Control-M Reviews

Control-M is our primary workload scheduling software, and we aim to utilize it for linking applications, hardware, and data transfers. Ideally, it should be the core component of our architecture. While we have started to expand its use over the past couple of years, we are not fully there yet.

A significant portion of our incoming files is handled through Managed File Transfer (MFT) Enterprise, which is an extension of Control-M. We have been utilizing this solution increasingly for both external and internal file transfers. Additionally, we rely on Control-M to schedule SAP jobs, which is one of its key functionalities. We also schedule a substantial portion of our data warehouse production using Control-M. 

As a health insurance company, we have a main internal application that heavily depends on Control-M. For instance, many incoming files need to be decoded and processed for our internal application, followed by a job that transfers the data into our databases. We have recently begun to use Control-M for the maintenance of various databases, such as SAP HANA, Oracle, and Postgres. This aspect of our usage is constantly growing.

Another significant aspect of our workflow involves creating customized job types for our clients. For example, some files we receive are encoded and zipped, for which there is no default Control-M job. To address this, we frequently use the Application Integrator to provide additional functionalities not currently available in Control-M. This allows us to automate tasks that would otherwise require manual intervention, such as extracting contents from ZIP files.

We are also standardizing processes with Control-M, providing standard job types for all our customers. This includes file renaming, combining files, or separating them based on specific application types. For instance, we receive files that contain multiple text files, which we may need to split or combine into a certain format when sending them out.

In summary, we are dedicated to making Control-M our primary workload automation and orchestration software. Recently, we've begun integrating Control-M with Ansible to manage patch routines for our Linux and Windows servers. The challenge has been linking these patch routines with our application jobs. We've started this integration so that whenever there is a patch, our applications automatically halt until we receive confirmation from Ansible. We frequently use the Control-M API for this purpose, enabling seamless coordination between Ansible and Control-M. Overall, these are the main use cases we are currently implementing with Control-M, and we continually seek to expand its applications across our operations.

The application integrator provides a toolbox that makes integration easy. We use it to mask Python scripts that handle various routines, such as ZIP solutions, while the application integrator manages drop-down menus. Sometimes the integrations that Control-M provides are not suitable for our specific use cases. For instance, with S3 storage from Amazon, Control-M's integration only uses a small percentage of available S3 functionalities, so we had to create our own solution. When using BMC tools such as the application integrator, Control-M provides an easy path to integrate many features.

One thing we have criticized is the MFTE capabilities, particularly regarding high availability. BMC hasn't provided a setup with multiple fallbacks for error situations. We've experienced main problems with MFTE where having one setup means when an error occurs, the entire service goes down. We have requested BMC to provide a high availability solution for MFTE. While there are other minor issues, this remains our main concern.

I started using the z/OS parts in 2007-2008 for one to two years. After switching companies, I have been using it in its current form since 2013, totaling twelve years of experience.

It is usually very stable, except for the MFT solution where an error leads to an outage. This has occurred several times in the last two to three years. We are in direct contact with their lab development regarding these issues. While the system is stable 99% of the time, when issues occur, they are significant.

The scalability is very good with numerous configuration options available. Though we sometimes use load balancers in front of Control-M, the available functions offer good options for configuration. Our license doesn't limit our ability to configure Control-M as needed, allowing us to easily create new agents or environments.

I don’t contact the BMC support every day, but we are in constant communication with them. They are currently migrating many features to their web interface, which means some functionalities that were available on their desktop solution are now missing. As a result, we are creating a lot of tickets regarding this issue. At least once a week, one of my colleagues reaches out to BMC support.

Regarding the quality and speed of their responses, it really depends on the representative and the topic at hand. There are certain areas where they have more experienced experts, while in other cases, the support staff tends to rely on standard procedures. Generally, their answers are acceptable; I would rate them around a seven out of ten. Many times, their assistance is more like a nine or ten, but there are occasions when a representative provides standard responses without understanding the specific issue. In these situations, I often have to clarify the problem multiple times because they start with standard solutions that don’t apply to my case. Overall, I would rate their support around a solid seven out of ten.

I previously used OPC (now TWS) from IBM for z/OS software. We regularly evaluate the market, comparing different vendors with BMC, including Redwood, Stone Branch, and HLC. While we conduct proof of concepts and demonstrations from various vendors, we maintain only one product installation.

Control-M was previously used for both z/OS (mainframe) and distributed environments. However, we have since migrated from the mainframe to a distributed system two years ago, and now Control-M is only implemented on the distributed side. I was responsible for this migration due to my knowledge of mainframe systems. While Control-M was already in place, certain functionalities, like data transfer, were not integrated into Control-M. This integration has been accomplished in the last five years.

The learning curve for me was easy due to my background. It took about a year maximum to transition into an administration role. My experience with both mainframe and distributed systems was beneficial. For new colleagues without z/OS background, the learning curve was steeper. While becoming a scheduler is relatively easy in Control-M, mastering specific features requires more time and learning.

In terms of maintenance, we usually have one or two major releases each year, which is quite a significant process. If there’s one area where BMC could improve, it would be optimizing the maintenance process. The type of maintenance required depends on the range of components included. If we talk specifically about the core Control-M product or its basic components, then currently, there isn’t much maintenance involved. However, when we include the agents, the maintenance requirements increase. Overall, I would classify the maintenance needs as moderate. It could be less demanding, particularly during situations like a major version change, where we would prefer to minimize outages. We are in discussions with BMC about how to enhance this process. To summarize, I would say that the maintenance requirements are not low, but they don’t necessitate weekly attention either. So, I would characterize it as being in the middle range.

Control-M is among the highest-priced solutions in the market. When comparing with other vendors, BMC consistently maintains premium pricing. We hope for more creative pricing solutions from BMC in the future, as currently, the price often remains constant regardless of package customization or feature selection. We would appreciate more pricing flexibility from BMC.

Our relationship with BMC is more in the direction of transformative. We’ve built a solid relationship with BMC over the years. It was a process, but I would classify it as transformative. They support us well. While there are certain functions we wish were available, overall, I think our relationship is transformative.

We have a good relationship with BMC, and that required effort on both sides. Sometimes, there’s a gap between what we need as customers and the direction BMC is heading. However, overall, we’re satisfied with how BMC provides us with solutions. Of course, there are things we feel are missing at times, but I also must say that the experience can vary depending on who you’re speaking with at BMC. The experience with sales representatives can differ from discussions with technical staff. In general, though, I would say the relationship is positive.

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

Control-M is an automation tool I use for monitoring, scheduling, job batches, and job flows. It helps me with troubleshooting failures, checking job failures, identifying errors, investigating dependencies, and providing a runbook to recover failed logs, which allows me to practice with realistic IT support.

It reduces time and human errors.

The best features Control-M offers include job monitoring, job scheduling, job dependencies, failure recovery, alerts and notifications, file traversals and integrations, SLA management, automation, and APIs, which are especially valuable for L1 and L2 support.

Control-M has positively impacted my organization by saving time and operational effort, reducing human error, improving product monitoring, connecting business processes, and enhancing business reporting. I can share specific outcomes that include reduced manual effort, such that if a person takes two days to complete a task, Control-M might finish it in half an hour.

I have noticed some improvements needed for Control-M, including proactive failure monitoring, automating repeated recovery, automated recovery, analyzing recurring failures, SLA management, integrating automation, optimizing job performance, and standard runbooks.

I have been using Control-M for one and a half years.

Yes, Control-M is generally a stable and reliable workload automation solution. It provides centralised job monitoring, dependency management, automated scheduling, and failure notifications. With proper configuration, regular maintenance, and monitoring, it helps minimise operational disruptions and maintain business continuity.

I rated Control-M highly for scalability because it can manage growing workloads, increasing job volumes, and complex workflows across multiple applications and cloud environments. It supports business growth through centralised scheduling and automation, with capacity depending on infrastructure, configuration, and licensing.

Control-M provides reliable technical support, comprehensive documentation, and effective troubleshooting resources. Customer support and product guidance help resolve issues and maintain smooth operations.

Yes, I reviewed the previous solution and made changes to improve automation, monitoring, and operational efficiency. The changes were intended to address recurring issues, reduce manual effort, strengthen governance and security, and improve overall performance and reliability.

The initial setup is moderately complex, requiring technical configuration and integration. However, once configured, daily job scheduling and monitoring are relatively straightforward.

The deployment was primarily handled by our internal IT team, with vendor support where required.

Control-M delivers ROI by reducing manual effort, minimising job failures, saving operational costs, and improving team productivity through automation.

I have limited visibility into the exact pricing and licensing costs, as these are typically managed through organisational agreements with the vendor. The value of Control-M comes from its automation capabilities, reduced manual effort, and improved operational efficiency. Transparent pricing, flexible licensing, and clear ROI metrics would further improve the experience.

We previously used manual job monitoring and scheduling processes to track job execution, identify failures, and resolve incidents. We are now leveraging Control-M automation to improve workflow management, reduce manual intervention, and enhance operational efficiency and reliability.

On a scale of 1 to 10, I would rate Control-M around nine.

I chose nine as my rating for Control-M because it improves operational efficiency through monitoring, simplifies job monitoring and scheduling, reduces manual efforts, and helps identify failures quickly.

Regarding Control-M's governance and security, I find them to be robust as it supports controlled access, role-based permissions, and auditable and standardized workflows, helping organizations without any concerns noticed as of now.

Regarding Control-M's accuracy and reliability of output, I find it to be high, with automated workflows reducing human errors, which I have personally experienced.

My advice for others looking into using Control-M is to explore its automation capabilities and to follow security concerns.

Our main use case for Control-M at our organization is that we are a mid to large-scale IT consumer and we use Control-M to schedule all batch jobs. We are heavily reliant on our batch operations, whether in mainframe or distributed environments. We run nearly hundreds of thousands of jobs every single day. Control-M has been very helpful and insightful, and controlling batch operations across various environments is straightforward. We are satisfied with Control-M's usage since we migrated from Broadcom ESP over three years ago. Comparatively, Control-M is performing significantly better for us and the outcomes are excellent. We look forward to seeing how BMC is integrating AI into Control-M and how we can see improvements in overall performance, throughput, and efficiency with the new AI capabilities.

Since we run hundreds of thousands of jobs every single day, we are heavily reliant on our batch operations for generating various reports on a day-to-day basis for our business. It is very important that our batch cycle is managed efficiently and the schedule is managed well so we receive our reports on time, which go up to the executive level. Many business decisions are based on these reports. Control-M plays a significant and vital role in managing our batch operations. We look forward to seeing what additional innovations BMC is engaged with regarding Control-M and what we can expect with the new AI capabilities.

Control-M has positively impacted my organization by improving the overall batch performance and scheduling. I can share that specific outcomes show improvement in the efficiency of our batch operations as a whole.

I am fairly new to Control-M, but I know at a high level how Control-M is used in our organization.

At a high level, I have thoughts on features that stand out in Control-M, such as scheduling flexibility, cross-platform support, centralized monitoring, and automation capabilities. The flexibility in scheduling is notable, and since we use it on mainframe and distributed environments, cross-platform support is essential. Obviously, monitoring centrally and the automation capabilities are also important. All four of these are what I hear on a day-to-day basis, and Control-M is doing a great job for us. These at least four areas are what stand out.

I am not a direct user of Control-M, but I manage the Control-M team and I know a little about Control-M, how it works, the purpose, and the outcomes we are getting in our company. I can speak to that.

On a scale of one to ten, I would rate Control-M overall as nine. I choose nine for my rating because of the overall usage, flexibility, and efficiency in Control-M.

Regarding Control-M's AI capabilities, I think as an organization, we are trying to implement a lot of governance around AI capabilities and the tools around AI. I still need to better understand how BMC is integrating the AI capabilities into Control-M and at what capacity. Based on that, I think our organization will see how we can leverage the AI and the governance.

Regarding Control-M's AI capabilities, the accuracy and reliability of output are fairly good.

Our main use case for Control-M is that we are using Remedy from BMC for incident, problem, and change management. Control-M is helpful for those processes as it is fully automated and we are using it for end-to-end problem and change management, being integrated with our other applications. We are satisfied with what we are currently using Control-M for in these processes.

We have several integrations in place with Control-M. We have integration with PagerDuty so that if a critical incident is happening, the team can receive an alert or page, and also with Prometheus as part of monitoring. We have not seen any challenges with these integrations; they were smooth.

We are still exploring Control-M before integrating AI or ML workloads. Control-M does not have a specific feature to adhere to compliance, so for whatever incidents and change records that are part of SOX, we ensure to follow the proper ITIL process and close incidents on time with proper resolution.

Control-M has impacted our organization positively, as we are satisfied and are getting all the use cases we were looking for. Efficiency is one of the key points that stands out since we started using Control-M. Control-M improved our efficiency, helping the team work faster as it is very fast, with no bottlenecks and no slowness.

The best features Control-M offers include the tool's flexibility, as there are many features that fulfill our requirements. Control-M showcases its flexibility for our use cases, including ITSM, incident, problem, and change management, as we have never seen any slowness or bottlenecks with the application. We are aware of all these features in Control-M, which is why we are using it. I do not see much of a bottleneck with the application, as Control-M is very flexible without any slowdown.

I cannot find anything that needs improvement in Control-M, as for whatever use cases we have, we are getting all of those things from it. I do not see anything that needs improvement for Control-M at this time.

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

Control-M is stable.

Control-M's scalability is very good, as I do not see any issues with it; it is very scalable.

The customer support for Control-M is excellent, and we do not see any issues.

We have been using Control-M for a long time, so I did not use a different solution before it.

As of now, we have not seen anything during deployment or setup that needs improvement.

We have seen a return on investment with Control-M, as time is saved for the employees.

My experience with pricing, setup cost, and licensing for Control-M is satisfactory, as it was decided by the procurement team and I have not seen any challenges.

It is client-driven, and we did not formally evaluate other options before choosing Control-M.

On a scale of 1 to 10, I would rate Control-M a 9 to 10. I would rate it a 10 because for all the use cases and purposes we had, we are getting everything without any concerns or issues.

Regarding Control-M's AI capabilities, I am exploring its governance and security. We are still exploring Control-M's AI capabilities, but the expectation is that it should be secure and comply with governance.

My advice to others looking into using Control-M is that it is a centralized tool to manage all IT service management, providing a great experience and complying with audits.

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 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.