Control-M Reviews

I primarily use Control-M for application and data workflow orchestration, workflow automation, and batch processing. My tech team uses it as well.

We are currently working on an API integration project where we have used Control-M for automating workflows and for batch processing of data moving from one system to another. We built that API integration using Control-M.

I think that covers what we have actually used Control-M for primarily. We are exploring other capabilities of Control-M, but that is what we have used so far.

Control-M has actually helped us with efficiency primarily. I'm sure in the long run, it will definitely help us in reducing some costs or controlling some costs. The improved efficiency due to automation is the best thing for us right now.

In the API integration project that I mentioned, we have saved roughly 20 percent of the time, or basically the process has improved by 20 percent in terms of efficiency. Previously, the bulk migration used to take roughly an hour, and now we can do it in 45 to 47 minutes. We have achieved a 20 percent saving so far, and we are trying to improve it.

Some of the features I can think of in data workflow are the native cloud and big data integration, including integration for Snowflake and Databricks, as well as built-in connections for AWS, Azure, and Google Cloud.

Regarding workload automation, features include DevOps integration, Jobs-as-Code, and hybrid and multi-cloud provisioning.

The native cloud and big data integration stand out as the most important thing because it gives me built-in connections for tools such as Snowflake and Databricks.

Two things that come to mind are both related to the execution guardrails for AI agents. One is the MFT server support that Control-M features, which is the native MFT support acting as a standardized connection layer. Another feature is the intent-based access control, where every action initiated by an AI agent is bound by strict role-based access control, or RBAC. These two are great features for securing both the governance and security aspects of the product.

For our use case, everything is there, at least for now.

I have been using Control-M for three to four months.

Understanding the product properly before using it would be better. You should first understand or map your requirements into the solutions or features provided by the product so that it will be much better for you as a user as well as for Control-M as a product.

Matching your requirements with the features provided by the product will be a win-win for both of you and will be a great experience for you as well as for the product builders to have a relationship between.

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.

We are partners with Control-M, and I believe we have recently elevated to platinum status, though I am not entirely certain about that partnership level. We are partners with BMC, including both Control-M and Alex.

What I appreciate most about Control-M is the automation steps of how things are automated and scheduled. Our relationship with BMC is more transformative than transactional since we have been BMC partners for a long journey, almost more than ten years. We work closely with all partners on the BMC side and actively engage with all stakeholders, not just for the sake of the client, but we are closely involved with the respective stakeholders of BMC.

Regarding areas for improvement with Control-M, I would need to go into the depth of detail and provide specific feedback. If you share a form, I will have corrections and additional input from our team. Using the code option is better overall, but I believe better capacity planning can be done.

Unfortunately, to provide meaningful feedback regarding room for improvement, we need to identify something Control-M can do better or something they can add in a future release.

Throughout my career, I have been using Control-M for almost more than six or seven years. From my perspective, I have recently taken on the lead role for Control-M for two years.

Regarding stability, I rarely observe any lagging, crashing, or downtime when I use Control-M. That very rarely happens based on memory space or statistics. With respect to resolution management and ticket management systems, BMC always responds first. We would appreciate improved feedback on solution response time, which would be beneficial for us, the clients, and BMC as well.

I believe Control-M is scalable. Control-M is indeed scalable, and technically I need to verify this with the correct details and share them with you.

Control-M does not require any maintenance on my end, such as updates. I think BMC is doing a fantastic job with tenant updates and periodic knowledge updates, whereby they provide training sessions as well. Recently, BMC has separated into two categories, and Control-M also has a separate wing. The tenant and the helpline are very good, but we have to continuously review the material released by BMC.

I have not used any alternatives or similar solutions to Control-M. We have heard about some products, but we are not using any alternative. Alex ITSM is a different platform. Control-M is the only solution we are using with respect to the client side.

Regarding the initial deployment of Control-M, I need to check with my solution architect because I am not involved on the daily operation side. From a high-level perspective, I observe that the initial setup and most of the configuration and other aspects are done, but better port level and prerequisite level automation or a more comprehensive checklist provided to partners would make the setup easier rather than complicated during the initial stage.

Our technical team members continue to contact our respective project stakeholders, though this is not from my side. I am overseeing the Control-M use overall.

Control-M is scalable and does an excellent job overall. The approximate number of people using Control-M is around fourteen or more. I am not the appropriate person to ask about the pricing of Control-M; a direct person from the sales team would have direct visibility of the pricing side. Regarding how well Control-M integrates with other solutions in our stack, I do not have the correct technical answer. My rating of Control-M overall is eight point five out of ten. The two reasons for not giving a ten out of ten are that the initial setup configuration can be better, though our team is working on it, and everything else is good. Do you have any mechanism to route questions about technical integration to the technical person leading this area?

My use case for Control-M is orchestration.

The best features of Control-M are that it is easy to use; even a non-technical person can learn it in a couple of days with normal documentation and a few videos. Just two days should be sufficient to pick it up. Users do not need to be technical to use the tool, and it is easy to implement and deploy.

Integrating Control-M with other technologies for DataOps and DevOps is easy; we export the jobs we create in a non-prod environment and, on the runtime, we know what variables need to be replaced, and we replace those variables to deploy to prod since Control-M is just an XML file, which is very easy to search and replace.

Control-M is extensively used in our projects. When we start a project and it becomes an enterprise tool, we are required to use it. If there are any failures, we can tag them with an incident, making it easy for maintenance, monitoring, tracking, and deployment since everything is in one place.

The area that has room for improvement in Control-M is a better dashboard. For example, sometimes we have up to 100 Control-M jobs, and there is no dashboard to know how many jobs are in progress, completed, or waiting for files. That requires us to create an additional dashboard on top of the Control-M metadata.

I've been using Control-M since I started my career in data warehousing in 2011 or 2012 since there are more jobs, more tables, and more data loads in data warehousing.

When it comes to stability, I would rate it as good, an eight out of ten.

The scalability of Control-M is good; we create different servers for different projects instead of putting all jobs on one server, and I would rate scalability as an eight out of ten.

The technical support from Control-M is good; I normally never ask the Control-M team for help as it hasn't gotten stuck for me, however, they are supportive, and I would rate it an eight out of ten.

Positive

We have used different orchestration tools, however, I am not aware of the specific tools you mentioned.

The deployment of the solution is easy.

Regarding the duration of deployment, if everything is proper, I don't see a big challenge. Normally, it takes a day if you have the code ready and follow the process and checklist.

The return on investment seen with Control-M is significant; in my experience, we run more than 100 to 150 jobs a day, and to monitor those jobs, one or two people should be enough since it triggers emails for failures and allows us to view logs within Control-M itself.

I'm not sure about the pricing of Control-M. I didn't get involved at the pricing level.

When comparing Control-M with other solutions, I see that everywhere, orchestration tools are simple, and while they come with basic monitoring and alerting functionalities, the decision to use Control-M often comes down to cost, licensing, and maintenance.

My relationship with BMC is good. 

I recommend Control-M if there are no other tools available as it is easy to use, with easy maintenance and a centralized monitoring system, alerting system, and incident creation. 

Overall, I would rate Control-M an eight out of ten.

Control-M is my main tool for business management with the team. We have multiple healthcare devices that need to be handled between multiple people, and we use Control-M for the same purpose with our end-user customers as well.

We are planning to use Control-M with AI agents to enhance the efficiency of our throughput to a better extent, thereby reducing some of the workforce. My organization actively uses the audit trails to address compliance requirements, such as SOX, audit trails, and regulatory reporting because there are many healthcare data that need to be tracked with the upgrades.

Control-M has positively impacted my organization by growing the business by a few percentages. Control-M contributed to that growth by reducing inefficiency and providing higher throughput.

The best features Control-M offers include easy and hands-free access to the main business logics. This hands-free access helps my workflow by providing easy day-to-day access, which helps us in managing the people and the business operations.

Regarding Control-M's AI capabilities, I find its governance and security to be quite secure, where the data has not been breached with the outside market. I also find its accuracy and reliability of output to be quite reliable, as I have found the data to be most accurate for my business.

I have no suggestions on how Control-M can be improved at this time.

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

Control-M is stable.

Control-M's scalability is quite good; it has been scaled from a few hundred to a thousand, and now it is going beyond those numbers.

The customer support for Control-M is great. On a scale of one to ten, I would rate the customer support a nine.

From day one, we have been working with Control-M.

I have seen a return on investment; it has been found to be more beneficial.

I rate Control-M a ten. I chose ten for my rating because I have not found any flaws, and it is quite smooth and helpful in day-to-day work.

The integration and sequencing of data processing across diverse data platforms and cloud environments have been handled by another team where I have limited knowledge. My advice to others looking into using Control-M is to try out the tool, as it is actually very useful.

I have used Control-M for scheduling one of the data load jobs that was designed to run every month. We received millions of data and a Python script was scheduled to run whenever an update was received on this particular data source.

Control-M was also used for capturing and re-running failed jobs. In cases where a job was stuck based on an entry from a table, if the table was not updated, we would re-run it.

The data is managed using Postgres and Oracle databases.

Currently, Control-M has not been used to support AI or ML workloads, though this may occur in the future.

Scheduling of the data loads is one major functionality that Control-M has been able to help with. Control-M can trigger shell scripts, and one of the Python scripts was invoked using a shell script and scheduled from Control-M. If the Python script could be directly invoked from Control-M, that would be one of the features that could have made it more effective.

Control-M is a scheduling tool and has helped us schedule our tasks that could have been manually done and missed. It saved us a lot of time by doing it at the right time in a scheduled way.

The best features Control-M offers include the ability for ad hoc job runs. We can delete, edit or modify any jobs, though in case of production, it requires a change request. The migration was done in a secured way, which avoided glitches that could happen in production.

The ad hoc job run feature saved time for my team because whenever a job was stuck, it immediately triggered the alternative and ensured that the job was completed. This saved a lot of manual effort of going back and checking.

Regarding Control-M's governance and security, they are quite secure since deployments on higher environments do not happen directly without a change request. This ensures that the development and quality assurance environment is working properly, and only then can we proceed to the production environment.

Control-M has most of the features we were looking for, but the UI could be more user-friendly for beginners. This was a challenge I faced immediately. More tutorial videos could also help.

Control-M could have been easier for beginners through a more intuitive user interface or by adding more introductory videos or tutorials.

When a particular job fails, there is a feature in Control-M where we can email notifications. However, on constant failures, such as when a scheduled job runs every five minutes, back-to-back emails would spam the group. It would be better if there were a self-healing process on the job, such as automatically pausing or stopping it if there is continuous failure, which would avoid the spam.

I have been using Control-M for a few months.

Control-M is stable.

Control-M's scalability is good.

Previously, a manual approach was being used where the Python script was directly invoked, which was time-taking and caused us to miss updates. Once Control-M was implemented, it happened in a scheduled manner, saving time and occurring on the accurate date when the update was received.

Initially, I ran into challenges with Control-M where we used the graphical user interface, and it was difficult to adapt to that UI. However, once we set up everything, it was easier to use and continue with Control-M for the scheduling purpose.

Control-M is deployed in my organization on-premises. We have an internal GitLab pipeline running for the job. This is how we migrated from development to quality assurance to production.

These would be managed by the infrastructure team.

As soon as I started working with this team, Control-M was already being used.

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

I chose eight because it could have been much better. There is room for improvement, and it did help save time on the task that I worked on.

If you are looking for a good scheduling tool or a tool that can manage many scripts, you can choose Control-M.

Control-M is a fantastic product in terms of automation. It is predominantly used for task scheduling, and it is one of the finest products available. We use it especially for task scheduling, and most BFSI customers are using it for transaction settlement. We have used it for credit card transaction settlements.

Control-M is a fantastic product in terms of automation. It is predominantly used for task scheduling, and it is one of the finest products available. We use it especially for task scheduling, and most BFSI customers are using it for transaction settlement.

There is nothing disliking about it. To be very frank, it is one of the finest products that came after IBM high frames. The use cases are predominantly in BFSI. If it could do something in manufacturing, it would be helpful where we could do automation using Control-M or something that focuses on domain-specific needs in manufacturing.

Control-M is a fantastic product in terms of automation. It is predominantly used for task scheduling, and it is one of the finest products available. We use it especially for task scheduling, and most BFSI customers are using it for transaction settlement.

It is quite easy and not a big challenge. It is pretty simple and not complicated. It just requires an understanding, and it is plug and play. Wherever we are implementing it or it is getting implemented, it is smooth and running.

Control-M is definitely a scalable product. There are no two thoughts on it. Scalability is amazing.

Any product will have some concerns. It depends on implementation and integrations and how we are doing it, and support here is decent enough.

The pricing is acceptable. It is not that heavy compared to other products like ServiceNow and SAP.

UiPath can be used to a certain extent, but Control-M has its own flavors. If it is invoice processing or if robotic automation is needed, I would go for UiPath. If task scheduling or something related to BFSI is needed, I would use Control-M. It depends on use cases.

UiPath can be used for any kind of use cases in terms of automation, but Control-M has its own flavor. ServiceNow as a platform is a great enterprise platform. UiPath has its own automation flavors, and Control-M has its own scheduling and task automation. Everything comes with its own flavors and provides output. It depends on the use case.

I am comfortable with the feedback I have provided. I rate this product an eight out of ten.