Control-M Reviews

My main use case for Control-M is for production job scheduling and monitoring purposes.

For live incidents, I use Control-M for production job monitoring. If a job fails, I backtrack to find where it is failing and for what purpose, check that, and then rectify it. I also check dependency jobs to see if the data was loaded correctly.

I mainly use Control-M for dependencies, to see where the job is failing and why it is failing. I analyze that, and then after that, I solve that error and rerun the jobs again.

I rely on Control-M to make sure my data jobs and processes run smoothly between different platforms, such as my on-premises systems and my public cloud.

I am using Control-M at the production level.

I use Control-M for security purposes to address compliance requirements. I am not using audit, but security is very much needed, so I am using it for that.

Control-M has helped me save time and reduce errors, as I am just monitoring the jobs. If any error occurs in the job, I can see it directly there. Otherwise, I can read the job name, then I go to my ETL and with the job name, I can search for it. I can find that job and in detail, I can get that error in the ETL. In case I do not get the full details in Control-M, I can get the session log in my ETL and then I can resolve it easily.

The best features Control-M offers are the scheduling part and dependencies are very easy to find there. Dependency jobs are easy to find there. In my ETL tool, I cannot find dependencies very directly, so in Control-M, I can find them very easily.

The scheduling and dependency features help me in my daily work by being more efficient because manual runs are very difficult. If I schedule it for a specific time, it automatically triggers the job, runs the job, and loads the data into my tables in the databases, which makes it easy. If there is any error, it throws a notification email, and then I can find it easily and I can go and resolve that.

In the future, I will see how it goes.

As of now, there are no improvements needed for Control-M. In my three years of experience, I am good with it, and whatever I require, I get it.

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

Control-M is stable.

Control-M's scalability is very high, and the interface is also very easy to check.

I previously used only monitoring and dependencies, and I am going to check on all that, which is very helpful.

Time is being saved with Control-M, without question.

I checked other tools previously as well, but Control-M is very easy and convenient to use.

On a scale of one to ten, I would rate Control-M as an eight to nine.

I choose a nine because I am good with it. Regarding the AI implementation that is there now, I am looking forward to seeing that, and that is why.

Regarding Control-M's AI capabilities, I think its governance and security are very much secure. That is the thing.

I would recommend Control-M. It is very easy and effective. From a cost point of view also, I think it is a very good choice, and I can convince them to use it.

I am using Control-M mostly to automate banking jobs and monitor the jobs at the same time. Since I have been using it for the last year, it is mostly an operator's job as a Control-M operator. What I do is monitor the jobs, and if there is any job delay or job failure, we go through it. I use it to find where the problem is, why the job is failing, or why the job is getting delayed. Then I work with the application team to sort it out.

My favorite feature is in the web version, since I have been asked not to use the UI. In the web version, what I mostly appreciate is that when a job is waiting for any reason, whether a prerequisite is not met or the in-conditions are not satisfied, I can simply go to the waiting info section. There is an option where I can just click on it and I get to know every detail, such as why the job is waiting, whether the prerequisite jobs or the earlier jobs are executing or have failed. So in just one click, I get to know everything about that particular job, including why it is waiting and where the job flow is getting stuck. That is my favorite feature.

I heard that Control-M is going to discontinue the UI version. When I search for a job and ask for the neighbor jobs, such as the predecessors and the successors, it does not give a sequence; the jobs are not in sequence. I have to do some settings and navigate through multiple screens and clicks, and then I find out the information. If I could get the jobs in sequence by default, that would help me a lot in troubleshooting and understanding the job flow, particularly which job is going to run first and which job is going to run after that. This is the thing that I mostly do not appreciate.

I have been using Control-M for the last one year.

In the last year, I have never seen Control-M environment lagging or crashing. Sometimes some servers are down, which is a different matter, but I do not have any experience in the last year where Control-M was down. Failover is a different consideration.

Control-M might be scalable because I remember in April of this year, I attended a roadshow for Control-M and there were very new features, including AI integration. All these features have already been released in April of this year. Seeing those features in Control-M, the AI integration and how easy it has made the job is impressive. For example, in simple English, I can type it down in my environment and jobs get started. With the help of AI, I can make scripts, and if a rerun is required for any particular job, I can do that just by using very simple English language. I would say it is scalable.

I have used multiple tools, but nothing similar to Control-M. I have used Dynatrace, I have used Splunk, I have used SCOM, but nothing similar to Control-M.

It was not very difficult to learn how to use Control-M. It was something new for me, a totally new thing. It was not that difficult. I started learning things, and it hardly took me three months from scratch to understand how things are working in Control-M.

Control-M was already deployed when I joined and started working.

My main use case for Control-M is for data warehousing and AWS cloud. I use Control-M to run data pipelines and workloads, and I also use it for invoice generating for business warehouse and data warehouse.

I manage the integration and sequencing of data processing across diverse data platforms and cloud environments using Control-M.

We are already using Control-M to support AI Ops, as we implemented AI Ops integrated with Grokstream, and it is going well.

We have not tried integrating Control-M with Kubernetes or cloud-native workflows, but we are going to implement it very soon.

We do not use Control-M for SOX audits; we just provide evidence to the SOX team.

Control-M has positively impacted my organization. As soon as we implemented job as code, the AWS teams were very happy about the implementation, allowing them to deploy workloads as fast as they can, which is very helpful for all IT departments.

Earlier deployments used to take a couple of days, but now we can deploy code to production within fifteen minutes and keep it running.

The best features Control-M offers include job as code, which is very useful for employees who can quickly schedule their batch jobs and deploy to production.

The job as code feature has helped my team significantly. As Control-M admins, we also work as DevOps engineers, allowing us to implement the solution as quickly as possible and deploy to production using job as code and DevOps methods.

Control-M provides very good governance and security, which is why everyone chooses Control-M.

Control-M's accuracy and reliability of output are very dependable, with accurate data, making it the best in the market for optimal utilization of its features.

Control-M needs improvement regarding the licensing part. BMC should see how users are utilizing it and help us save money to optimize the usage of Control-M.

I have been using Control-M for over ten years.

Control-M is stable.

Control-M's scalability can scale dynamically.

Customer support for Control-M is very good.

Since the beginning, we have been using Control-M only.

I have seen a return on investment with Control-M through time saved and fewer employees needed.

Our business management team reached out to BMC directly, and they purchased Control-M.

The pricing and licensing for Control-M is a bit expensive, and BMC should help customers reduce costs if they are not using the jobs or licenses they have.

I would rate Control-M around nine point five on a scale of one to ten.

I choose nine point five out of ten because Control-M is the number one tool in Gartner for workload automation and orchestration.

I would advise others looking into using Control-M that it is a very good tool, and BMC provides very good support.

My main use case for Control-M is job scheduling and automation. I want to automate Windows batch files, Linux shell scripts, applications, and all those things we want to automate.

I do not have any additional plans for using Control-M to support AI or ML workloads.

I do not expect any challenges regarding Control-M facilitating the integration of Kubernetes or cloud-native workflows with traditional batch processing.

We have not implemented Control-M yet, as we want to take full benefit from it.

Once Control-M is fully implemented, we are looking for time-saving and automation so that manual errors are prevented.

Especially in the IT infrastructure sector, I expect to see the biggest time savings using Control-M to start server backups, automate tasks, and after successful completion of backups, notifications take place to alert the end-user or the IBM IT team to investigate if there is an issue.

Regarding Control-M's AI capabilities, I have expectations about its accuracy and reliability of output. For example, in traditional methods, an IT engineer manually checks logs, whereas with Control-M's AI, the AI helps analyze jobs, outputs, and logs.

I plan to use Control-M to address compliance requirements with features such as audit trail, user activity tracking, recording actions performed by users, job change histories, and security features.

Based on my exploration so far, improvements to Control-M are not regularly needed.

I would like to add that regular updates and enhancements would be beneficial.

I am exploring Control-M and have been using it.

Control-M is stable, and I have not noticed any issues with reliability.

I expect Control-M to handle increasing workloads and more complex processes as my needs grow.

Currently, our employees are managing it manually. We want to automate, which is why we are moving forward with Control-M.

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

Currently, it is offsite, so we are not on the cloud, but we want to implement on-site and then move to the cloud afterward.

Once Control-M is implemented, I expect that both money and time will be saved.

We have not purchased Control-M yet. We want to see how it functions regarding the licensing part before making a decision.

I would rate Control-M around seven out of ten based on my exploration so far. I chose a seven for my rating because when we depend on Control-M, it takes care of automation, reduces our time, prevents manual errors, and accomplishes all that we need.

We primarily use Control-M for our client. Due to a signed NDA, we cannot reveal much of the information. We have a couple of client-sensitive use cases, and we extensively use Control-M to majorly automate things on the client environment.

In terms of time efficiency, Control-M is quite useful, as it saves a lot of time due to all the automation and scheduling jobs. While I won't have an exact number, it's approximately a couple of hours in time savings.

The type of automation which Control-M provides is quite good, as it helped us automate multiple things on a client environment. Majorly, it's about handling tasks and scheduling jobs which are client-sensitive, as per the particular use case of the client. We do schedule the jobs for our client and automate things accordingly.

Since we are already in the AI era, we could add more AI-extensive features to Control-M. I would like to see something from the AI perspective to make it more efficient.

We have recently started using Control-M.

Control-M is deployed on the public cloud, probably one of the giant cloud providers, though we cannot name that provider. We have deployed it on one of the major providers.

We do have a separate deployment and runtime team who is responsible for end-to-end deployment and the related activities.

I would rate Control-M around 8.5. We are using Control-M and it is helping us improve efficiency and reduce time, so it deserves a good rating. However, it somehow needs some more improvement on the AI side, so I deducted two marks for that.

Governance and security are major aspects whenever we are working on any environment, especially when we are working extensively on a client environment. We do have clients from various industries. For example, if we have a healthcare client, they have HIPAA compliance. If we have a client from somewhere else, they have GDPR compliance. There are many compliances, so compliance and security are major aspects.

Control-M is quite reliable.

I'm not 100% sure, but probably if there is any future need, then we could extend our usage.

I'm not sure about Control-M because it is only for another team member to use. What I can tell you is it is very helpful to use Control-M in the mainframe platform because we can run and schedule many routines at the same time and we can create some scenarios. By these scenarios, we can run a lot of routines. We can build some scenarios and maintain this in the real world in production environment.

Regarding Control-M, what I can mention is I only know about the platform used nowadays for our team, and I believe it is a very archaic model. Probably there is another interface, better solutions and maybe with an interface easier to use on a daily basis. But what we have nowadays is something not so familiar for people that don't have a complete understanding about how to use.

Regarding Control-M, I'm not the one to use it directly, but I know it is a very powerful tool and very dynamic and helps us a lot on a daily basis. I believe the tools to schedule a routine in Control-M are very helpful. On a daily basis, we can use a lot without some problems. It's very easy to use.

It is necessary to take some time to learn Control-M. More or less a month to be familiar with the first steps. As you continue, you will increase your understanding about the feature and probably need more or less six months.

People in the first initial step need more or less one month to be familiar with some commands and start using Control-M tools. To feel comfortable using it on a daily basis without support, it takes more or less six months. This is the appropriate time to be able to use Control-M.

I have used another tool related to Control-M, but it is not so similar. It is something more related about running only individual routines one, two, or three routines at the same time. It is Topaz. It is a tool directly connected to the mainframe as well, but it is more destined to developers to build routines and programs and run these sources. It is not the same, but it is what I use on a daily basis when I need to run routines.

I have never experienced any issues such as lagging or crashing with Control-M.

The scalability of Control-M is adequate. We need to follow some steps to add some routines, but that's acceptable. What I know about this feature is it is reasonable to use when you need to add some new steps, schedule some new routines, or add some files. It is pretty nice and not a big deal to use.

I didn't need to contact the technical support or customer support for Control-M.

Positive

I would rate Control-M overall an eight out of ten. I believe you can put my company name as entity data. I'm a software engineer.

We are currently exploring AI-related applications, specifically examining Control-M for AI with captive insurance accountancy and HR-related work. This exploration may prove interesting as we look forward to applying it for captive insurance and HR-related tasks, as well as for research activities within our company.

We manage the integration and sequencing of data processing across diverse data platforms and cloud environments with an internal application.

We are still exploring Control-M for AI and ML workloads.

We have not used Kubernetes so far with Control-M.

Control-M is not currently in place to address compliance requirements such as SOX, audit trails, or regulatory reporting, and we still have to standardize.

Control-M has positively impacted our organization during the exploration phase by helping us identify risks in our captive insurance compared to earlier risks. We can identify defaults, which has brought us some benefits.

There is still a lot more to explore, and we have been given all database information, such as expiry dates and customers who have defaulted. Background verification and identifying future capabilities, as well as analyzing all these features, have helped us understand repayment risks and reinsurance possibilities.

The best features we see in Control-M right now mainly focus on AI, and we are still exploring Control-M. We need to look into that more.

As of now, everything is satisfactory with those features, but we are still exploring, so we need to see the pros and cons and understand that this exploration might take some time. In the future when we come across certain aspects, we may add to these thoughts.

I believe the security in Control-M is good, and the governing securities are all satisfactory as of now.

The accuracy and reliability of the AI output within Control-M are very good and reliable.

Almost everything is satisfactory with Control-M, and as of now, we find it useful. However, we are still in exploration mode, and AI has a lot to explore as well. Control-M could still come up with many controls going forward, especially in our domains of risk insurance and HR-related tasks.

I have been using Control-M for two years.

Control-M has been stable in my experience, with no problems or downtime.

The scalability of Control-M is good so far.

The customer support from Control-M is very good, and I have interacted with them. I would rate the customer support as a nine on a scale of one to ten.

I have not previously used a different solution before Control-M.

We have not seen a return on investment yet.

We have not evaluated other options before choosing Control-M; we started using it directly.

I would rate Control-M around eight. I chose eight as my rating because there are still some features we are exploring, which is why I have not rated it higher.

In the future, after it becomes fully scalable, we can consider a higher rating. My advice to others looking into using Control-M is that almost all features and everything is good.

My main use case for Control-M is batch monitoring.

I use batch monitoring mainly in production and non-production environments.

We manage the integration and sequencing of data processing across diverse data platforms and cloud environments with special applications that help them communicate with each other because of our heterogeneous environment.

We are planning to use Control-M to support AI or ML workloads; currently, we are using on-premise Control-M for job scheduling only.

Control-M is mainly used in my organization for regulatory reporting to address compliance requirements like SOX and audit trails.

Control-M has positively impacted my organization by improving productivity and tracking.

Productivity and tracking have improved with daily jobs like getting the status, pulling data, and transmitting data from client to client; those tasks are scheduled overnight, and there is a morning consolidated mail to inform if there are any issues with overnight job scheduling.

Control-M's best features include job scheduling, monitoring, troubleshooting, and visibility.

I rely on all those features because in order to lead day-to-day operations, all these features are important to manage the environment.

I want to add reporting because that is something we can look back at.

In terms of Control-M's AI capabilities, I think its accuracy and reliability of output are good, especially with Control-M Guardian for AI-specific offerings, and I think new features are added with global availability addressing some of the issues on security and vulnerability levels.

Control-M has facilitated the integration of Kubernetes or cloud-native workflows with traditional batch processing through various plugins or features that integrate with different tools and technologies; one of them is Kubernetes, which is widely used and for which Control-M is well equipped to cater to the needs.

Control-M could be improved, but I have no frustrations because AI features have been included and continue to be added, which is great.

Regarding Control-M's AI capabilities, I think agentic and governance is an important need of the hour, and Control-M has some of the features that address those issues, so we are looking forward to the maturity of the product because its AI adoption is at a rapid pace; it may require more time to reach a mature level.

I have been using Control-M for three years.

Control-M is stable.

In the past we used UCS, which is a job scheduling tool plus monitoring; now we are using Control-M.

I have seen a return on investment from using Control-M; I think it has resulted in fewer employees needed and money saved, which is why we continuously use it.

I am a technical person, and the pricing, setup cost, and licensing are managed by our client.

Before choosing Control-M, I evaluated many other options, but I do not want to name them; however, the majority of people said to proceed with Control-M, just because of the offerings.

On a scale of one to ten, I would rate Control-M a nine.

I choose nine out of ten for Control-M because maybe I need to explore more on all the features, as some features are not applicable.

My advice to others looking into using Control-M is that it combines a job scheduler with AI features, keeping up to date with industry trends and requirements; it has a proven track record and longer usage in the industry, so the maturity developed in this product is high; all users or prospective customers can use Control-M without any doubt.

My main use case for Control-M includes batch monitoring, batch operations, and batch scheduling.

For monitoring and scheduling, I support 24/7 with multiple customers in a shared environment, where I monitor all respective customer jobs around the clock and action the respective alerts if they have failed, are waiting, or have any dependencies. I check and perform the necessary tasks, and similarly for job scheduling, I create, modify, or delete jobs based on requests, and in case of failures, I investigate and perform the respective tasks.

Everything about my main use case is already covered by Control-M.

Control-M has positively impacted my organization as I support a shared environment and multiple clients. The product could help more if add-ons or enablements were available for free, which could assist customers in understanding the benefits.

However, I have been working with Control-M for more than 15 years, and I have observed continuous improvements that make me very satisfied with where the product stands now compared to 15 years ago.

In my experience with Control-M, the best features include Control-M, BMC Batch Impact Manager, and Control-M Manager, which helps determine the impact of respective jobs, how long they will take if they are running, and any impacts from previous days.

The Impact Manager specifically helps in my daily operations by predicting when issues will arise and how long jobs will be running, while also checking for any dependencies during the job's execution, making it easy to monitor.

Control-M provides excellent features that help significantly.

To improve Control-M, I believe that with AI capabilities, if a job fails, it could show what the error is, how it can be rectified, and whether there is any manual intervention or systematic process that can automatically fix the failure. These features would significantly reduce manual efforts.

Having an automated report for the last 24 hours would be very helpful, as it would verify job statuses and identify issues at both the Control-M job level and Control-M agent level, making it similar to a good morning report.

I have been using Control-M for more than 15 years.

On a scale of one to ten, I would rate Control-M an eight.

Regarding Control-M's AI capabilities, I am satisfied with the enabled governance that is already in place.

However, I believe that if utilized effectively, it could enhance understanding significantly.

My main use case for Control-M jobs is fetching data from other applications to our main Oracle application and triggering the jobs. The feedback files and jobs will move to archival, and that should indicate successful completion, meaning the applications and endpoints will place the jobs and files on archival successfully.

Control-M helps ensure that everything moves smoothly from those applications to Oracle and then to the archive because I find it user-friendly. The user interface is completely excellent and very useful.

I manage the integration and sequencing of data processing across diverse data platforms and cloud environments with Control-M, and I find it good.

We have a few processes in place to help with integration and sequencing, and I review all of that.

My organization is currently using Control-M and we are planning to use it for AI or ML workloads. I do not have a specific project or workflow in mind for using Control-M for AI or ML workloads yet; we are still in the process of learning those things and implementing them.

Control-M has facilitated the integration of Kubernetes or cloud-native workflows with traditional batch processing within my organization by fixing issues with batch processing workflows that failed due to SFTP connection problems. We upgraded to Azure cloud SFTP recently, and so far, it is performing well.

My organization uses Control-M to address compliance requirements by relying on regular audits.

Control-M offers a lot of features. The features that stand out to me or that I use the most include scheduling and monitoring; everything is very user-friendly and these features are excellent.

Control-M has positively impacted my organization by improving efficiency. It has improved things through excellent monitoring and service. If something goes wrong, we will re-trigger, rerun, and reproduce the issue to resolve it.

Control-M is performing well so far. I will monitor additional features and analyze them and provide feedback in the future. I do not find any improvements needed for Control-M that I have not mentioned yet; there is nothing I wish was better.

We have been using Control-M for jobs, data inputs, outputs, and data management for the last two years.

I rate Control-M a nine out of ten. I chose nine out of ten because I want to monitor more things and will update regarding that.

Regarding Control-M's AI capabilities, I think its governance and security are excellent. The accuracy and reliability of Control-M's AI output have met my expectations. AI and governance will be implemented completely, so we will see more features and AI tools come to Control-M.

I definitely recommend looking into using Control-M.