can manage or rerun the jobs. Audit logs are also useful for tracking job activities and supporting compliance requirements. Overall, I emphasize the importance of proper access permissions and auditability, while ensuring that sensitive data and credentials are handled securely.

However, AI-assisted analysis can be useful for identifying potential job failures, highlighting patterns, and speeding up troubleshooting. I would still validate the recommendations against the job logs and actual execution results before taking action. I think greater accuracy in root cause analysis and more actionable recommendations could make the capabilities even more valuable for ETL monitoring and support.

The most effective Control-M functionalities for my workflow are centralized job monitoring, scheduling, dependency management, and failure notifications. These help coordinate traditional batch processing and ensure jobs run in the correct sequence. For example, source data extraction must finish successfully before transformations and target loading jobs begin. If the source job fails, I can identify the issue, coordinate with the support team, and verify the downstream processing after the job is rerun.

One area that can require adaptation is integrating jobs running on different platforms, particularly when they have different authorization methods, scheduling requirements, or error-handling mechanisms. Another challenge is troubleshooting failures across multiple systems where the root cause may not be immediately clear. Better error details, consistent logging, and clear dependency visibility could make the workflow easier. Overall, Control-M provides a centralized view for monitoring workflows, but successful migrations still depend on proper configuration, testing, and coordination between different teams.

Audit trails and regulatory compliance are important considerations in my data processing workflows. Control-M's job execution history and activity tracking can help me understand when jobs run, whether they are successful or fail, and support troubleshooting and audit reviews.

Control-M could be improved by making troubleshooting easier by providing root cause information when a job fails. For example, it would be helpful to have more actionable suggestions for resolving common ETL job failures. Another improvement would be more customizable real-time notifications and dashboards, so teams can quickly identify critical failures and prioritize them. These enhancements could reduce troubleshooting time, improve communication between teams, and make daily monitoring more efficient.

Apart from what I have mentioned, everything is good with Control-M.

I have been using Control-M for the past six years and eight months, mainly for monitoring ETL jobs, checking job execution status, and identifying failures or delays. I use it to help ensure data processing workflows complete successfully and to coordinate with the support team when issues occur.

In my experience, Control-M is generally reliable for monitoring and scheduling ETL jobs. However, job failures or delays sometimes occur because of downstream systems.

Overall, Control-M has been reliable for my daily ETL job monitoring and scheduling. When an issue occurs, the job status and execution history help me investigate and coordinate with the support team.

Overall, my experience with Control-M has been positive. It makes monitoring ETL jobs, tracking execution status, and identifying failures easier. For example, when a job schedule fails, I can quickly identify the issues and coordinate with the support team. I would rate customer support as 4 out of 10 based on my support experience.

I do not have any exact figures for time or money saved, but Control-M helps reduce manual effort through automated scheduling and centralized job monitoring.

I am aware that solutions such as AutoSys, IBM Workload Automation, and Apache Airflow can also be used for workflow scheduling.

On a scale of 1 to 10, I will rate Control-M an 8 out of 10. It is a useful tool for automating job scheduling, monitoring ETL workflows, managing job dependencies, and identifying failures. It improves operational efficiency and helps teams coordinate more efficiently when issues occur. I have not given it a 10 because there is still room for improvement in error analysis, root cause identification, and customizable alerts. Overall, it is a reliable tool that makes daily batch monitoring and support activities easier.

Control-M can be deployed in a public cloud, where it runs in environments such as AWS or Azure. In a private cloud, it runs on a dedicated cloud environment for an organization. Hybrid workloads run across both cloud and on-premises infrastructure. On-premises means Control-M runs on an organization's own servers and data center. In my project, they use AWS and Microsoft Azure.

I would give good advice to everyone. It has great facilities and efficiency. I would suggest Control-M to everyone.

My main use case for Control-M is for control jobs on Linux machines. We have some shells, and I automate shell scripts on Linux machines. Control-M works similarly to a graphical crontab.

For example, I have some scripts that I must process on batches for clients. These batches have a time-specific moment when they can be used. For example, we receive a text file and must upload it to a mainframe using a shell script to automate this process. Control-M executes this task at the specific time and works fine. I check it every day and provide reports to the customer and developers with information in emails to detect whether the job is successful or not.

Control-M is a good tool for managing a large number of jobs across a large number of machines. Another very important feature is cyclic jobs. For example, I have the first job, and then I have two, three, or four jobs that must run after this, and I can make these dependencies as well. It is easy to change the schedule, and this feature is very useful in Control-M. The graphical interface makes things easy.

The cyclical jobs are a good feature that I see, along with the ease of implementation. It is very easy to put a job to run, suspend a job, or hold the job. The management is very useful and fantastic. I believe this is the main feature of Control-M that administrators decided to use it. In any project that I work on as a projectist or architect, I would suggest the use of Control-M in larger environments as well.

Control-M has positively impacted my organization by saving time. It makes administration tasks easy. I have very accurate information in emails, and every time a job runs, the outputs are classified by time, which makes things easy as well. It is very good. I work in an environment where I have around 300 servers all working together with Control-M on my IUA. It would be quite impossible to manage all these jobs and this entire server park without this tool. The time saving is very impressive.

The graphical interface of Control-M, although useful, needs some improvements. It is quite old.

The boxes in the graphical interface could be changed to get a better view. It is not useful for using on small screens. A larger screen is needed to see properly a large number of smart jobs or jobs on the screen. This organization should be improved in my view.

In my current field, I have worked for one year, but I have worked with Control-M for more than 10 years.

Control-M's scalability is easy because we have several servers in use, and it is quite easy to add new servers and put them under our management as well.

Customer support for Control-M is nice. Because the solution is stable, there are few situations that require activating support, but the team provides good responses and support every time we need a solution.

The solution was already implemented when I started, so I did not compare it to other solutions. I am not an architect in order to decide for another solution. The solution was already implemented, and I use it to manage my servers.

The company saved money and time and has good results with the use of this tool. We have a portal named MyCloud, and in this portal, we have the cost of each solution that I worked on. It has a cost, but it has a good benefit.

The versions of Control-M that I have worked with do not have AI features, so I do not have any information about this, but in my vision, I think some tasks can be implemented through an AI agent. Some aspects of security should be required in this case.

I use Amazon Web Services as the cloud provider for those servers.

On my site, in my team, we use Control-M to manage around 300 servers. Increasing usage is directly proportional to the use of servers. If we implement more servers, they will have Control-M to manage the jobs. At this moment, we have a project that is stable, and we have no plans to increase it, but it is possible at any time.

The biggest lesson that I have learned is that Control-M is useful to save time and manage extensively a structure with one console.

At this moment, I believe that is all I have to say. The service is quite extensive, and there is a lot of information that might be useful. My overall review rating for Control-M is nine out of ten.

My main use cases for Control-M are managing file transfers and running the database management programs for the enterprise.

When I mention connections in terms of Control-M, I'm thinking of connecting to databases to run database queries and updates, connecting to file transfer facilities to move files across the organization, and communicating with API environments using the API interface. Those are the sorts of capabilities I'm referring to.

We haven't actually started down the path of using Control-M to support AI or ML workloads yet. Currently, I'm working on a project to upgrade and migrate our on-premises Control-M to the cloud, specifically to SaaS Control-M. The next step after this migration would be to begin integrating AI capabilities into our workflows, but that journey hasn't started yet as we need to get internal approval and review the AI offerings from BMC first. Right now, my focus is on transitioning to the SaaS platform instead of jumping straight into AI integration.

So far, the thinking around how to use AI integrations with Control-M is not advanced. Initially, I consider utilizing AI's facilities and capabilities to help with day-to-day management of the environment. For example, a simple use case could be using AI to reduce call-outs to on-call staff because the tool will provide guidance for the 24/7 staff on how to investigate a problem or rectify issues. The capability of AI could help with triage and sorting out problems before the need to call someone to fix it, addressing the tendency of 24/7 teams to only react to failures without engaging in investigation.

At this stage, we don't use Control-M to facilitate integration of Kubernetes or cloud-native workflows with traditional batch processing. My main focus remains on file transfer activity and simple interfaces to run batch processes for managing database updates, data extracts, and reporting.

From my perspective, my role doesn't involve corporate compliance and auditing, but I utilize Control-M to monitor its use through audit reports and logging. I ensure I capture and retain that information for historical purposes over several years so that if there's a need to review events from six months ago, that data is available. However, I'm not involved in corporate-wide auditing and compliance aspects.

I can't provide metrics from using Control-M because I've only been with this particular organization for five years, but the biggest benefit is that it allows workflow orchestration to be done in a cost-effective manner, without the need for complex solutions or customized proprietary solutions to keep track of things or run the workflow. Control-M does that for me, and as it's a consistent interface, it allows me to deploy many different applications and tasks in one place while providing an opportunity for the business to have an easy, quick interface to determine where the workflow has progressed.

In my opinion, the most valuable features of Control-M are the GUI interface, which provides the facility for other parties to use the tool without a huge learning curve, as well as a very good intuitive view of my workflow that allows me to find issues and rectify them in a timely manner. With the new features, such as AI coming along, it's going to be even more exciting. I find that another aspect is the capability of the tool to connect to many different environments and be platform agnostic, so I don't have to worry about operating systems or what I'm connecting to, thanks to the built-in plug-in control modules.

From my perspective, the beauty of Control-M is that I don't have to think about managing integration and sequencing of data processing across diverse data platforms and cloud environments. I simply have the interface, and I define the workflow to communicate with those environments using the tool's capabilities that provide that connectivity. Therefore, I'm not too concerned about the specifics of what I'm trying to achieve in the backend, as the tool facilitates that without needing to know the target tool or environment.

I am currently migrating to SaaS Control-M, which is BMC's software-as-a-service, and this represents an improvement because it eliminates the need to manage the platform and keep it up-to-date. However, some aspects of the new interface could be improved, which are relatively minor issues. For example, I have four environments in my SaaS Control-M setup, and there isn't an easy way to tag those environments to quickly identify which one I'm in without doing checks. I believe there should be a mechanism to allow customization of the display, whether through color or a title, to clarify my location within the environments.

I've personally been using Control-M for over 30 years, and in my current role, I've been using it since I started with my current organization, which has been about four or five years now.

I can't fault the stability and reliability of Control-M. With 30 years of experience using the tool, I've encountered maybe one to three disasters due to product faults, with the first occurring decades ago and another about 15 years ago. Such failures are rare, and when they do happen, recovery is usually straightforward. I've never faced a situation where a Control-M database became corrupt and unrecoverable; typically, proper environment management with backup ensures a swift restoration process.

The workload profile with Control-M hasn't changed significantly since I started. However, in other environments, I've experienced how scalability has been quite achievable. I can execute both horizontal and vertical scaling if I understand my architecture and the design of the software. I've never had issues scaling the infrastructure or application to meet increased workloads. Currently, the workload volumes in my position have not changed drastically enough to warrant performance reviews or upgrades.

My experience with BMC support has been quite good overall. Although I faced some challenges during this particular upgrade and had to send assertive emails to the account manager and support teams, I must commend them for taking my concerns seriously and addressing them effectively. It seems the personnel I interacted with weren't entirely familiar with the new SaaS architecture, resulting in extensive correspondence with higher levels of support. I believe this experience will improve over time, and more recently, support has indeed been satisfactory.

I can't speak to what was used before adopting Control-M for this organization because when I joined five years ago, Control-M was already in use since 2011.

I'm currently migrating from on-premises Control-M to SaaS Control-M. This process involves two components: building the new environment and migrating workflows. While it's SaaS, deploying on-premises components is still necessary to run workloads. The methods for transitioning workflows from the old environment to the new have not been mature or well developed, which has posed challenges, requiring discussions with BMC to find the best path forward. Additionally, evolving security requirements have made the build and deployment processes more time-consuming than initially planned. The recent engagement with security teams shows that early involvement is essential for understanding their expectations for securing the environment, especially given the pressing concerns around cyber threats.

I can't quote a specific number when discussing cost-effectiveness in relation to Control-M because I'm more on the technical side and not privy to the commercial details. However, I do know that the project I'm currently working on will produce savings by doing platform consolidation, illustrating the kind of advantages Control-M allows me to achieve due to its platform-agnostic nature and comprehensive functionality. I can't provide a number regarding reduced spend with Control-M.

Pricing, setup costs, and licensing for Control-M aren't truly under my purview. However, I know from my current project that the costs associated with migrating to the new tool and converting it were competitive, and that the ongoing licensing costs were very reasonable compared to my current solution, which combines Control-M with another tool.

There was no evaluation of similar tools during my time, as the current upgrade was always intended to be Control-M focused. The emphasis lies on changing the delivery model to transition to SaaS architecture rather than conducting a straightforward on-premises upgrade due to the associated costs and complexities.

Reflecting on my experience with other tools, I recall being involved in a project decades ago to evaluate alternatives at a different organization. At that time, it involved comparing mainframe solutions, where Control-M proved to be a simpler tool to understand, while alternatives were often quite complex. Notably, I find the learning curve for Control-M to be less steep. I simply define what I want my jobs to do and link them together, while other tools require more complex workflow projections, such as ESP or CA 7, which aren't as intuitive in my opinion.

Based on my migration experience, I would advise anyone transitioning to a new SaaS platform to engage the security team early in the process and involve them in the solution design. Even in a SaaS model, there's a need for an architecture accommodating on-premises components. It's also crucial to recognize that moving workflows may not be a straightforward like-for-like transition; customization and potential rewrites of flows should be anticipated, especially now with the capabilities of AI potentially allowing for automation in this process.

I would rate Control-M an 8 on a scale of one to ten, simply because I don't believe in giving a perfect score. The reasons I don't give it a 10 include minor quirks such as the interface design in SaaS Control-M needing improvements. Some customizable features aren't easily accessible at the user level and can be somewhat global instead. Although the documentation is comprehensive, its search functionality isn't effective, making information hard to locate. I hope the emerging AI capabilities can significantly enhance this aspect.

My main use case for Control-M is to schedule and automate workflows.

A specific workflow that I automated using Control-M was the weekend maintenance where we bring down certain applications and conduct certain verifications, then perform maintenance and OS patching work, bring up those applications, and automate the workflows. Control-M has been very successful for this purpose and has eliminated unnecessary human hours from the workflow, providing significant convenience and automation.

The main use case for Control-M in our organization's context is to schedule the bringing up and bringing down of the market because we work for a stock exchange. We have many applications running through the pre-open, open, during, and closing periods, as well as market close, end of day, and batching. All of these involve many applications that must run one after another along with dependencies and successors. Creating workflows in Control-M has greatly improved our workflow management.

The best features Control-M offers are the convenience and the reporting aspect, along with the ability to monitor current workflows and see where things are at, where they are stalling, and where abnormalities are occurring.

For reporting and monitoring features, it is very important that we understand how long certain applications take to run and which applications are draining the memory and hard disks of our servers. Reporting helps us understand execution times, average execution times, points of improvement, and provides information for our higher management. Monitoring has been an essential part of our mission-critical applications, with a separate team utilizing the monitoring and alerting features of Control-M.

Control-M has positively impacted our organization very much because it gives all our external teams a single platform to use as a single pane of glass to view different workflows, different points of improvement, and monitor how certain processes are performing.

Automating the maintenance workflow changed things for our team and organization by reducing the potential risk of human errors and saving considerable time for team members who previously had to be available over weekends to work on these tasks. Overall, it has been a step towards advancement in our company to automate workflows.

Control-M can definitely be improved in terms of the user interface. The user interface they have been using is the same thing they have been using for the last 10 years. Control-M Web UI has not done justice to the fat client because we are forced to switch from the fat client to the Web UI. The Web UI has drawbacks, including the jobs on the monitoring page taking several seconds, from 5 to 10 to even 15 seconds, for the status to update from executing to completed stage. This is something we have to refresh the monitoring domain to see. There are a couple of inconsistencies when switching from the fat client, though I think we might get used to it over time.

Control-M's learning platforms need significant improvement. The Control-M courses are really bad, and the exams are really bad. Nobody has really spent any time improving them or putting thought into them. It appears someone made them without much consideration. I would like those things to be eventually improved.

I chose a rating of 8 out of 10 because Control-M has many things to improve.

I have been using Control-M for close to three years.

Control-M is stable.

Control-M's scalability is very good.

Customer support for Control-M is not too bad, but we wish that some of the bugs would be prioritized by the development teams so that we do not have to wait until the next year for certain issues to be resolved.

I am not involved in the pricing, setup cost, and licensing conversations as that is usually handled by my higher management.

I have seen a return on investment with Control-M, and while I do not have specific numbers, it has led to fewer employees being hands-on with the applications because of Control-M's automation.

We are yet to utilize the artificial intelligence capabilities of Control-M and would not know about the accuracy and reliability of its output.

Control-M is deployed in our organization on-premise. It is not very difficult to integrate other processors with Control-M, which is a reason why we continue using it.

Control-M is currently used extensively in my organization, but the decision to increase usage is entirely with management. Approximately 100 people use Control-M with different roles, though I am not very aware of those specific roles.

Our main business use cases supported by Control-M involve finance-related applications, though I am not very aware of the functional or application usage details.

My advice to others looking into using Control-M is to learn all Control-M related features before starting the integration because there are many things you can do. If you understand everything in your initial analysis, I think you will be able to utilize 100 percent of Control-M's features.

I rated this product 8 out of 10.

In day-to-day life, we are currently using Control-M as a workload automation tool, which provides the solution of scheduling jobs, file transfers, and cloud infrastructure jobs.

We use Control-M to get the start/stop status of a VM and to start or stop any VM with the help of Control-M and Azure VM plugins. We are also using Azure Functions and Azure Resource Group managers.

Control-M has a centralized GUI and includes many supportive plugins through cloud infrastructure and file transfer plugins.

One valuable feature is the centralized connection profile, which allows us to browse all connection profiles in a single point of view, despite a specific agent. We can browse through an EM level. The integrated Azure cloud profiles enable us to get the status of our VMs or to stop or start a specific VM without accessing the Azure portal.

Control-M has positively impacted our organization as we transition from legacy systems. We have configured around 70,000 jobs and are currently migrating all jobs from the legacy system to cloud infrastructure. Control-M helps us manage all file backup processes and batch process management, which people previously performed manually. By using Control-M, they are now able to perform these activities in an automated manner.

Control-M has definitely reduced the time required for job execution. Since we had 70,000 jobs that people were performing manually before Control-M, they are now specifically scheduled and available as on-demand jobs. They can run the jobs whenever they want, which has helped us reduce the time span and the manual efforts people spend on these tasks.

The configuration process could be improved as it is a lengthy, complex process. The installation might be simplified.

Additionally, the BMC support process might be streamlined. The complexity of the setup and the delayed support we receive from BMC are areas for improvement.

I have been using Control-M for seven years.

Control-M is stable. We are using the HA environment, so we are satisfied with the high availability.

In our organization, around 2,000 to 3,000 people are using Control-M, and they have many roles depending upon the application.

The BMC support process might be streamlined.

The configuration process could be improved as it is a lengthy, complex process. The installation might be simplified.

A total of 25 staff members are required for deployment and maintenance. In the organization, they are segregated as a manager, deputy manager, assistant manager, or senior supervisor.

I would suggest using Control-M because it provides vast capability with much more integration with cloud and native technology. Control-M should be considered by others looking for workload automation solutions.

The support requirements depend on the deployment capabilities. Whether it is a large deployment or a small deployment depends upon the deployment criteria and its size.

Since our company policy prevents us from using any external AI, I am not able to comment on its AI capability. We have not yet used Control-M AI, so for this reason we are not able to provide any comment on this functionality.

Some parts of Control-M are configured in a private cloud and some are in our on-premises environment. This constitutes a hybrid deployment. We have been using Control-M from the initial days. I would rate this solution a 7 overall.

My main use case for Control-M is that I am the administrator for Control-M, and I maintain the environment. As an administrator, I handle a variety of tasks and processes day to day, including maintaining the environment, patching, installing, and maintenance.

I aim to provide 24/7 support without downtime as part of my main use case. I manage the integration and sequencing of data processing across diverse data platforms and cloud environments by deploying the agent on the cloud platform as well, so I can run jobs on the cloud platform; that is how I am integrating. I have all versions available: Cube, Docker, VM, and everything.

Currently, my organization is not using Control-M to support AI or ML workloads, but I have plans.

Control-M has impacted my organization positively as a running tool. I have converted from ESP and am converting from cron and task scheduler to Control-M for all scheduling.

Since moving from those tools to Control-M, I have noticed improvements in efficiency, reliability, and how my team works. The efficiency has improved, and the API and all other new features are helping me move forward with good improvements.

Control-M's best features include APIs and support for all environments. These features help my team by allowing me to trigger anything through API, so I do not have to depend on any operation or reporting; I can do it through API.

Control-M supports all platforms, and I have to have an agent plus many plug-ins to support anything new in the market. I find that Control-M's AI capabilities are accurate and meeting expectations.

I would like to see improvements in resiliency and patching. I am releasing patches every quarter, and it seems I need to improve QA better. I am releasing point patches maybe one month after the main release. Recently, there was a release in the agent which came in 15 to 20 days, and another release came after that.

Regarding Control-M's AI capabilities, I think its governance and security need improvement. For now, it only supports AWS AI, and it has to have a hookup; only then can I take it to my company.

I have been using Control-M for six years.

Control-M is stable in my environment, with no issues regarding downtime or reliability.

Control-M's scalability is impressive. It scales well as my needs grow, and I am going to try it on the Helix internal cloud as part of my strategic approach.

I am satisfied with the customer support for Control-M. I would rate the customer support as a nine on a scale of one to ten.

Before switching to Control-M, I was using ESP, and prior to that, it was CA7, and then it was Maestro. I switched to Control-M because of all the new capabilities being added to Control-M, which includes all the AI and API capability that comes with the product.

Control-M is deployed in my organization on-premises.

Control-M has facilitated the integration of Kubernetes or cloud native workflows with traditional batch processing within my organization. I do my own implementation in cloud or cube; I do not use the native options available in VM from BMC. I do my own scripting to deploy Control-M on cube or Docker.

I have seen a return on investment with Control-M, particularly in terms of time saved. It saves so much time when I want to schedule jobs. I can generate a JSON and push it to the deploy API that could schedule the jobs; it is time-saving.

My organization uses Control-M to address compliance requirements such as SOX, audit trails, and regulatory reporting. For now, it is based on the task count, and for the MFT, it is on the node; that is how it works.

Before choosing Control-M, I evaluated other options. I evaluated Control-M with One Automic.

I would rate Control-M at nine on a scale of one to ten. Control-M deserves a nine because I have used many scheduling tools, and I recently worked on Control-M; I previously worked on Zeke, OPC, and ESP. My advice for others looking into using Control-M is that it is a great tool with all the new features available in the market.

I have used Control-M mostly for batch monitoring across three projects with financial institutions. The primary focus was on batch monitoring and checking the health of the environment in production or pre-production settings. In almost every project, we implemented jobs that check network connection from the mainframe and database health. I monitored and controlled many different processes with Control-M. I mostly used the Windows version, but I have also used the version that operates inside the IBM mainframe.

I did not use Control-M for audit preparation. I knew that other people in my company used Control-M for audit purposes, but that was not my case.

In the mainframe part, Control-M works smoothly and perfectly with no lag or problems. However, in Windows, it certainly has some problems at times. I do not remember which version I was using, but it has a lot of lag when you try to click for rerun. You click it, the job starts working, but the display does not show the information that the job is already running. Sometimes it does not have the option for confirmation, so it was annoying to click run and not see the job working or doing anything because of the lag on the screen. Sometimes you click rerun or click the rerun button twice, so you get a second run that you did not want, but because of the lag, you get the second run. This was the most annoying thing in Control-M.

Regarding what I would want improved, the tool in the mainframe would be very helpful with a report that you can follow directly in the path of the jobs. In the Windows version, you have all the graphics, so you can follow your path and see when it is going to run and how it is scheduled, which allows you to work more easily to change, hold, or make any movement in the job. In the mainframe version, it is complicated sometimes to follow the same path. You need to consult a lot of documentation for the planning of how the jobs are done, but you cannot find this information directly in Control-M in the mainframe.

I think the biggest thing I would like Control-M to improve in the mainframe is some way to track or follow the jobs a little bit more graphically, similar to the Windows version. I know it is very complicated, but that is my feeling.

I would appreciate Control-M being more complex when it has more features, such as when you are making the planning for the batch. The more complex your tool is, the more you can do for certain parameters or to put a job exactly between two different processes. You can accomplish more with a more complex interface and tools. I think probably other job scheduling tools get a little bit easier or offer a more light version and a simplified version, but I think not. At least for me, when I was making the planning or checking the batch, the more complex and different tools for the different situations that you have in the batch is the better.

I have been working with Control-M for about five years.

There are a lot of stability issues, and it depends on the platform. In the mainframe part, Control-M works smoothly and perfectly with no lag or problems. However, in Windows, it certainly has some problems at times. I do not remember which version I was using, but it has a lot of lag when you try to click for rerun. You click it, the job starts working, but the display does not show the information that the job is already running. Sometimes it does not have the option for confirmation, so it was annoying to click run and not see the job working or doing anything because of the lag on the screen. Sometimes you click rerun or click the rerun button twice, so you get a second run that you did not want, but because of the lag, you get the second run. This was the most annoying thing in Control-M.

I do not have a certain response for how scalable Control-M is because every time I have needed to use it, it covers everything and I think that is great scalability. However, I have never been in a situation in which Control-M is insufficient and I need a more powerful tool for scaling or anything related to that. So, at least for my experience, it is great.

In the two projects I have been working on, the maintenance was from BMC directly. I actually did not know that other people can give maintenance or support directly to the tool because I have always used it through BMC, which actually provides great support. It is very useful and they respond very quickly, so I never figured out that we would probably need local support.

In the last project, we had a BMC group who was in charge of us or they responded directly to the last enterprise, in the last company where I was, so it was very helpful. If we had any problem regarding Control-M or BMC, they have a team in the company ready to support us.

I used another one, but I do not remember the name quite well. It was a bit more for DBA, but I do not remember. It was also a job scheduling tool, but I do not remember the name quite well. It was about six or seven years ago.

The initial deployment was pretty easy. Even the one in the mainframe was easy to follow. The one in Windows is super easy to start working with, and if you have minimum knowledge about COBOL and how the mainframe works, you will be very easily walked through the JCL. Also, the feature that you can see the JCL in the Windows version is fantastic.

I am probably not the correct one because I have never done a quotation for the team to integrate the tool for the team or for any specific project, so I do not have information about this.

As I said, I used another job scheduler in the past. I do not remember it quite well, but right now, it is very complicated to see another job scheduler that is not Control-M because I am very used to it. Also, since it is the principal tool in the mainframe for job scheduling, it is also complicated for me to try to figure out another tool for job scheduling because it has a lot of options. It is actually a pretty good tool for monitoring the jobs or for my experience and in the areas where I have been working. So, at this moment I cannot imagine another tool right now because Control-M is actually all that I need for my work.

In the two projects I have been working on, the maintenance was from BMC directly. I actually did not know that other people can give maintenance or support directly to the tool because I have always used it through BMC, which actually provides great support. It is very useful and they respond very quickly, so I never figured out that we would probably need local support.

For my last experience, I would give a rating of ten because we have this team specifically for us. However, in the past with another project when we had to call BMC directly for support, I think that overall I would give them an eight. I would give the support an eight overall.

I give this review a rating of nine.

In our project, we are using Control-M for job scheduling and monitoring. We have data workflows and many other components that we can manage from a business point of view. We can manage processes across on-premises and all kinds of environments.

Control-M is the easiest tool available because we can accomplish what we want. We can automate processes and reduce manpower, which is the primary benefit. We can manage all workflows across different cloud environments with the help of batch scheduling, automating, and controlling jobs. It is easy to handle if you are confident with scheduling and related components. We can improve Service Level Agreements and SLA management.

Integrations are available through API and Control-M automation API to build, run, and manage workflows. We can integrate with CI/CD pipelines. As an automation solution, Control-M provides cost and licensing benefits that are good for our ownership considerations. Flexibility is also available. Job failure monitoring includes email notifications and alerts. Some users feel that the interfaces, both web and desktop, could be more streamlined.

IBM workload automation is another tool, but we are satisfied while using Control-M and comparing it to other solutions. IBM is primarily suited for mainframe integrations only, whereas Control-M is a workload automation platform where we can implement job as code and use it easily.

Deployment and agent upgrades are straightforward with Control-M. If you want to upgrade one agent version or the client version, Control-M is easier to manage compared to other tools. If we have Java capabilities, we can easily perform these upgrades. Moving to Oracle 19c would be beneficial. TLS protocols are in place while fixing vulnerabilities. TLS 1.2 and higher versions are good, and we could upgrade to TLS 1.3 for better security.

From a security perspective, communication protocols like TLS are available. SAP optimization would be beneficial if possible. Improving the overall application path would enhance the solution further.

We have been using Control-M for four years.

We experience all kinds of stability issues, and they are difficult to manage.

Compared to all other tools, the scalability is moderate only.

We are receiving all the good support we need. Even when we encounter issues with vulnerabilities that we cannot fix internally, the vendor provides excellent response times and support. Everything has been positive.

Positive

We have used other vendors in the past, including solutions from Azure, AWS, and Salesforce.

We have achieved nearly 30% return on investment.

Nearly 100 users are using Control-M in our organization. We previously used BMC Eclipse, which is a Software as a Service solution, for three years. Control-M has enabled us to transition from mainframe to the cloud environment with Azure. We are using this on a video conference basis. My overall rating for Control-M is 8 out of 10.

BMC Control-M Managed File Transfer is extensively used by our clients mainly in the BFSI sector, where we see around 5,000 to 10,000 file transfers for a few critical customers. We use it for data from their vendors who provide inputs for their end clients, including insurance agents who provide data in these files, facilitating both B2B and B2C processes.

Regarding the usability of BMC Control-M Managed File Transfer, I have been using it since 2009, and I have encountered no issues. I appreciate that no code is required, it is centrally managed through account management, validations are in place, and file transfers are tracked in an audit through which account they occur. It is one of my favorite solutions, existing since 1980, and I have written a lot of papers on Control-M, including one on my LinkedIn called 'A Leader's Journey' before BMC published the journey of Control-M.

My impressions of application workflow orchestration with BMC Control-M Managed File Transfer are that it is a fantastic tool I have been using for 16 years. I have even received appreciation from the development team in Israel, stating that no one has used the solution to the extent that my team and I have for one of our customers. The orchestration process allows easy accessibility to different applications, and it facilitates configuring with drag-and-drop functionality to set dependencies.

If you can share an email, I can provide pointers on potential improvements for BMC Control-M Managed File Transfer, focusing on customer-centric enhancements. For example, providing checksums for file metadata in reports could significantly help with file transfers.

I have been working with BMC Control-M Managed File Transfer for more than three to four years.

Regarding stability, there were some issues reported during implementation and usage by our customers, but I would rate it an eight out of ten.

The scalability of BMC Control-M Managed File Transfer is impressive due to its ability to handle large quantities of data and files, but there are certain features that could be added to make it a game changer.

From a support perspective, BMC technical support needs improvements. There are novice users needing help, but for customers such as us, who have been using the solution for over a decade, the response needs to be more timely and efficient, utilizing L2 and L3 support effectively.

Neutral

The initial setup process for BMC Control-M Managed File Transfer is very simple for us, as it requires a component to be deployed in the DMZ, from where the file gets transferred centrally to the server.

We are the premium partners for BMC products implementations, recognized as Bihom partner of the year multiple times. I have deployed BMC Control-M Managed File Transfer since 2011 for our customers, and it has been working flawlessly, with people speaking highly about the solution as the heart of their organization.

BMC Control-M Managed File Transfer has indeed helped our clients reduce IT operation costs. For instance, I implemented it for one of the largest banks in 2012, which reduced their loan process sanction from four days to just two hours, and now it completes in 30 minutes. Additionally, the timeline for the policy dispatch to insurance end clients, which initially took up to ten days, now happens in two hours.

BMC Control-M Managed File Transfer has some competitors in the market, but according to the Forrester and Gartner reports, nobody is even close to this solution, and I prefer not to use open source options.

BMC Control-M Managed File Transfer is my favorite product, so while I would typically rate it around 9.7 or 9.8, I would ultimately assign it a rating of 10.

My main use case for Control-M is to schedule jobs, particularly those that need to run overnight or on weekends. Control-M handles all cron job scheduling requirements for our organization.

A specific example of a job I have scheduled with Control-M involves our microservice architecture for an asset management company. We need to retrieve information from different third-party environments through APIs, and all of these operations are scheduled to run overnight, with some jobs executing over the weekend. Control-M manages the scheduling of these jobs and facilitates the fetching of that information into our databases.

Beyond scheduling, Control-M provides monitoring capabilities for our microservices. If an API goes down or if a job takes longer than expected, Control-M alerts us to these issues, which has proven invaluable.

For managing the integration and sequencing of data processing across diverse data platforms and cloud environments, we have built a separate integration layer. Through that integration layer, we leverage Azure's built-in capabilities to handle these tasks.

Regarding AI or ML workloads, we are not developing anything separately in this area. Most of our work depends on large language models. We integrate with multiple LLMs to generate meaningful responses for RAG-based implementations and agent-based solutions we provide. If Control-M aligns with these capabilities, we will consider it for these purposes.

Control-M has facilitated the integration of Kubernetes and cloud-native workflows with traditional batch processing within our organization. For any containerized or Docker implementations we consider, Kubernetes support is essential. Control-M serves as the main player in providing meaningful solutions for our microservices.

Control-M has positively impacted our organization by eliminating human intervention during job execution. Otherwise, we would need an SRE to monitor all these jobs, and even with manual intervention, there is always a risk of unintentional mistakes. Given that the information fetched through these jobs is critical to our operations and essential for our website to function, Control-M provides solutions that eliminate this risk. As a result, Control-M delivers very positive results, our site remains stable, and the information we display is highly reliable.

Regarding measurable metrics, we have saved four hours of manual monitoring effort per working day.

The best features Control-M offers include handling overnight work. Once you schedule a job with Control-M, it takes care of monitoring and provides timely notifications. Additionally, Control-M handles weekend scheduling effortlessly, allowing us to schedule tasks such as database reindexing and manage them automatically.

The dashboard and analytics capabilities are particularly outstanding. The dashboard provides greater visibility into which jobs require attention and how long each is taking. This information is invaluable when presenting metrics to the boardroom.

Control-M's AI capabilities demonstrate strong alignment with our organization's AI governance policy. Control-M safeguards our enterprise data and ensures it does not leave our environment. Only LLMs are used to generate meaningful responses, nothing beyond that. Control-M does not share our data and adheres to all GDPR and compliance requirements.

Regarding Control-M's AI capabilities, the accuracy and reliability of output have been consistent for us. While depending entirely on AI cannot guarantee 100% accuracy, Control-M delivers accurate results approximately 97% of the time.

Control-M's dashboard and analytics features help us address compliance requirements such as SOX, audit trails, and regulatory reporting.

Control-M could be improved by implementing AI features that read changes from the source side. Rather than simply generating exceptions, Control-M could suggest solutions based on source and destination dependencies. For example, if a field has changed on the source side, Control-M could recommend adding that field to the destination to resolve the issue.

Instead of requiring a step-by-step process, Control-M should enable a prompt-based approach. Everyone is moving toward prompt-based solutions, so enabling users to schedule jobs through prompts would align with current trends.

For now, Control-M serves all our needs, and if anything changes, we will provide additional feedback.

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

Control-M is very stable.

Control-M's scalability is excellent. It consistently adapts to new solutions and new integrations, and it can integrate with AI workloads as well. Control-M is definitely scalable.

Control-M's customer support is very good. Beyond the customer support, the community support is exceptional. Blog posts from the community and community responses to any questions posted are tremendous.

I would rate Control-M's customer support a nine out of ten.

We have not previously used a different solution. Earlier, monitoring was entirely manual, and cron jobs ran through different database schedulers, but we did not use any other dedicated solution.

I have seen a return on investment with Control-M. Overall, any employee working on integration services and monitoring jobs, particularly SREs, saves four hours per working day. This represents almost half of their work effort saved. The return on investment is very strong, and within less than a year, we observed these metrics, which is the reason we continue using Control-M.

Before choosing Control-M, I did not evaluate other options, possibly because it was not on my radar at that time. I started using Control-M from the beginning two years ago.

I would rate Control-M a ten out of ten.

I choose a ten out of ten because Control-M is essential for our daily job operations. Previously, we faced many production issues. When a job failed and an SRE did not monitor it properly, the resulting data unreliability created major escalations. Control-M prevents these issues and ensures data reliability, which is critical given that millions of users visit our website.

My advice to others considering Control-M is to definitely evaluate it. Instead of looking for other solutions and services, Control-M can handle many day-to-day tasks, and it is reliable and scalable.