Control-M Reviews

My use case for Control-M is that I am a Control-M administrator, and I use it for everything, starting from installing, upgrading, and configuring it, to defining jobs and file transfers, so almost everything.

The best feature of Control-M is that it has a lot of integrations with different other tools; I don't think there is any other workflow orchestrator that has so many integrations. I think it has the most, and if you don't have one, you have the possibility to create the integration with whatever tool you want. That's one of the best things I've seen in Control-M.

The ease of integrating Control-M for my DataOps and DevOps processes is actually great because it has native integration with Jenkins and CI/CD pipelines, so you don't have to work hard to integrate this. In my current company, we have Control-M integrated with Jenkins, and everything gets deployed via Jenkins without accessing Control-M, which is honestly the best way to do it.

Building, scheduling, and managing production workflows with Control-M is easy. Control-M also has a feature called Jobs as Code, so if a developer wants to create a job, they don't need to have extensive Control-M knowledge. Developers can create Control-M jobs and flows via code by scripting them in JSON files, making it not so difficult.

In terms of improvement areas for Control-M, the only concern I've seen is some mismatch in documentation, which is not ideal. You may easily find things that happen on the application, but you can spend time or have to raise a case to BMC to find out what went wrong, which is something they need to improve.

I have been using Control-M since 2005, so it should be 21 years.

My relationship with BMC is transformative rather than transactional.

BMC serves as a strategic partner and trusted advisor, helping my company map migrations and deployment. They also developed an integration module specifically for our company based on our requirements.

The integration process with Redwood was easy, didn't take long, and wasn't too complex, as there weren't many jobs involved.

I rate the technical support of Control-M as a nine.

I have migrated from Redwood some time ago.

In my opinion, the deployment process is easy; it is not difficult to deploy Control-M.

The deployment of Control-M takes hours.

Maintaining the Control-M system is easy.

I honestly have no idea about precise return on investment percentages, but I think the Jobs as Code aspect and the integration with Jenkins reduce the time of deploying things and training people to use the application to create jobs.

Regarding pricing, I consider Control-M to be the most expensive scheduling tool. However, it's worth it, as you buy something powerful and complete. In my opinion, despite being expensive, it is one of the best orchestrator tools available.

When comparing Control-M with other solutions such as Broadcom or ActiveBatch, I find that Control-M has a huge integration capability with many tools that other competitors do not have, particularly the native integration with Jenkins and CI/CD pipelines, which other tools lack.

The easy integration of Control-M with DevOps tools has made the decision to keep Control-M as the main scheduler in my organization a straightforward one.

Control-M is deployed in a hybrid model in my organization.

Control-M is deployed on AWS.

My organization is an enterprise business using Control-M.

I would recommend Control-M to other users and suggest they take it. They can conduct a proof of concept, but I don't think they'll find anything more complex than this tool, which is expensive but has everything you need. I rate this product a ten overall.

Hybrid Cloud

Amazon Web Services (AWS)

I have several use cases for Control-M. I have been implementing Control-M for a long time in several enterprises in Brazil, and then five years ago I moved to the US. I started working here in the US as well. I have several use cases for insurance companies and bank companies in Brazil, and currently, I am working with Bank Charles Schwab using this tool to transfer internal files between systems and applications.

We also have user-defined transfers to move files to business partners. Overall, I have been using this solution for 17 years and have many use cases to speak of.

When I joined Bank Charles Schwab, Control-M was already implemented, but I also work on implementing Control-M from scratch.

Recently, I did an integration involving Control-M with Pentaho and Power BI. Even though Control-M did not have the plugin for Pentaho, I managed to run a data pipeline using scripts and successfully integrate it into Power BI dashboards.

In general, the ability to check all your processes in a unified view that Control-M provides is what I appreciate the most about it.

Control-M helps to integrate processes across various applications in big enterprises, making it significantly easier since you have a single point of control and can see failures and impacts on the flow.

Now, with the new plugins that they launch every month, it is easy to integrate with technologies for my DataOps and DevOps processes.

I think they are going in the direction of managing data that Control-M orchestrates. Currently, it is hard to get data from the process that Control-M is processing.

The ease of deploying Control-M depends on the architecture chosen, as some configurations can require more setup.

I have been working with Control-M for 17 years overall in my career.

In terms of scalability, I think it is good. I have seen effective escalation when necessary during issue resolution.

I have contacted BMC technical support, especially when I cannot solve certain issues myself, but I have a good handle on it due to my long experience.

The quality of support is fast during production emergencies, but it can take longer when issues are not critical, with interactions sometimes taking several days.

They have limited support for native language issues, which can create challenges for non-English speakers.

Negative

Usually, I handle the deployment myself, but I need a team to implement large numbers of jobs after the deployment.

I do not have experience using alternatives to Control-M, as I was directly presented with Control-M when I started working with workload automation.

Control-M tends to be the most expensive compared to other competitors. However, I believe it is worth the price since it delivers the most.

It requires some maintenance on my end occasionally, especially when compliance or security updates are needed.

On-premises

Other

From an administrative standpoint, we are mostly engaged in upgrading, downgrading, and fixing the issues on a day-to-day basis, such as integration with the cloud functionalities. 

My only objective is that the application is up and running 99% of the time. I will make sure that it is not down, and it does not impact my application teams. If it goes down, it has a major impact on SLA, financial, and everything, so I make sure this is up and running 99%.

Control-M improved our organization significantly. We integrated it with our DevOps model, which saved us around 32 FTEs in resource costs. We also integrated with AWS Glue jobs or other clouds, such as Informatica. It's compatible.

Control-M supports our daily IT automation tasks. It is the blood for my entire organization, integrating with every app team, including HR.

Control-M offers pretty good REST API connectivity with other solutions.

The most effective feature of Control-M is the API integration. BMC is focusing on API integration. It's also very easy to deploy and identify issues. 

I don't see any areas where Control-M could be improved because they are doing well. The support is awesome. We have a weekly connection with our BMC tech focal, so I don't see any improvement needed. Things are going well. They are already working on GenAI integration with Control-M. The only thing that comes to mind is the cost. If it could be more competitive, it would be great.

I have been using Control-M for almost 15 years.

The stability of Control-M has been good so far. However, the testing and development phases need to be more rigorous before releasing patches. I would rate it an eight out of ten for stability.

It's scalable. I would rate it a nine out of ten for scalability.

It's used across multiple departments. There are more than 3,000 users.

Control-M's technical support is very helpful. The immediate acknowledgment and solutions provided by BMC's support team make it stand out compared to other tools. I would rate them a ten out of ten.

Positive

My current organization has been using Control-M for 20-25 years, but I have used Autosys and IBM Workload Scheduler previously.

The initial deployment of Control-M was straightforward. We started with version 6.4 and are now at 9.0.21.304.

When I started, it used to take about eight hours. We are now able to do it within 30 minutes, including the downtime.

We are using it on-premise because their SaaS solution is not capable of handling our workload. Its maintenance mostly involves upgrades.

I handle the deployment. We have about five members involved in the deployment of Control-M from our side, including an administrator.

After implementing Control-M, there should be cost savings or efficiency gains.

Its cost can be more competitive. One of the main things customers look at is the cost. It's not affordable. The cost is very high, according to my customers. The licensing cost is very high, and they often consider switching to IBM Workload Scheduler or other options.

There aren't any extra expenses after purchasing Control-M. We have never paid any extra cost unless we needed additional packages, and the team was able to help us with that.

Nothing comes to mind regarding alternatives to Control-M, but I see all the features available in it since it is a leader. However, other solutions might be more cost-effective.

If you want to implement Control-M in your business, the first thing is to look at your budget. Checking affordability is key. Control-M has a lot of features, more than just scheduling batch jobs. 

The biggest lesson I have learned from using Control-M is that you need to have patience. Errors or issues cannot be resolved without developer assistance, but once you get their help, you learn a lot.

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

On-premises

My main use case for Control-M is that we are building software products. A specific example of how I use Control-M in one of my software products is in in-building solutions for data-syncing software.

I manage the integration and sequencing of data processing across diverse data platforms and cloud environments with various techniques using AWS integration and data security.

My organization is currently using Control-M to support AI or ML workloads. My organization uses Control-M to address compliance requirements with audit trails.

Control-M has positively impacted my organization by reducing errors and increasing our productivity. Specifically, it reduces errors by 60% and increases productivity by 80%.

Control-M has facilitated the integration of Kubernetes or cloud native workflows with traditional batch processing within my organization. We are working with a huge amount of data, so it is very helpful for us to integrate Control-M in our team.

The best features Control-M offers are security, data migration, and integration. These features help us significantly as we are creating software for industrial applications.

I would like to add that Control-M is easy to use. Regarding Control-M's AI capabilities, I think its governance and security are very good. The accuracy and reliability of Control-M's AI output are also very good.

I believe there are no improvements needed; everything is good. I can confirm that there are no small improvements that could be made.

I have been using Control-M for nearly three years.

Control-M is stable.

Control-M's scalability is very good.

The customer support is very good. I would rate the customer support a 10.

I have seen a return on investment. Specifically, I have achieved 60% time saved and 80% money saved.

My experience with pricing, setup cost, and licensing is good.

Before choosing Control-M, I did not evaluate other options; the decision was straightforward.

On a scale of one to 10, I would rate Control-M overall a 10. I chose 10 because it is good. Control-M is deployed in my organization using private, public, and multi-cloud. We use Azure and AWS as cloud providers for Control-M. My advice to others looking into using Control-M is that I recommend them to buy it. It is a 10 out of 10.

Hybrid Cloud

Microsoft Azure

My main use case for Control-M is maintaining the Control-M infrastructure as part of the batch mission, where I perform upgrades, migration, automation, API integrations, and cloud-native integrations.

A recent project I implemented using Control-M involved Kubernetes for an EKS site, where I enabled the additional integrator plugin on top of the EKS agent, the Control-M agent, and Control-M for Kubernetes, and we executed the project successfully.

We manage the integration and sequencing of data processing across diverse platforms and cloud environments by utilizing Control-M for Lambda, AWS Batch, Step Function, and Azure, with agents deployed in EC2 instances.

Our organization does not currently have Control-M in our roadmap for AI or ML workloads, but we are testing Control-M for AI as an initial POC, and we are looking forward to AI-native or ML functionalities for anomaly detection.

To address compliance requirements, we follow the regular TLM and end-of-life components flagged in Fidelity and use that to ensure follow-up with BMC for necessary fixes.

Control-M has positively impacted our organization by helping to modernize the batch processes and implementing event-driven workload automation.

The benefits of modernization and workload automation have included improvements in our SLA, more modernization of the application, and integration with many cloud-native solutions that help various business units achieve their outcomes.

Control-M has facilitated the integration of Kubernetes or cloud-native workflows with traditional batch processing by allowing teams to deploy the Control-M agent in their applications and modernizing it with scaling features, which has resulted in cost reductions.

Control-M offers great features, including integrations with native cloud components.

What I value most about the integrations with native cloud components are Control-M for EKS, Control-M for AWS Batch, Control-M for AWS Step Functions, and Control-M for Lambda.

Flexibility in Control-M is essential, and we utilize the Automation API for taking actions on jobs, such as holding and adding conditions, and for most of the reporting.

One area that Control-M can improve is that some cloud-native integrations do not have the IAM role, so we face challenges due to some limitations within our organization and a lack of testing from BMC's side.

We need clearer documentation on what is supported and what is not, including future enhancements and release dates, so we have a clear vision on when we can test that plugin.

Currently, we are conducting the POC in Control-M AI Jet but face challenges to implement it because it requires an AWS Bedrock account with a backend Claude Sonnet model and also MCP server, which presents some difficulties.

I have been using Control-M for the past 10 years.

Control-M is stable.

I would rate Control-M's scalability a three, as we are on-premises and have limited options for scaling.

Customer support needs improvement, as they should analyze issues before sending documentation instead of sending random documents.

My experience with pricing, setup cost, and licensing is limited, as I know what is being charged for how many jobs per year or month.

On a scale from one to 10, I would rate Control-M a seven.

I chose a rating of seven out of 10 based on how we use Control-M and its ease of use, whereas I didn't give a 10 out of 10 because there need to be improvements in documentation and understanding the various organizational requirements to provide immediate solutions.

Regarding the accuracy and reliability of Control-M's AI output, we haven't started our testing because we need to enable the AWS Bedrock account with the Claude Sonnet model, but the deployment has been done, and we have an open case with BMC that we are working on.

My advice for others looking into using Control-M is to perform intense testing before moving anything to production, as there might be known or unknown bugs that are not documented.

On-premises

My main use case for Control-M is workflow automations in my organization, primarily for in-house operations.

A specific example of a workflow automation I've set up using Control-M is for lead evaluation, where I've deployed a form that passes through multiple nodes and evaluates the fit of the given candidate. If they are a potential prospect to us, that is when we get in touch with them, or otherwise we direct them to other solutions of ours. In some cases where they do not seem to fit in any of our use cases, we decline them with a humble reply.

In addition to my main use case, after collecting raw data with Control-M, I use nodes to primarily filter and clean out unwanted information, thus helping me use it in analytical insights and train my models personally.

Control-M has positively impacted my organization, as instead of having multiple chunks and segments of independent standalone products, I have been using Control-M's suite of tools, whether it is workflow automation, agent orchestration, or data orchestration from across multiple sources. I've replaced them with a one-stop solution that Control-M offers, helping me control and coordinate all of these in one place.

Specific outcomes I can share include that earlier, when I had to look out for services and how they had been impacted, the DevOps part, the infrastructural control, and the correlated things used to take a lot of time, as I would have to log in, debug, check out the logs, and analyze and fix if anything was needed. With Control-M, it has become much easier, cutting down the time spent on issue analysis by more than 50 to 60 percent. Earlier it took me about 20 to 30 minutes per service for analysis, but now it hardly takes a couple of minutes or maybe five to six minutes at most.

The best features Control-M offers include the AIOps suite of products, which is really helpful to me, and the observability part of Control-M is also pretty helpful. It helps in investigating and checking out the current status of my deployments and my infrastructure. Having a one-stop solution for all of these is pretty helpful, avoiding me navigating through multiple services, reducing wastage of time and helping me easily identify issues. The dashboards that have been provided briefly help me as well.

Control-M's dashboards and observability features help with my daily tasks and troubleshooting, as when some service goes down, it requires me or my teammate to dive deeper and analyze if something is wrong. However, with Control-M and its centralized dashboard, it's much easier to glance through and analyze what the issue is. The ability to use Jet, the AI beacon feature available on Control-M, helps me query it in natural language and get results, thus reducing and cutting down time on resolution of issues.

Regarding features of Control-M, I think when it comes to the UI and UX, it could be made a bit more simple and sophisticated because I feel at times the people who aren't proficient in the software would find it hard to navigate through.

In terms of improvement for Control-M, I think you could make use of agents on the plane, meaning it shouldn't just be restricted to a chatbot. If you ask Control-M's AI to do something, it should rather get it done instead of prompting or navigating the user through, which would be quite helpful in non-technical layman use cases.

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

My advice for others looking into using Control-M is that if they do not want the headache of operating and controlling multiple tools, Control-M is the best solution for them.

On-premises

My main use case for Control-M is primarily in the banking sector and finance. I have used it mostly in clean architectures and Web API calls.

I can provide a specific example of how I plan to use Control-M in my banking and finance projects. I want to automate validation messages, email sending, mail send automations, and integrate with bots for customer feedback.

I also apply microservice architectures and patterns, and I have implemented multiple patterns including Saga patterns, retry patterns, and repository patterns.

My organization is not currently using Control-M to support AI or ML workloads, but we have plans to explore it in the future. Currently, I am using AI agents such as GitHub Copilot, Claude, and OpenAI models.

Control-M has facilitated the integration of Kubernetes and cloud-native workflows with traditional batch processing within my organization. I have primarily created pipelines in DevOps and have orchestration in the cloud.

Control-M has positively impacted my organization by fulfilling our requirements, and I expect to see more benefits as I continue working with it.

The positive outcomes I would like to see include improvements in accuracy and performance, which is my main focus.

The best features Control-M offers include the security components, which I believe are very important.

What makes the security features stand out to me is that I follow certain algorithms and use tokens. I want to handle JWT tokens and that type of mechanism, and I expected to integrate them more securely in the banking sector.

Regarding Control-M's AI capabilities, I think they handle security well, as they follow the setup rules.

I think Control-M can be improved, and I hope to see enhancements in the future.

I do not have anything specific in mind regarding needed improvements right now, but I may attend some conference keynote sessions and demos. After that, I will learn more and potentially identify additional ideas.

I started as a new user, so I want to explore Control-M today. I am learning and will begin including it in my projects. I want to check how to integrate and manage deployments, validation, and security.

I rate Control-M an eight out of ten.

I chose eight out of ten because there are some points I want to explore further, including keynote sessions and conference sessions.

Regarding Control-M's AI capabilities, I examine its accuracy and reliability of output.

Control-M is deployed in my organization mostly on-premises using API services, primarily with Azure as my cloud provider.

I manage the integration and sequencing of data processing across diverse data platforms and cloud environments using separate solutions with GitHub.

My advice to others considering Control-M is to explore the team for accuracy purposes or processing performance.

On-premises

Microsoft Azure

My main use case for Control-M is that it helps to schedule jobs in process chains. I needed sales data to run regularly at a certain interval of time after another job runs. Since this is event-based processing and controlling, it is really useful. Control-M helps make my job easier.

The best feature Control-M offers is job scheduling.

Control-M positively impacts my organization as it helps me run my jobs easily. It enables me to run future jobs as well, which benefits both me and my company.

In my project, Control-M saves a significant amount of time.

Control-M facilitates the integration of Kubernetes and cloud-native workflows with traditional batch processing within my organization. It helps our project run smoother and faster, and it takes less time to work on it.

For now, Control-M meets my use case requirements, but I think more AI could be incorporated to automate certain aspects of job scheduling.

I rated Control-M eight out of ten because a bit more automation could be beneficial. Nothing achieves a perfect ten out of ten, so as technology evolves, Control-M will need to change as well, which would be good for users.

I have been using Control-M for almost two years.

Control-M is stable.

Control-M's scalability helps with expanding job timing and scheduling jobs in the future, making it more scalable.

My experience with customer support was good. I would rate customer support as nine out of ten.

I have seen a return on investment with Control-M. It helps me in scheduling jobs, and from that perspective, it saves time.

My experience with pricing, setup cost, and licensing was good.

I would rate Control-M eight out of ten.

Regarding Control-M's governance and security, I think it is currently good as it is a secure platform. However, in the future, there might be different use cases that emerge.

Regarding Control-M's accuracy and reliability of output, it is currently at a moderate basis.

I would advise others looking into using Control-M to utilize it for increased scalability, security, and governance. If they have governance and security concerns, Control-M might be a solution for them.

On-premises

My main use case for Control-M is running Oracle package procedures on a daily basis to load data from an external system to Oracle staging tables. We need to schedule the Oracle packages on specific dates and times, and we configured Oracle packages in Control-M so that on a specified date and time, Control-M picks and runs the Oracle package jobs.

During my initial learning phase, I faced difficulties in understanding Control-M's capabilities, but when I started exploring, things became easier. Debugging the logs proved to be helpful.

My organization uses Control-M to address compliance requirements such as SOX and audit trails, which are involved in regulatory reports.

Control-M positively impacts my organization by improving performance and offering no-code features since no coding is involved to configure any monitoring for job scheduling.

We have developed more than 20 packages, and for running and scheduling these packages using Control-M, it is easy for the L2 support team to handle without involving developer support.

The best features Control-M offers are for debugging logs and monitoring scheduler jobs.

Whenever any issues occur, I can find the particular job log in the monitoring interface. I can see how many failed jobs and successful jobs there are, and I can monitor based on that information.

Regarding Control-M's governance and security capabilities, I find them very important for protecting an organization's data. Control-M has all the capabilities to configure secured configurations.

Control-M's AI capabilities are accurate and reliable, definitely meeting my expectations.

Control-M is overall good, and currently, there is no specific improvement needed.

I have used Control-M in previous companies for scheduling Oracle packages in a UNIX environment for loading delta and incremental data to Oracle staging tables.

Control-M is stable in my experience.

Control-M's scalability is good.

The customer support for Control-M is very responsive. If any issues arise and I create a support ticket, they resolve the issues very quickly.

I rate the customer support as 10 out of 10.

We exclusively used Control-M and did not use any other tool previously.

I have seen a return on investment from using Control-M as it definitely saves considerable time.

I would rate Control-M as 10 out of 10.

I choose 10 out of 10 for Control-M because it is easy to use and easy to explain to others, with no coding involved.

My advice for others looking into using Control-M is that if you have any scheduling jobs to run and monitor, you should definitely prefer Control-M.

On-premises

My main use case for Control-M is that I am interested in learning about middleware programming and how it simplifies tasks when using Control-M.

A specific example of a task or workflow I am hoping Control-M will simplify is workflow management, where manual processes can be reduced.

I want to explore more from this session regarding my main use case.

I manage the integration and sequencing of data processing across diverse data platforms and cloud environments with a straightforward approach.

My organization is planning to use Control-M to support AI or ML workloads based on project requirements.

My organization uses Control-M to address compliance requirements through audit and respective department heads.

Control-M has positively impacted my organization so far, as I have noticed several benefits.

The benefits I can identify include a combination of time saving and automated tasks.

The best features Control-M offers, based on my experience so far, are the simplification and how it makes performing day-to-day tasks easier in a quicker manner.

The simplification helps me in my day-to-day tasks because it combines automation and a generic UI, which is very easy and understandable for end users.

The recent AI addition surprised me as it provides more prompts for simplification.

Regarding Control-M's AI capabilities, I think its governance and security are top-notch.

The accuracy and reliability of Control-M's AI output are quite good so far.

Control-M has facilitated the integration of Kubernetes and cloud-native workflows with traditional batch processing by simplifying day-to-day tasks through its automated features.

I believe Control-M can be improved by including more AI features.

Improvements should focus on addressing more repetitive tasks.

I have been using Control-M for more than six or seven months.

Control-M is quite stable as it is in the exploration phase.

From what I have seen so far, Control-M's scalability is quite good.

I have seen a return on investment, specifically in terms of time saved.

My experience with pricing, setup cost, and licensing is moderate.

I have experience with AI and ML features in multi-cloud environments where I have been using AI and ML.

I chose a rating of eight because there is room for improvement in daily tasks, and based on current usage and the exploration phase, I would keep it at eight.

My advice to others looking into using Control-M is that the decision should be based on project requirements, as it simplifies tasks and saves time.

On-premises